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0000000000000000000000000000000000000000..25000cd87fa6af2f2a10742de52a271c955ada92 --- /dev/null +++ b/coreai/assets/g2p/export_config.json @@ -0,0 +1,33 @@ +{ + "batch_dynamic": true, + "format": "scyllasband_g2p", + "input_granularity": "phrase", + "languages": [ + "de", + "en_gb", + "en_us", + "es", + "fr", + "it", + "vi" + ], + "model_type": "transformer", + "onnx_export_batch_size": 4, + "onnx_export_layout": "batch_first", + "onnx_export_max_batch_size": 256, + "onnx_exporter": "dynamo", + "onnx_fp32_model": "model.onnx", + "onnx_model": "model.onnx", + "onnx_padded_output": "ctc_blank", + "onnx_quantization": "none", + "onnx_quantized_parity_tolerance": 3.0, + "opset": 18, + "phoneme_token_mode": "char", + "phrase_rows_per_language": 200000, + "preserve_commas": true, + "punctuation_silence_target": "explicit_silence", + "release_role": "fp32_bundle_frontend", + "source_checkpoint": "output/deepphonemizer_multilingual/model_v10_7lang/checkpoints/model_step_250k.pt", + "training_mix": "phrase_word_chunked_clean", + "word_rows_per_language": 200000 +} diff --git a/coreai/assets/g2p/language_map.json b/coreai/assets/g2p/language_map.json new file mode 100644 index 0000000000000000000000000000000000000000..e70f9bff55415d972fbc5de94b878686d47a88c3 --- /dev/null +++ b/coreai/assets/g2p/language_map.json @@ -0,0 +1,47 @@ +{ + "american": "en_us", + "american_english": "en_us", + "british": "en_gb", + "british_english": "en_gb", + "de": "de", + "de_de": "de", + "deu": "de", + "en": "en_us", + "en-gb": "en_gb", + "en-uk": "en_gb", + "en-us": "en_us", + "en_gb": "en_gb", + "en_uk": "en_gb", + "en_us": "en_us", + "eng": "en_us", + "eng_gb": "en_gb", + "eng_us": "en_us", + "english": "en_us", + "english_gb": "en_gb", + "english_uk": "en_gb", + "english_us": "en_us", + "es": "es", + "es_es": "es", + "es_mx": "es", + "fr": "fr", + "fr_fr": "fr", + "fra": "fr", + "fre": "fr", + "french": "fr", + "gb": "en_gb", + "ger": "de", + "german": "de", + "it": "it", + 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0000000000000000000000000000000000000000..25000cd87fa6af2f2a10742de52a271c955ada92 --- /dev/null +++ b/coreml/assets/g2p/export_config.json @@ -0,0 +1,33 @@ +{ + "batch_dynamic": true, + "format": "scyllasband_g2p", + "input_granularity": "phrase", + "languages": [ + "de", + "en_gb", + "en_us", + "es", + "fr", + "it", + "vi" + ], + "model_type": "transformer", + "onnx_export_batch_size": 4, + "onnx_export_layout": "batch_first", + "onnx_export_max_batch_size": 256, + "onnx_exporter": "dynamo", + "onnx_fp32_model": "model.onnx", + "onnx_model": "model.onnx", + "onnx_padded_output": "ctc_blank", + "onnx_quantization": "none", + "onnx_quantized_parity_tolerance": 3.0, + "opset": 18, + "phoneme_token_mode": "char", + "phrase_rows_per_language": 200000, + "preserve_commas": true, + "punctuation_silence_target": "explicit_silence", + "release_role": "fp32_bundle_frontend", + "source_checkpoint": "output/deepphonemizer_multilingual/model_v10_7lang/checkpoints/model_step_250k.pt", + "training_mix": "phrase_word_chunked_clean", + "word_rows_per_language": 200000 +} diff --git a/coreml/assets/g2p/language_map.json b/coreml/assets/g2p/language_map.json new file mode 100644 index 0000000000000000000000000000000000000000..e70f9bff55415d972fbc5de94b878686d47a88c3 --- /dev/null +++ b/coreml/assets/g2p/language_map.json @@ -0,0 +1,47 @@ +{ + "american": "en_us", + "american_english": "en_us", + "british": "en_gb", + "british_english": "en_gb", + "de": "de", + "de_de": "de", + "deu": "de", + "en": "en_us", + "en-gb": "en_gb", + "en-uk": "en_gb", + "en-us": "en_us", + "en_gb": "en_gb", + "en_uk": "en_gb", + "en_us": "en_us", + "eng": "en_us", + "eng_gb": "en_gb", + "eng_us": "en_us", + "english": "en_us", + "english_gb": "en_gb", + "english_uk": "en_gb", + "english_us": "en_us", + "es": "es", + "es_es": "es", + "es_mx": "es", + "fr": "fr", + "fr_fr": "fr", + "fra": "fr", + "fre": "fr", + "french": "fr", + "gb": "en_gb", + "ger": "de", + "german": "de", + "it": "it", + 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b/coreml/assets/g2p/pronunciation_overrides.json new file mode 100644 index 0000000000000000000000000000000000000000..46ee8e9d368a4a75545f4d8a76213ce4209dddf3 --- /dev/null +++ b/coreml/assets/g2p/pronunciation_overrides.json @@ -0,0 +1,4 @@ +{ + "format": "scyllasband_g2p_pronunciation_overrides_v1", + "languages": {} +} diff --git a/coreml/assets/g2p/tokenizer.json b/coreml/assets/g2p/tokenizer.json new file mode 100644 index 0000000000000000000000000000000000000000..cc6a708c009b634d46c8cec0aa9286bfb38e7fa4 --- /dev/null +++ b/coreml/assets/g2p/tokenizer.json @@ -0,0 +1,517 @@ +{ + "format": "deepphonemizer_forward_ctc", + "languages": [ + "de", + "en_gb", + "en_us", + "es", + "fr", + "it", + "vi" + ], + "char_repeats": 4, + "lowercase": true, + "phoneme_token_mode": "char", + "text_symbols": { + "_": 0, + "": 1, + "": 2, + "": 3, + "": 4, + "": 5, + "": 6, + "": 7, + "": 8, + "\b": 9, + " ": 10, + "!": 11, + "\"": 12, + "#": 13, + "$": 14, + "%": 15, + "&": 16, + "'": 17, + "(": 18, 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b/coreml/assets/languages.json @@ -0,0 +1,30 @@ +[ + { + "id": "en_us", + "index": 0 + }, + { + "id": "en_gb", + "index": 1 + }, + { + "id": "es", + "index": 2 + }, + { + "id": "it", + "index": 3 + }, + { + "id": "fr", + "index": 4 + }, + { + "id": "de", + "index": 5 + }, + { + "id": "vi", + "index": 6 + } +] diff --git a/coreml/assets/phone_vocab.json b/coreml/assets/phone_vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..4e3fc8dfaa63902e040737865f3d5a4edd5320d8 --- /dev/null +++ b/coreml/assets/phone_vocab.json @@ -0,0 +1,111 @@ +{ + "schema_version": 1, + "mode": "scyllasband_ipa_phone_symbols", + "special_tokens": [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "", + "" + ], + "vocab_size": 87, + "token_to_id": { + "": 0, + "": 1, + "": 2, + "": 3, + "": 4, + "": 5, + "": 6, + "": 7, + "": 8, + "": 9, + "": 10, + "": 11, + "": 12, + "": 13, + "": 14, + "1": 15, + "2": 16, + "4": 17, + "5": 18, + "6": 19, + "7": 20, + 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"index": 0 + }, + { + "id": "felix", + "index": 1 + }, + { + "id": "gwen", + "index": 2 + }, + { + "id": "ink", + "index": 3 + }, + { + "id": "max", + "index": 4 + }, + { + "id": "orpheus", + "index": 5 + }, + { + "id": "rex", + "index": 6 + }, + { + "id": "scylla", + "index": 7 + }, + { + "id": "stone", + "index": 8 + }, + { + "id": "tuesday", + "index": 9 + }, + { + "id": "thistle", + "index": 10 + }, + { + "id": "kincade", + "index": 11 + }, + { + "id": "poppy", + "index": 12 + }, + { + "id": "priya", + "index": 13 + } +] diff --git a/coreml/coreml/duration_predictor.mlmodelc/analytics/coremldata.bin b/coreml/coreml/duration_predictor.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a2fc51e0f1ce09194bce59e4c8118ebf64600346 --- /dev/null +++ b/coreml/coreml/duration_predictor.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:585f8dd86087c76e342efacc9831498940d74a8aec9f9babee5f31f3a927bb40 +size 243 diff --git a/coreml/coreml/duration_predictor.mlmodelc/coremldata.bin b/coreml/coreml/duration_predictor.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b2a9776b22f2305906040c8e214448bfd9ed6fd2 --- /dev/null +++ b/coreml/coreml/duration_predictor.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:76bf36062d9bfdc6d13bc8cdc35e0d8bb7c01a7e6080996d09061605c4aeb50d +size 554 diff --git a/coreml/coreml/duration_predictor.mlmodelc/model.mil b/coreml/coreml/duration_predictor.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..b0cb4856856184689eb441f96ed3eecdd0adab65 --- /dev/null +++ b/coreml/coreml/duration_predictor.mlmodelc/model.mil @@ -0,0 +1,776 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor delivery_present, tensor delivery_values, tensor language_id, tensor span_mask, tensor span_phone_ids, tensor span_segment_ids, tensor voice_id) { + int32 var_19 = const()[name = string("op_19"), val = int32(0)]; + tensor span_mask_1 = not_equal(x = span_mask, y = var_19)[name = string("span_mask_1")]; + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_52 = const()[name = string("op_52"), val = int32(-1)]; + int32 var_94 = const()[name = string("op_94"), val = int32(7)]; + tensor var_95 = mul(x = voice_id, y = var_94)[name = string("op_95")]; + tensor index = add(x = var_95, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_121_dtype_0 = const()[name = string("cast_121_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_11")]; + tensor cast_121 = cast(dtype = cast_121_dtype_0, x = index_to_int16)[name = string("cast_10")]; + tensor greater_equal_0 = greater_equal(x = cast_121, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_16 = const()[name = string("slice_by_index_16"), val = int32(98)]; + tensor add_0 = add(x = cast_121, y = slice_by_index_16)[name = string("add_0")]; + tensor select_0 = select(a = cast_121, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_9")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_8")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_7")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + int32 var_121 = const()[name = string("op_121"), val = int32(87)]; + tensor var_122 = mul(x = span_segment_ids, y = var_121)[name = string("op_122")]; + tensor input_1 = add(x = var_122, y = span_phone_ids)[name = string("input_1")]; + int32 var_129_axis_0 = const()[name = string("op_129_axis_0"), val = int32(0)]; + int32 var_129_batch_dims_0 = const()[name = string("op_129_batch_dims_0"), val = int32(0)]; + bool var_129_validate_indices_0 = const()[name = string("op_129_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(153536))))[name = string("module_model_phone_embed_table_weight_to_fp16_quantized")]; + string input_1_to_uint16_dtype_0 = const()[name = string("input_1_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_1_to_uint16 = cast(dtype = input_1_to_uint16_dtype_0, x = input_1)[name = string("cast_6")]; + tensor var_129_cast_fp16_cast_uint16 = gather(axis = var_129_axis_0, batch_dims = var_129_batch_dims_0, indices = input_1_to_uint16, validate_indices = var_129_validate_indices_0, x = module_model_phone_embed_table_weight_to_fp16_quantized)[name = string("op_129_cast_fp16_cast_uint16")]; + tensor op_131_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(154176))), scale = tensor([[[0x1.4d4p-5]]]))[name = string("op_131_to_fp16_quantized")]; + tensor hidden_1_cast_fp16 = add(x = var_129_cast_fp16_cast_uint16, y = op_131_to_fp16_quantized)[name = string("hidden_1_cast_fp16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_136_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_136_cast_fp16")]; + bool input_5_interleave_0 = const()[name = string("input_5_interleave_0"), val = bool(false)]; + tensor input_5_cast_fp16 = concat(axis = var_52, interleave = input_5_interleave_0, values = (var_136_cast_fp16, present_to_fp16))[name = string("input_5_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318080)))]; + tensor linear_0_bias_0_to_fp16 = const()[name = string("linear_0_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324544)))]; + tensor linear_0_cast_fp16 = linear(bias = linear_0_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_5_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_141_axes_0 = const()[name = string("op_141_axes_0"), val = tensor([1])]; + tensor var_141_cast_fp16 = expand_dims(axes = var_141_axes_0, x = linear_0_cast_fp16)[name = string("op_141_cast_fp16")]; + tensor hidden_3_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_141_cast_fp16)[name = string("hidden_3_cast_fp16")]; + int32 var_145_axis_0 = const()[name = string("op_145_axis_0"), val = int32(0)]; + int32 var_145_batch_dims_0 = const()[name = string("op_145_batch_dims_0"), val = int32(0)]; + bool var_145_validate_indices_0 = const()[name = string("op_145_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329792))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_3")]; + tensor var_145_cast_fp16_cast_uint16 = gather(axis = var_145_axis_0, batch_dims = var_145_batch_dims_0, indices = voice_id_to_uint16, validate_indices = var_145_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("op_145_cast_fp16_cast_uint16")]; + int32 var_147_axis_0 = const()[name = string("op_147_axis_0"), val = int32(0)]; + int32 var_147_batch_dims_0 = const()[name = string("op_147_batch_dims_0"), val = int32(0)]; + bool var_147_validate_indices_0 = const()[name = string("op_147_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(329920)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_2")]; + tensor var_147_cast_fp16_cast_uint16 = gather(axis = var_147_axis_0, batch_dims = var_147_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_147_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("op_147_cast_fp16_cast_uint16")]; + tensor var_148_cast_fp16 = add(x = var_145_cast_fp16_cast_uint16, y = var_147_cast_fp16_cast_uint16)[name = string("op_148_cast_fp16")]; + tensor var_150_to_fp16 = const()[name = string("op_150_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334464)))]; + tensor var_151_cast_fp16 = add(x = var_148_cast_fp16, y = var_150_to_fp16)[name = string("op_151_cast_fp16")]; + tensor var_154_cast_fp16 = add(x = var_151_cast_fp16, y = var_150_to_fp16)[name = string("op_154_cast_fp16")]; + tensor var_155_axes_0 = const()[name = string("op_155_axes_0"), val = tensor([1])]; + tensor var_155_cast_fp16 = expand_dims(axes = var_155_axes_0, x = var_154_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_5_cast_fp16 = add(x = hidden_3_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_5_cast_fp16")]; + fp16 var_51_to_fp16 = const()[name = string("op_51_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_50_to_fp16 = const()[name = string("op_50_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_51_to_fp16, beta = var_50_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor baseline_duration_mask_internal_tensor_assign_1_to_fp16 = const()[name = string("baseline_duration_mask_internal_tensor_assign_1_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335168)))]; + tensor baseline_mask_cast_fp16 = mul(x = clip_0_cast_fp16, y = baseline_duration_mask_internal_tensor_assign_1_to_fp16)[name = string("baseline_mask_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_51_to_fp16, beta = var_50_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_179_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = baseline_mask_cast_fp16)[name = string("op_179_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(335296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(345600))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(346304)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_179_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_185_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = baseline_mask_cast_fp16)[name = string("op_185_cast_fp16")]; + tensor var_186_axes_0 = const()[name = string("op_186_axes_0"), val = tensor([-1])]; + tensor var_186_cast_fp16 = expand_dims(axes = var_186_axes_0, x = clip_1_cast_fp16)[name = string("op_186_cast_fp16")]; + tensor var_187_cast_fp16 = mul(x = var_185_cast_fp16, y = var_186_cast_fp16)[name = string("op_187_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(347008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(357312))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_187_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor conditioned_cast_fp16 = add(x = linear_1_cast_fp16, y = linear_2_cast_fp16)[name = string("conditioned_cast_fp16")]; + tensor var_194_axes_0 = const()[name = string("op_194_axes_0"), val = tensor([1])]; + tensor var_194_cast_fp16 = expand_dims(axes = var_194_axes_0, x = conditioned_cast_fp16)[name = string("op_194_cast_fp16")]; + tensor hidden_cast_fp16 = add(x = hidden_5_cast_fp16, y = var_194_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_204_axes_0 = const()[name = string("op_204_axes_0"), val = tensor([-1])]; + tensor var_204_cast_fp16 = expand_dims(axes = var_204_axes_0, x = mask_7_cast_fp16)[name = string("op_204_cast_fp16")]; + tensor input_13_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_204_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(358016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(521920))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_3_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_13_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_209_axes_0 = const()[name = string("op_209_axes_0"), val = tensor([1])]; + tensor var_209_cast_fp16 = expand_dims(axes = var_209_axes_0, x = linear_3_cast_fp16)[name = string("op_209_cast_fp16")]; + tensor src_cast_fp16 = add(x = hidden_cast_fp16, y = var_209_cast_fp16)[name = string("src_cast_fp16")]; + tensor mask = logical_not(x = span_mask_1)[name = string("mask")]; + fp16 var_35_to_fp16 = const()[name = string("op_35_to_fp16"), val = fp16(-inf)]; + tensor var_229_to_fp16 = const()[name = string("op_229_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(522624)))]; + tensor key_padding_mask_1_cast_fp16 = select(a = var_35_to_fp16, b = var_229_to_fp16, cond = mask)[name = string("key_padding_mask_1_cast_fp16")]; + tensor query_1_axes_0 = const()[name = string("query_1_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_0_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_0_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(523712)))]; + tensor module_model_encoder_layers_0_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416)))]; + fp16 var_45_to_fp16 = const()[name = string("op_45_to_fp16"), val = fp16(0x1.5p-17)]; + tensor query_1_cast_fp16 = layer_norm(axes = query_1_axes_0, beta = module_model_encoder_layers_0_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_0_norm1_weight_to_fp16, x = src_cast_fp16)[name = string("query_1_cast_fp16")]; + tensor query_3_perm_0 = const()[name = string("query_3_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_0_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(832384))))[name = string("module_model_encoder_layers_0_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_0_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(834368)))]; + tensor query_3_cast_fp16 = transpose(perm = query_3_perm_0, x = query_1_cast_fp16)[name = string("transpose_55")]; + tensor linear_4_cast_fp16 = linear(bias = module_model_encoder_layers_0_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_0_self_attn_in_proj_weight_to_fp16_quantized, x = query_3_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor concat_4 = const()[name = string("concat_4"), val = tensor([512, 1, 3, 320])]; + tensor var_263_cast_fp16 = reshape(shape = concat_4, x = linear_4_cast_fp16)[name = string("op_263_cast_fp16")]; + tensor var_264_axes_0 = const()[name = string("op_264_axes_0"), val = tensor([0])]; + tensor var_264_cast_fp16 = expand_dims(axes = var_264_axes_0, x = var_263_cast_fp16)[name = string("op_264_cast_fp16")]; + tensor var_265_perm_0 = const()[name = string("op_265_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_266_axes_0 = const()[name = string("op_266_axes_0"), val = tensor([-2])]; + tensor var_265_cast_fp16 = transpose(perm = var_265_perm_0, x = var_264_cast_fp16)[name = string("transpose_54")]; + tensor var_266_cast_fp16 = squeeze(axes = var_266_axes_0, x = var_265_cast_fp16)[name = string("op_266_cast_fp16")]; + tensor q_1_begin_0 = const()[name = string("q_1_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_1_end_0 = const()[name = string("q_1_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_1_end_mask_0 = const()[name = string("q_1_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_1_squeeze_mask_0 = const()[name = string("q_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_1_cast_fp16 = slice_by_index(begin = q_1_begin_0, end = q_1_end_0, end_mask = q_1_end_mask_0, squeeze_mask = q_1_squeeze_mask_0, x = var_266_cast_fp16)[name = string("q_1_cast_fp16")]; + tensor k_1_begin_0 = const()[name = string("k_1_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_1_end_0 = const()[name = string("k_1_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_1_end_mask_0 = const()[name = string("k_1_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_1_squeeze_mask_0 = const()[name = string("k_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_1_cast_fp16 = slice_by_index(begin = k_1_begin_0, end = k_1_end_0, end_mask = k_1_end_mask_0, squeeze_mask = k_1_squeeze_mask_0, x = var_266_cast_fp16)[name = string("k_1_cast_fp16")]; + tensor v_1_begin_0 = const()[name = string("v_1_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_1_end_0 = const()[name = string("v_1_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_1_end_mask_0 = const()[name = string("v_1_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_1_squeeze_mask_0 = const()[name = string("v_1_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_1_cast_fp16 = slice_by_index(begin = v_1_begin_0, end = v_1_end_0, end_mask = v_1_end_mask_0, squeeze_mask = v_1_squeeze_mask_0, x = var_266_cast_fp16)[name = string("v_1_cast_fp16")]; + tensor var_274 = const()[name = string("op_274"), val = tensor([512, 8, 40])]; + tensor var_275_cast_fp16 = reshape(shape = var_274, x = q_1_cast_fp16)[name = string("op_275_cast_fp16")]; + tensor q_3_perm_0 = const()[name = string("q_3_perm_0"), val = tensor([1, 0, 2])]; + tensor var_281 = const()[name = string("op_281"), val = tensor([512, 8, 40])]; + tensor var_282_cast_fp16 = reshape(shape = var_281, x = k_1_cast_fp16)[name = string("op_282_cast_fp16")]; + tensor k_3_perm_0 = const()[name = string("k_3_perm_0"), val = tensor([1, 0, 2])]; + tensor var_288 = const()[name = string("op_288"), val = tensor([512, 8, 40])]; + tensor var_289_cast_fp16 = reshape(shape = var_288, x = v_1_cast_fp16)[name = string("op_289_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([1, 0, 2])]; + tensor var_292 = const()[name = string("op_292"), val = tensor([1, 1, 1, 512])]; + tensor var_293_cast_fp16 = reshape(shape = var_292, x = key_padding_mask_1_cast_fp16)[name = string("op_293_cast_fp16")]; + tensor var_295_reps_0 = const()[name = string("op_295_reps_0"), val = tensor([1, 8, 1, 1])]; + tensor var_295_cast_fp16 = tile(reps = var_295_reps_0, x = var_293_cast_fp16)[name = string("op_295_cast_fp16")]; + tensor var_303 = const()[name = string("op_303"), val = tensor([1, 8, 512, 40])]; + tensor q_3_cast_fp16 = transpose(perm = q_3_perm_0, x = var_275_cast_fp16)[name = string("transpose_53")]; + tensor q_5_cast_fp16 = reshape(shape = var_303, x = q_3_cast_fp16)[name = string("q_5_cast_fp16")]; + tensor var_305 = const()[name = string("op_305"), val = tensor([1, 8, 512, 40])]; + tensor k_3_cast_fp16 = transpose(perm = k_3_perm_0, x = var_282_cast_fp16)[name = string("transpose_52")]; + tensor k_5_cast_fp16 = reshape(shape = var_305, x = k_3_cast_fp16)[name = string("k_5_cast_fp16")]; + tensor var_307 = const()[name = string("op_307"), val = tensor([1, 8, 512, 40])]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_289_cast_fp16)[name = string("transpose_51")]; + tensor v_5_cast_fp16 = reshape(shape = var_307, x = v_3_cast_fp16)[name = string("v_5_cast_fp16")]; + tensor attn_output_1_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_5_cast_fp16, query = q_5_cast_fp16, value = v_5_cast_fp16)[name = string("attn_output_1_cast_fp16")]; + tensor var_310 = const()[name = string("op_310"), val = tensor([2, 0, 1, 3])]; + tensor var_315 = const()[name = string("op_315"), val = tensor([512, 320])]; + tensor var_311_cast_fp16 = transpose(perm = var_310, x = attn_output_1_cast_fp16)[name = string("transpose_50")]; + tensor attn_output_3_cast_fp16 = reshape(shape = var_315, x = var_311_cast_fp16)[name = string("attn_output_3_cast_fp16")]; + tensor module_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(938816))))[name = string("module_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(939520)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_3_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_319 = const()[name = string("op_319"), val = tensor([512, 1, 320])]; + tensor attn_output_7_cast_fp16 = reshape(shape = var_319, x = linear_5_cast_fp16)[name = string("attn_output_7_cast_fp16")]; + tensor input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor([1, 0, 2])]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = attn_output_7_cast_fp16)[name = string("transpose_49")]; + tensor input_17_cast_fp16 = add(x = src_cast_fp16, y = input_15_cast_fp16)[name = string("input_17_cast_fp16")]; + tensor input_19_axes_0 = const()[name = string("input_19_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_0_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_0_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940224)))]; + tensor module_model_encoder_layers_0_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940928)))]; + tensor input_19_cast_fp16 = layer_norm(axes = input_19_axes_0, beta = module_model_encoder_layers_0_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_0_norm2_weight_to_fp16, x = input_17_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_model_encoder_layers_0_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1351296))))[name = string("module_model_encoder_layers_0_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_0_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353920)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_encoder_layers_0_linear1_bias_to_fp16, weight = module_model_encoder_layers_0_linear1_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_6_cast_fp16")]; + string input_21_mode_0 = const()[name = string("input_21_mode_0"), val = string("EXACT")]; + tensor input_21_cast_fp16 = gelu(mode = input_21_mode_0, x = linear_6_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor module_model_encoder_layers_0_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1356544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1766208))))[name = string("module_model_encoder_layers_0_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_0_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_0_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1766912)))]; + tensor linear_7_cast_fp16 = linear(bias = module_model_encoder_layers_0_linear2_bias_to_fp16, weight = module_model_encoder_layers_0_linear2_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_27_cast_fp16 = add(x = input_17_cast_fp16, y = linear_7_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor query_5_axes_0 = const()[name = string("query_5_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_1_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_1_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1767616)))]; + tensor module_model_encoder_layers_1_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1768320)))]; + tensor query_5_cast_fp16 = layer_norm(axes = query_5_axes_0, beta = module_model_encoder_layers_1_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_1_norm1_weight_to_fp16, x = input_27_cast_fp16)[name = string("query_5_cast_fp16")]; + tensor query_7_perm_0 = const()[name = string("query_7_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_1_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1769024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2076288))))[name = string("module_model_encoder_layers_1_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_1_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2078272)))]; + tensor query_7_cast_fp16 = transpose(perm = query_7_perm_0, x = query_5_cast_fp16)[name = string("transpose_48")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_encoder_layers_1_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_1_self_attn_in_proj_weight_to_fp16_quantized, x = query_7_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor concat_6 = const()[name = string("concat_6"), val = tensor([512, 1, 3, 320])]; + tensor var_370_cast_fp16 = reshape(shape = concat_6, x = linear_8_cast_fp16)[name = string("op_370_cast_fp16")]; + tensor var_371_axes_0 = const()[name = string("op_371_axes_0"), val = tensor([0])]; + tensor var_371_cast_fp16 = expand_dims(axes = var_371_axes_0, x = var_370_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_372_perm_0 = const()[name = string("op_372_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-2])]; + tensor var_372_cast_fp16 = transpose(perm = var_372_perm_0, x = var_371_cast_fp16)[name = string("transpose_47")]; + tensor var_373_cast_fp16 = squeeze(axes = var_373_axes_0, x = var_372_cast_fp16)[name = string("op_373_cast_fp16")]; + tensor q_7_begin_0 = const()[name = string("q_7_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_7_end_0 = const()[name = string("q_7_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_7_end_mask_0 = const()[name = string("q_7_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_7_squeeze_mask_0 = const()[name = string("q_7_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_7_cast_fp16 = slice_by_index(begin = q_7_begin_0, end = q_7_end_0, end_mask = q_7_end_mask_0, squeeze_mask = q_7_squeeze_mask_0, x = var_373_cast_fp16)[name = string("q_7_cast_fp16")]; + tensor k_7_begin_0 = const()[name = string("k_7_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_7_end_0 = const()[name = string("k_7_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_7_end_mask_0 = const()[name = string("k_7_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_7_squeeze_mask_0 = const()[name = string("k_7_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_7_cast_fp16 = slice_by_index(begin = k_7_begin_0, end = k_7_end_0, end_mask = k_7_end_mask_0, squeeze_mask = k_7_squeeze_mask_0, x = var_373_cast_fp16)[name = string("k_7_cast_fp16")]; + tensor v_7_begin_0 = const()[name = string("v_7_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_7_end_0 = const()[name = string("v_7_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_7_end_mask_0 = const()[name = string("v_7_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_7_squeeze_mask_0 = const()[name = string("v_7_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_7_cast_fp16 = slice_by_index(begin = v_7_begin_0, end = v_7_end_0, end_mask = v_7_end_mask_0, squeeze_mask = v_7_squeeze_mask_0, x = var_373_cast_fp16)[name = string("v_7_cast_fp16")]; + tensor var_381 = const()[name = string("op_381"), val = tensor([512, 8, 40])]; + tensor var_382_cast_fp16 = reshape(shape = var_381, x = q_7_cast_fp16)[name = string("op_382_cast_fp16")]; + tensor q_9_perm_0 = const()[name = string("q_9_perm_0"), val = tensor([1, 0, 2])]; + tensor var_388 = const()[name = string("op_388"), val = tensor([512, 8, 40])]; + tensor var_389_cast_fp16 = reshape(shape = var_388, x = k_7_cast_fp16)[name = string("op_389_cast_fp16")]; + tensor k_9_perm_0 = const()[name = string("k_9_perm_0"), val = tensor([1, 0, 2])]; + tensor var_395 = const()[name = string("op_395"), val = tensor([512, 8, 40])]; + tensor var_396_cast_fp16 = reshape(shape = var_395, x = v_7_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor v_9_perm_0 = const()[name = string("v_9_perm_0"), val = tensor([1, 0, 2])]; + tensor var_410 = const()[name = string("op_410"), val = tensor([1, 8, 512, 40])]; + tensor q_9_cast_fp16 = transpose(perm = q_9_perm_0, x = var_382_cast_fp16)[name = string("transpose_46")]; + tensor q_11_cast_fp16 = reshape(shape = var_410, x = q_9_cast_fp16)[name = string("q_11_cast_fp16")]; + tensor var_412 = const()[name = string("op_412"), val = tensor([1, 8, 512, 40])]; + tensor k_9_cast_fp16 = transpose(perm = k_9_perm_0, x = var_389_cast_fp16)[name = string("transpose_45")]; + tensor k_11_cast_fp16 = reshape(shape = var_412, x = k_9_cast_fp16)[name = string("k_11_cast_fp16")]; + tensor var_414 = const()[name = string("op_414"), val = tensor([1, 8, 512, 40])]; + tensor v_9_cast_fp16 = transpose(perm = v_9_perm_0, x = var_396_cast_fp16)[name = string("transpose_44")]; + tensor v_11_cast_fp16 = reshape(shape = var_414, x = v_9_cast_fp16)[name = string("v_11_cast_fp16")]; + tensor attn_output_9_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_11_cast_fp16, query = q_11_cast_fp16, value = v_11_cast_fp16)[name = string("attn_output_9_cast_fp16")]; + tensor var_417 = const()[name = string("op_417"), val = tensor([2, 0, 1, 3])]; + tensor var_422 = const()[name = string("op_422"), val = tensor([512, 320])]; + tensor var_418_cast_fp16 = transpose(perm = var_417, x = attn_output_9_cast_fp16)[name = string("transpose_43")]; + tensor attn_output_11_cast_fp16 = reshape(shape = var_422, x = var_418_cast_fp16)[name = string("attn_output_11_cast_fp16")]; + tensor module_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2080256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2182720))))[name = string("module_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2183424)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_11_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_426 = const()[name = string("op_426"), val = tensor([512, 1, 320])]; + tensor attn_output_15_cast_fp16 = reshape(shape = var_426, x = linear_9_cast_fp16)[name = string("attn_output_15_cast_fp16")]; + tensor input_29_perm_0 = const()[name = string("input_29_perm_0"), val = tensor([1, 0, 2])]; + tensor input_29_cast_fp16 = transpose(perm = input_29_perm_0, x = attn_output_15_cast_fp16)[name = string("transpose_42")]; + tensor input_31_cast_fp16 = add(x = input_27_cast_fp16, y = input_29_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor input_33_axes_0 = const()[name = string("input_33_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_1_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_1_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2184128)))]; + tensor module_model_encoder_layers_1_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2184832)))]; + tensor input_33_cast_fp16 = layer_norm(axes = input_33_axes_0, beta = module_model_encoder_layers_1_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_1_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_encoder_layers_1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2185536))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2595200))))[name = string("module_model_encoder_layers_1_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_1_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2597824)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_layers_1_linear1_bias_to_fp16, weight = module_model_encoder_layers_1_linear1_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_10_cast_fp16")]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("EXACT")]; + tensor input_35_cast_fp16 = gelu(mode = input_35_mode_0, x = linear_10_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_layers_1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2600448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3010112))))[name = string("module_model_encoder_layers_1_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_1_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3010816)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_layers_1_linear2_bias_to_fp16, weight = module_model_encoder_layers_1_linear2_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = input_31_cast_fp16, y = linear_11_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor query_9_axes_0 = const()[name = string("query_9_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_2_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_2_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3011520)))]; + tensor module_model_encoder_layers_2_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3012224)))]; + tensor query_9_cast_fp16 = layer_norm(axes = query_9_axes_0, beta = module_model_encoder_layers_2_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_2_norm1_weight_to_fp16, x = input_41_cast_fp16)[name = string("query_9_cast_fp16")]; + tensor query_11_perm_0 = const()[name = string("query_11_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_2_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3012928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3320192))))[name = string("module_model_encoder_layers_2_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_2_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3322176)))]; + tensor query_11_cast_fp16 = transpose(perm = query_11_perm_0, x = query_9_cast_fp16)[name = string("transpose_41")]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_layers_2_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_2_self_attn_in_proj_weight_to_fp16_quantized, x = query_11_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor concat_8 = const()[name = string("concat_8"), val = tensor([512, 1, 3, 320])]; + tensor var_477_cast_fp16 = reshape(shape = concat_8, x = linear_12_cast_fp16)[name = string("op_477_cast_fp16")]; + tensor var_478_axes_0 = const()[name = string("op_478_axes_0"), val = tensor([0])]; + tensor var_478_cast_fp16 = expand_dims(axes = var_478_axes_0, x = var_477_cast_fp16)[name = string("op_478_cast_fp16")]; + tensor var_479_perm_0 = const()[name = string("op_479_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_480_axes_0 = const()[name = string("op_480_axes_0"), val = tensor([-2])]; + tensor var_479_cast_fp16 = transpose(perm = var_479_perm_0, x = var_478_cast_fp16)[name = string("transpose_40")]; + tensor var_480_cast_fp16 = squeeze(axes = var_480_axes_0, x = var_479_cast_fp16)[name = string("op_480_cast_fp16")]; + tensor q_13_begin_0 = const()[name = string("q_13_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_13_end_0 = const()[name = string("q_13_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_13_end_mask_0 = const()[name = string("q_13_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_13_squeeze_mask_0 = const()[name = string("q_13_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_13_cast_fp16 = slice_by_index(begin = q_13_begin_0, end = q_13_end_0, end_mask = q_13_end_mask_0, squeeze_mask = q_13_squeeze_mask_0, x = var_480_cast_fp16)[name = string("q_13_cast_fp16")]; + tensor k_13_begin_0 = const()[name = string("k_13_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_13_end_0 = const()[name = string("k_13_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_13_end_mask_0 = const()[name = string("k_13_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_13_squeeze_mask_0 = const()[name = string("k_13_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_13_cast_fp16 = slice_by_index(begin = k_13_begin_0, end = k_13_end_0, end_mask = k_13_end_mask_0, squeeze_mask = k_13_squeeze_mask_0, x = var_480_cast_fp16)[name = string("k_13_cast_fp16")]; + tensor v_13_begin_0 = const()[name = string("v_13_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_13_end_0 = const()[name = string("v_13_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_13_end_mask_0 = const()[name = string("v_13_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_13_squeeze_mask_0 = const()[name = string("v_13_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_13_cast_fp16 = slice_by_index(begin = v_13_begin_0, end = v_13_end_0, end_mask = v_13_end_mask_0, squeeze_mask = v_13_squeeze_mask_0, x = var_480_cast_fp16)[name = string("v_13_cast_fp16")]; + tensor var_488 = const()[name = string("op_488"), val = tensor([512, 8, 40])]; + tensor var_489_cast_fp16 = reshape(shape = var_488, x = q_13_cast_fp16)[name = string("op_489_cast_fp16")]; + tensor q_15_perm_0 = const()[name = string("q_15_perm_0"), val = tensor([1, 0, 2])]; + tensor var_495 = const()[name = string("op_495"), val = tensor([512, 8, 40])]; + tensor var_496_cast_fp16 = reshape(shape = var_495, x = k_13_cast_fp16)[name = string("op_496_cast_fp16")]; + tensor k_15_perm_0 = const()[name = string("k_15_perm_0"), val = tensor([1, 0, 2])]; + tensor var_502 = const()[name = string("op_502"), val = tensor([512, 8, 40])]; + tensor var_503_cast_fp16 = reshape(shape = var_502, x = v_13_cast_fp16)[name = string("op_503_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([1, 0, 2])]; + tensor var_517 = const()[name = string("op_517"), val = tensor([1, 8, 512, 40])]; + tensor q_15_cast_fp16 = transpose(perm = q_15_perm_0, x = var_489_cast_fp16)[name = string("transpose_39")]; + tensor q_17_cast_fp16 = reshape(shape = var_517, x = q_15_cast_fp16)[name = string("q_17_cast_fp16")]; + tensor var_519 = const()[name = string("op_519"), val = tensor([1, 8, 512, 40])]; + tensor k_15_cast_fp16 = transpose(perm = k_15_perm_0, x = var_496_cast_fp16)[name = string("transpose_38")]; + tensor k_17_cast_fp16 = reshape(shape = var_519, x = k_15_cast_fp16)[name = string("k_17_cast_fp16")]; + tensor var_521 = const()[name = string("op_521"), val = tensor([1, 8, 512, 40])]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_503_cast_fp16)[name = string("transpose_37")]; + tensor v_17_cast_fp16 = reshape(shape = var_521, x = v_15_cast_fp16)[name = string("v_17_cast_fp16")]; + tensor attn_output_17_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_17_cast_fp16, query = q_17_cast_fp16, value = v_17_cast_fp16)[name = string("attn_output_17_cast_fp16")]; + tensor var_524 = const()[name = string("op_524"), val = tensor([2, 0, 1, 3])]; + tensor var_529 = const()[name = string("op_529"), val = tensor([512, 320])]; + tensor var_525_cast_fp16 = transpose(perm = var_524, x = attn_output_17_cast_fp16)[name = string("transpose_36")]; + tensor attn_output_19_cast_fp16 = reshape(shape = var_529, x = var_525_cast_fp16)[name = string("attn_output_19_cast_fp16")]; + tensor module_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3324160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3426624))))[name = string("module_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3427328)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_19_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_533 = const()[name = string("op_533"), val = tensor([512, 1, 320])]; + tensor attn_output_23_cast_fp16 = reshape(shape = var_533, x = linear_13_cast_fp16)[name = string("attn_output_23_cast_fp16")]; + tensor input_43_perm_0 = const()[name = string("input_43_perm_0"), val = tensor([1, 0, 2])]; + tensor input_43_cast_fp16 = transpose(perm = input_43_perm_0, x = attn_output_23_cast_fp16)[name = string("transpose_35")]; + tensor input_45_cast_fp16 = add(x = input_41_cast_fp16, y = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor input_47_axes_0 = const()[name = string("input_47_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_2_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_2_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3428032)))]; + tensor module_model_encoder_layers_2_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3428736)))]; + tensor input_47_cast_fp16 = layer_norm(axes = input_47_axes_0, beta = module_model_encoder_layers_2_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_2_norm2_weight_to_fp16, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")]; + tensor module_model_encoder_layers_2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3429440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3839104))))[name = string("module_model_encoder_layers_2_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_2_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3841728)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_layers_2_linear1_bias_to_fp16, weight = module_model_encoder_layers_2_linear1_weight_to_fp16_quantized, x = input_47_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_14_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_layers_2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4254016))))[name = string("module_model_encoder_layers_2_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_2_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4254720)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_layers_2_linear2_bias_to_fp16, weight = module_model_encoder_layers_2_linear2_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_55_cast_fp16 = add(x = input_45_cast_fp16, y = linear_15_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor query_13_axes_0 = const()[name = string("query_13_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_3_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_3_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4255424)))]; + tensor module_model_encoder_layers_3_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4256128)))]; + tensor query_13_cast_fp16 = layer_norm(axes = query_13_axes_0, beta = module_model_encoder_layers_3_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_3_norm1_weight_to_fp16, x = input_55_cast_fp16)[name = string("query_13_cast_fp16")]; + tensor query_15_perm_0 = const()[name = string("query_15_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_3_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4256832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4564096))))[name = string("module_model_encoder_layers_3_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_3_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4566080)))]; + tensor query_15_cast_fp16 = transpose(perm = query_15_perm_0, x = query_13_cast_fp16)[name = string("transpose_34")]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_layers_3_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_3_self_attn_in_proj_weight_to_fp16_quantized, x = query_15_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor concat_10 = const()[name = string("concat_10"), val = tensor([512, 1, 3, 320])]; + tensor var_584_cast_fp16 = reshape(shape = concat_10, x = linear_16_cast_fp16)[name = string("op_584_cast_fp16")]; + tensor var_585_axes_0 = const()[name = string("op_585_axes_0"), val = tensor([0])]; + tensor var_585_cast_fp16 = expand_dims(axes = var_585_axes_0, x = var_584_cast_fp16)[name = string("op_585_cast_fp16")]; + tensor var_586_perm_0 = const()[name = string("op_586_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_587_axes_0 = const()[name = string("op_587_axes_0"), val = tensor([-2])]; + tensor var_586_cast_fp16 = transpose(perm = var_586_perm_0, x = var_585_cast_fp16)[name = string("transpose_33")]; + tensor var_587_cast_fp16 = squeeze(axes = var_587_axes_0, x = var_586_cast_fp16)[name = string("op_587_cast_fp16")]; + tensor q_19_begin_0 = const()[name = string("q_19_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_19_end_0 = const()[name = string("q_19_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_19_end_mask_0 = const()[name = string("q_19_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_19_squeeze_mask_0 = const()[name = string("q_19_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_19_cast_fp16 = slice_by_index(begin = q_19_begin_0, end = q_19_end_0, end_mask = q_19_end_mask_0, squeeze_mask = q_19_squeeze_mask_0, x = var_587_cast_fp16)[name = string("q_19_cast_fp16")]; + tensor k_19_begin_0 = const()[name = string("k_19_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_19_end_0 = const()[name = string("k_19_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_19_end_mask_0 = const()[name = string("k_19_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_19_squeeze_mask_0 = const()[name = string("k_19_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_19_cast_fp16 = slice_by_index(begin = k_19_begin_0, end = k_19_end_0, end_mask = k_19_end_mask_0, squeeze_mask = k_19_squeeze_mask_0, x = var_587_cast_fp16)[name = string("k_19_cast_fp16")]; + tensor v_19_begin_0 = const()[name = string("v_19_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_19_end_0 = const()[name = string("v_19_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_19_end_mask_0 = const()[name = string("v_19_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_19_squeeze_mask_0 = const()[name = string("v_19_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_19_cast_fp16 = slice_by_index(begin = v_19_begin_0, end = v_19_end_0, end_mask = v_19_end_mask_0, squeeze_mask = v_19_squeeze_mask_0, x = var_587_cast_fp16)[name = string("v_19_cast_fp16")]; + tensor var_595 = const()[name = string("op_595"), val = tensor([512, 8, 40])]; + tensor var_596_cast_fp16 = reshape(shape = var_595, x = q_19_cast_fp16)[name = string("op_596_cast_fp16")]; + tensor q_21_perm_0 = const()[name = string("q_21_perm_0"), val = tensor([1, 0, 2])]; + tensor var_602 = const()[name = string("op_602"), val = tensor([512, 8, 40])]; + tensor var_603_cast_fp16 = reshape(shape = var_602, x = k_19_cast_fp16)[name = string("op_603_cast_fp16")]; + tensor k_21_perm_0 = const()[name = string("k_21_perm_0"), val = tensor([1, 0, 2])]; + tensor var_609 = const()[name = string("op_609"), val = tensor([512, 8, 40])]; + tensor var_610_cast_fp16 = reshape(shape = var_609, x = v_19_cast_fp16)[name = string("op_610_cast_fp16")]; + tensor v_21_perm_0 = const()[name = string("v_21_perm_0"), val = tensor([1, 0, 2])]; + tensor var_624 = const()[name = string("op_624"), val = tensor([1, 8, 512, 40])]; + tensor q_21_cast_fp16 = transpose(perm = q_21_perm_0, x = var_596_cast_fp16)[name = string("transpose_32")]; + tensor q_23_cast_fp16 = reshape(shape = var_624, x = q_21_cast_fp16)[name = string("q_23_cast_fp16")]; + tensor var_626 = const()[name = string("op_626"), val = tensor([1, 8, 512, 40])]; + tensor k_21_cast_fp16 = transpose(perm = k_21_perm_0, x = var_603_cast_fp16)[name = string("transpose_31")]; + tensor k_23_cast_fp16 = reshape(shape = var_626, x = k_21_cast_fp16)[name = string("k_23_cast_fp16")]; + tensor var_628 = const()[name = string("op_628"), val = tensor([1, 8, 512, 40])]; + tensor v_21_cast_fp16 = transpose(perm = v_21_perm_0, x = var_610_cast_fp16)[name = string("transpose_30")]; + tensor v_23_cast_fp16 = reshape(shape = var_628, x = v_21_cast_fp16)[name = string("v_23_cast_fp16")]; + tensor attn_output_25_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_23_cast_fp16, query = q_23_cast_fp16, value = v_23_cast_fp16)[name = string("attn_output_25_cast_fp16")]; + tensor var_631 = const()[name = string("op_631"), val = tensor([2, 0, 1, 3])]; + tensor var_636 = const()[name = string("op_636"), val = tensor([512, 320])]; + tensor var_632_cast_fp16 = transpose(perm = var_631, x = attn_output_25_cast_fp16)[name = string("transpose_29")]; + tensor attn_output_27_cast_fp16 = reshape(shape = var_636, x = var_632_cast_fp16)[name = string("attn_output_27_cast_fp16")]; + tensor module_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4568064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4670528))))[name = string("module_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4671232)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_27_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_640 = const()[name = string("op_640"), val = tensor([512, 1, 320])]; + tensor attn_output_31_cast_fp16 = reshape(shape = var_640, x = linear_17_cast_fp16)[name = string("attn_output_31_cast_fp16")]; + tensor input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor([1, 0, 2])]; + tensor input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = attn_output_31_cast_fp16)[name = string("transpose_28")]; + tensor input_59_cast_fp16 = add(x = input_55_cast_fp16, y = input_57_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor input_61_axes_0 = const()[name = string("input_61_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_3_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_3_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4671936)))]; + tensor module_model_encoder_layers_3_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4672640)))]; + tensor input_61_cast_fp16 = layer_norm(axes = input_61_axes_0, beta = module_model_encoder_layers_3_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_3_norm2_weight_to_fp16, x = input_59_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor module_model_encoder_layers_3_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4673344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5083008))))[name = string("module_model_encoder_layers_3_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_3_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5085632)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_layers_3_linear1_bias_to_fp16, weight = module_model_encoder_layers_3_linear1_weight_to_fp16_quantized, x = input_61_cast_fp16)[name = string("linear_18_cast_fp16")]; + string input_63_mode_0 = const()[name = string("input_63_mode_0"), val = string("EXACT")]; + tensor input_63_cast_fp16 = gelu(mode = input_63_mode_0, x = linear_18_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_layers_3_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5088256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5497920))))[name = string("module_model_encoder_layers_3_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_3_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_3_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5498624)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_layers_3_linear2_bias_to_fp16, weight = module_model_encoder_layers_3_linear2_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_59_cast_fp16, y = linear_19_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor query_17_axes_0 = const()[name = string("query_17_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_4_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_4_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5499328)))]; + tensor module_model_encoder_layers_4_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5500032)))]; + tensor query_17_cast_fp16 = layer_norm(axes = query_17_axes_0, beta = module_model_encoder_layers_4_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_4_norm1_weight_to_fp16, x = input_69_cast_fp16)[name = string("query_17_cast_fp16")]; + tensor query_19_perm_0 = const()[name = string("query_19_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_4_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5500736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5808000))))[name = string("module_model_encoder_layers_4_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_4_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5809984)))]; + tensor query_19_cast_fp16 = transpose(perm = query_19_perm_0, x = query_17_cast_fp16)[name = string("transpose_27")]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_layers_4_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_4_self_attn_in_proj_weight_to_fp16_quantized, x = query_19_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor concat_12 = const()[name = string("concat_12"), val = tensor([512, 1, 3, 320])]; + tensor var_691_cast_fp16 = reshape(shape = concat_12, x = linear_20_cast_fp16)[name = string("op_691_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([0])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_691_cast_fp16)[name = string("op_692_cast_fp16")]; + tensor var_693_perm_0 = const()[name = string("op_693_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_694_axes_0 = const()[name = string("op_694_axes_0"), val = tensor([-2])]; + tensor var_693_cast_fp16 = transpose(perm = var_693_perm_0, x = var_692_cast_fp16)[name = string("transpose_26")]; + tensor var_694_cast_fp16 = squeeze(axes = var_694_axes_0, x = var_693_cast_fp16)[name = string("op_694_cast_fp16")]; + tensor q_25_begin_0 = const()[name = string("q_25_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_25_end_0 = const()[name = string("q_25_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_25_end_mask_0 = const()[name = string("q_25_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_25_squeeze_mask_0 = const()[name = string("q_25_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_25_cast_fp16 = slice_by_index(begin = q_25_begin_0, end = q_25_end_0, end_mask = q_25_end_mask_0, squeeze_mask = q_25_squeeze_mask_0, x = var_694_cast_fp16)[name = string("q_25_cast_fp16")]; + tensor k_25_begin_0 = const()[name = string("k_25_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_25_end_0 = const()[name = string("k_25_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_25_end_mask_0 = const()[name = string("k_25_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_25_squeeze_mask_0 = const()[name = string("k_25_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_25_cast_fp16 = slice_by_index(begin = k_25_begin_0, end = k_25_end_0, end_mask = k_25_end_mask_0, squeeze_mask = k_25_squeeze_mask_0, x = var_694_cast_fp16)[name = string("k_25_cast_fp16")]; + tensor v_25_begin_0 = const()[name = string("v_25_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_25_end_0 = const()[name = string("v_25_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_25_end_mask_0 = const()[name = string("v_25_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_25_squeeze_mask_0 = const()[name = string("v_25_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_25_cast_fp16 = slice_by_index(begin = v_25_begin_0, end = v_25_end_0, end_mask = v_25_end_mask_0, squeeze_mask = v_25_squeeze_mask_0, x = var_694_cast_fp16)[name = string("v_25_cast_fp16")]; + tensor var_702 = const()[name = string("op_702"), val = tensor([512, 8, 40])]; + tensor var_703_cast_fp16 = reshape(shape = var_702, x = q_25_cast_fp16)[name = string("op_703_cast_fp16")]; + tensor q_27_perm_0 = const()[name = string("q_27_perm_0"), val = tensor([1, 0, 2])]; + tensor var_709 = const()[name = string("op_709"), val = tensor([512, 8, 40])]; + tensor var_710_cast_fp16 = reshape(shape = var_709, x = k_25_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor k_27_perm_0 = const()[name = string("k_27_perm_0"), val = tensor([1, 0, 2])]; + tensor var_716 = const()[name = string("op_716"), val = tensor([512, 8, 40])]; + tensor var_717_cast_fp16 = reshape(shape = var_716, x = v_25_cast_fp16)[name = string("op_717_cast_fp16")]; + tensor v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor([1, 0, 2])]; + tensor var_731 = const()[name = string("op_731"), val = tensor([1, 8, 512, 40])]; + tensor q_27_cast_fp16 = transpose(perm = q_27_perm_0, x = var_703_cast_fp16)[name = string("transpose_25")]; + tensor q_29_cast_fp16 = reshape(shape = var_731, x = q_27_cast_fp16)[name = string("q_29_cast_fp16")]; + tensor var_733 = const()[name = string("op_733"), val = tensor([1, 8, 512, 40])]; + tensor k_27_cast_fp16 = transpose(perm = k_27_perm_0, x = var_710_cast_fp16)[name = string("transpose_24")]; + tensor k_29_cast_fp16 = reshape(shape = var_733, x = k_27_cast_fp16)[name = string("k_29_cast_fp16")]; + tensor var_735 = const()[name = string("op_735"), val = tensor([1, 8, 512, 40])]; + tensor v_27_cast_fp16 = transpose(perm = v_27_perm_0, x = var_717_cast_fp16)[name = string("transpose_23")]; + tensor v_29_cast_fp16 = reshape(shape = var_735, x = v_27_cast_fp16)[name = string("v_29_cast_fp16")]; + tensor attn_output_33_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_29_cast_fp16, query = q_29_cast_fp16, value = v_29_cast_fp16)[name = string("attn_output_33_cast_fp16")]; + tensor var_738 = const()[name = string("op_738"), val = tensor([2, 0, 1, 3])]; + tensor var_743 = const()[name = string("op_743"), val = tensor([512, 320])]; + tensor var_739_cast_fp16 = transpose(perm = var_738, x = attn_output_33_cast_fp16)[name = string("transpose_22")]; + tensor attn_output_35_cast_fp16 = reshape(shape = var_743, x = var_739_cast_fp16)[name = string("attn_output_35_cast_fp16")]; + tensor module_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5811968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5914432))))[name = string("module_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5915136)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_35_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_747 = const()[name = string("op_747"), val = tensor([512, 1, 320])]; + tensor attn_output_39_cast_fp16 = reshape(shape = var_747, x = linear_21_cast_fp16)[name = string("attn_output_39_cast_fp16")]; + tensor input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor([1, 0, 2])]; + tensor input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = attn_output_39_cast_fp16)[name = string("transpose_21")]; + tensor input_73_cast_fp16 = add(x = input_69_cast_fp16, y = input_71_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_4_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_4_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5915840)))]; + tensor module_model_encoder_layers_4_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5916544)))]; + tensor input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = module_model_encoder_layers_4_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_4_norm2_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_layers_4_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5917248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6326912))))[name = string("module_model_encoder_layers_4_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_4_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6329536)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_layers_4_linear1_bias_to_fp16, weight = module_model_encoder_layers_4_linear1_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_77_mode_0 = const()[name = string("input_77_mode_0"), val = string("EXACT")]; + tensor input_77_cast_fp16 = gelu(mode = input_77_mode_0, x = linear_22_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_layers_4_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6332160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6741824))))[name = string("module_model_encoder_layers_4_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_4_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_4_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6742528)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_layers_4_linear2_bias_to_fp16, weight = module_model_encoder_layers_4_linear2_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_73_cast_fp16, y = linear_23_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor query_21_axes_0 = const()[name = string("query_21_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_5_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_5_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6743232)))]; + tensor module_model_encoder_layers_5_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6743936)))]; + tensor query_21_cast_fp16 = layer_norm(axes = query_21_axes_0, beta = module_model_encoder_layers_5_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_5_norm1_weight_to_fp16, x = input_83_cast_fp16)[name = string("query_21_cast_fp16")]; + tensor query_23_perm_0 = const()[name = string("query_23_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_5_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6744640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7051904))))[name = string("module_model_encoder_layers_5_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_5_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7053888)))]; + tensor query_23_cast_fp16 = transpose(perm = query_23_perm_0, x = query_21_cast_fp16)[name = string("transpose_20")]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_layers_5_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_5_self_attn_in_proj_weight_to_fp16_quantized, x = query_23_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor concat_14 = const()[name = string("concat_14"), val = tensor([512, 1, 3, 320])]; + tensor var_798_cast_fp16 = reshape(shape = concat_14, x = linear_24_cast_fp16)[name = string("op_798_cast_fp16")]; + tensor var_799_axes_0 = const()[name = string("op_799_axes_0"), val = tensor([0])]; + tensor var_799_cast_fp16 = expand_dims(axes = var_799_axes_0, x = var_798_cast_fp16)[name = string("op_799_cast_fp16")]; + tensor var_800_perm_0 = const()[name = string("op_800_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_801_axes_0 = const()[name = string("op_801_axes_0"), val = tensor([-2])]; + tensor var_800_cast_fp16 = transpose(perm = var_800_perm_0, x = var_799_cast_fp16)[name = string("transpose_19")]; + tensor var_801_cast_fp16 = squeeze(axes = var_801_axes_0, x = var_800_cast_fp16)[name = string("op_801_cast_fp16")]; + tensor q_31_begin_0 = const()[name = string("q_31_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_31_end_0 = const()[name = string("q_31_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_31_end_mask_0 = const()[name = string("q_31_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_31_squeeze_mask_0 = const()[name = string("q_31_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_31_cast_fp16 = slice_by_index(begin = q_31_begin_0, end = q_31_end_0, end_mask = q_31_end_mask_0, squeeze_mask = q_31_squeeze_mask_0, x = var_801_cast_fp16)[name = string("q_31_cast_fp16")]; + tensor k_31_begin_0 = const()[name = string("k_31_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_31_end_0 = const()[name = string("k_31_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_31_end_mask_0 = const()[name = string("k_31_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_31_squeeze_mask_0 = const()[name = string("k_31_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_31_cast_fp16 = slice_by_index(begin = k_31_begin_0, end = k_31_end_0, end_mask = k_31_end_mask_0, squeeze_mask = k_31_squeeze_mask_0, x = var_801_cast_fp16)[name = string("k_31_cast_fp16")]; + tensor v_31_begin_0 = const()[name = string("v_31_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_31_end_0 = const()[name = string("v_31_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_31_end_mask_0 = const()[name = string("v_31_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_31_squeeze_mask_0 = const()[name = string("v_31_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_31_cast_fp16 = slice_by_index(begin = v_31_begin_0, end = v_31_end_0, end_mask = v_31_end_mask_0, squeeze_mask = v_31_squeeze_mask_0, x = var_801_cast_fp16)[name = string("v_31_cast_fp16")]; + tensor var_809 = const()[name = string("op_809"), val = tensor([512, 8, 40])]; + tensor var_810_cast_fp16 = reshape(shape = var_809, x = q_31_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor q_33_perm_0 = const()[name = string("q_33_perm_0"), val = tensor([1, 0, 2])]; + tensor var_816 = const()[name = string("op_816"), val = tensor([512, 8, 40])]; + tensor var_817_cast_fp16 = reshape(shape = var_816, x = k_31_cast_fp16)[name = string("op_817_cast_fp16")]; + tensor k_33_perm_0 = const()[name = string("k_33_perm_0"), val = tensor([1, 0, 2])]; + tensor var_823 = const()[name = string("op_823"), val = tensor([512, 8, 40])]; + tensor var_824_cast_fp16 = reshape(shape = var_823, x = v_31_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor v_33_perm_0 = const()[name = string("v_33_perm_0"), val = tensor([1, 0, 2])]; + tensor var_838 = const()[name = string("op_838"), val = tensor([1, 8, 512, 40])]; + tensor q_33_cast_fp16 = transpose(perm = q_33_perm_0, x = var_810_cast_fp16)[name = string("transpose_18")]; + tensor q_35_cast_fp16 = reshape(shape = var_838, x = q_33_cast_fp16)[name = string("q_35_cast_fp16")]; + tensor var_840 = const()[name = string("op_840"), val = tensor([1, 8, 512, 40])]; + tensor k_33_cast_fp16 = transpose(perm = k_33_perm_0, x = var_817_cast_fp16)[name = string("transpose_17")]; + tensor k_35_cast_fp16 = reshape(shape = var_840, x = k_33_cast_fp16)[name = string("k_35_cast_fp16")]; + tensor var_842 = const()[name = string("op_842"), val = tensor([1, 8, 512, 40])]; + tensor v_33_cast_fp16 = transpose(perm = v_33_perm_0, x = var_824_cast_fp16)[name = string("transpose_16")]; + tensor v_35_cast_fp16 = reshape(shape = var_842, x = v_33_cast_fp16)[name = string("v_35_cast_fp16")]; + tensor attn_output_41_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_35_cast_fp16, query = q_35_cast_fp16, value = v_35_cast_fp16)[name = string("attn_output_41_cast_fp16")]; + tensor var_845 = const()[name = string("op_845"), val = tensor([2, 0, 1, 3])]; + tensor var_850 = const()[name = string("op_850"), val = tensor([512, 320])]; + tensor var_846_cast_fp16 = transpose(perm = var_845, x = attn_output_41_cast_fp16)[name = string("transpose_15")]; + tensor attn_output_43_cast_fp16 = reshape(shape = var_850, x = var_846_cast_fp16)[name = string("attn_output_43_cast_fp16")]; + tensor module_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7055872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7158336))))[name = string("module_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159040)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_43_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_854 = const()[name = string("op_854"), val = tensor([512, 1, 320])]; + tensor attn_output_47_cast_fp16 = reshape(shape = var_854, x = linear_25_cast_fp16)[name = string("attn_output_47_cast_fp16")]; + tensor input_85_perm_0 = const()[name = string("input_85_perm_0"), val = tensor([1, 0, 2])]; + tensor input_85_cast_fp16 = transpose(perm = input_85_perm_0, x = attn_output_47_cast_fp16)[name = string("transpose_14")]; + tensor input_87_cast_fp16 = add(x = input_83_cast_fp16, y = input_85_cast_fp16)[name = string("input_87_cast_fp16")]; + tensor input_89_axes_0 = const()[name = string("input_89_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_5_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_5_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159744)))]; + tensor module_model_encoder_layers_5_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7160448)))]; + tensor input_89_cast_fp16 = layer_norm(axes = input_89_axes_0, beta = module_model_encoder_layers_5_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_5_norm2_weight_to_fp16, x = input_87_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_layers_5_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7161152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7570816))))[name = string("module_model_encoder_layers_5_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_5_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7573440)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_layers_5_linear1_bias_to_fp16, weight = module_model_encoder_layers_5_linear1_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_26_cast_fp16")]; + string input_91_mode_0 = const()[name = string("input_91_mode_0"), val = string("EXACT")]; + tensor input_91_cast_fp16 = gelu(mode = input_91_mode_0, x = linear_26_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_layers_5_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7576064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7985728))))[name = string("module_model_encoder_layers_5_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_5_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_5_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7986432)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_layers_5_linear2_bias_to_fp16, weight = module_model_encoder_layers_5_linear2_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_87_cast_fp16, y = linear_27_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor query_25_axes_0 = const()[name = string("query_25_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_6_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_6_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7987136)))]; + tensor module_model_encoder_layers_6_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7987840)))]; + tensor query_25_cast_fp16 = layer_norm(axes = query_25_axes_0, beta = module_model_encoder_layers_6_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_6_norm1_weight_to_fp16, x = input_97_cast_fp16)[name = string("query_25_cast_fp16")]; + tensor query_27_perm_0 = const()[name = string("query_27_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_6_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7988544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8295808))))[name = string("module_model_encoder_layers_6_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_6_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8297792)))]; + tensor query_27_cast_fp16 = transpose(perm = query_27_perm_0, x = query_25_cast_fp16)[name = string("transpose_13")]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_layers_6_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_6_self_attn_in_proj_weight_to_fp16_quantized, x = query_27_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor concat_16 = const()[name = string("concat_16"), val = tensor([512, 1, 3, 320])]; + tensor var_905_cast_fp16 = reshape(shape = concat_16, x = linear_28_cast_fp16)[name = string("op_905_cast_fp16")]; + tensor var_906_axes_0 = const()[name = string("op_906_axes_0"), val = tensor([0])]; + tensor var_906_cast_fp16 = expand_dims(axes = var_906_axes_0, x = var_905_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_907_perm_0 = const()[name = string("op_907_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-2])]; + tensor var_907_cast_fp16 = transpose(perm = var_907_perm_0, x = var_906_cast_fp16)[name = string("transpose_12")]; + tensor var_908_cast_fp16 = squeeze(axes = var_908_axes_0, x = var_907_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor q_37_begin_0 = const()[name = string("q_37_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_37_end_0 = const()[name = string("q_37_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_37_end_mask_0 = const()[name = string("q_37_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_37_squeeze_mask_0 = const()[name = string("q_37_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_37_cast_fp16 = slice_by_index(begin = q_37_begin_0, end = q_37_end_0, end_mask = q_37_end_mask_0, squeeze_mask = q_37_squeeze_mask_0, x = var_908_cast_fp16)[name = string("q_37_cast_fp16")]; + tensor k_37_begin_0 = const()[name = string("k_37_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_37_end_0 = const()[name = string("k_37_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_37_end_mask_0 = const()[name = string("k_37_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_37_squeeze_mask_0 = const()[name = string("k_37_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_37_cast_fp16 = slice_by_index(begin = k_37_begin_0, end = k_37_end_0, end_mask = k_37_end_mask_0, squeeze_mask = k_37_squeeze_mask_0, x = var_908_cast_fp16)[name = string("k_37_cast_fp16")]; + tensor v_37_begin_0 = const()[name = string("v_37_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_37_end_0 = const()[name = string("v_37_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_37_end_mask_0 = const()[name = string("v_37_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_37_squeeze_mask_0 = const()[name = string("v_37_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_37_cast_fp16 = slice_by_index(begin = v_37_begin_0, end = v_37_end_0, end_mask = v_37_end_mask_0, squeeze_mask = v_37_squeeze_mask_0, x = var_908_cast_fp16)[name = string("v_37_cast_fp16")]; + tensor var_916 = const()[name = string("op_916"), val = tensor([512, 8, 40])]; + tensor var_917_cast_fp16 = reshape(shape = var_916, x = q_37_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor q_39_perm_0 = const()[name = string("q_39_perm_0"), val = tensor([1, 0, 2])]; + tensor var_923 = const()[name = string("op_923"), val = tensor([512, 8, 40])]; + tensor var_924_cast_fp16 = reshape(shape = var_923, x = k_37_cast_fp16)[name = string("op_924_cast_fp16")]; + tensor k_39_perm_0 = const()[name = string("k_39_perm_0"), val = tensor([1, 0, 2])]; + tensor var_930 = const()[name = string("op_930"), val = tensor([512, 8, 40])]; + tensor var_931_cast_fp16 = reshape(shape = var_930, x = v_37_cast_fp16)[name = string("op_931_cast_fp16")]; + tensor v_39_perm_0 = const()[name = string("v_39_perm_0"), val = tensor([1, 0, 2])]; + tensor var_945 = const()[name = string("op_945"), val = tensor([1, 8, 512, 40])]; + tensor q_39_cast_fp16 = transpose(perm = q_39_perm_0, x = var_917_cast_fp16)[name = string("transpose_11")]; + tensor q_41_cast_fp16 = reshape(shape = var_945, x = q_39_cast_fp16)[name = string("q_41_cast_fp16")]; + tensor var_947 = const()[name = string("op_947"), val = tensor([1, 8, 512, 40])]; + tensor k_39_cast_fp16 = transpose(perm = k_39_perm_0, x = var_924_cast_fp16)[name = string("transpose_10")]; + tensor k_41_cast_fp16 = reshape(shape = var_947, x = k_39_cast_fp16)[name = string("k_41_cast_fp16")]; + tensor var_949 = const()[name = string("op_949"), val = tensor([1, 8, 512, 40])]; + tensor v_39_cast_fp16 = transpose(perm = v_39_perm_0, x = var_931_cast_fp16)[name = string("transpose_9")]; + tensor v_41_cast_fp16 = reshape(shape = var_949, x = v_39_cast_fp16)[name = string("v_41_cast_fp16")]; + tensor attn_output_49_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_41_cast_fp16, query = q_41_cast_fp16, value = v_41_cast_fp16)[name = string("attn_output_49_cast_fp16")]; + tensor var_952 = const()[name = string("op_952"), val = tensor([2, 0, 1, 3])]; + tensor var_957 = const()[name = string("op_957"), val = tensor([512, 320])]; + tensor var_953_cast_fp16 = transpose(perm = var_952, x = attn_output_49_cast_fp16)[name = string("transpose_8")]; + tensor attn_output_51_cast_fp16 = reshape(shape = var_957, x = var_953_cast_fp16)[name = string("attn_output_51_cast_fp16")]; + tensor module_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8299776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8402240))))[name = string("module_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8402944)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_51_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_961 = const()[name = string("op_961"), val = tensor([512, 1, 320])]; + tensor attn_output_55_cast_fp16 = reshape(shape = var_961, x = linear_29_cast_fp16)[name = string("attn_output_55_cast_fp16")]; + tensor input_99_perm_0 = const()[name = string("input_99_perm_0"), val = tensor([1, 0, 2])]; + tensor input_99_cast_fp16 = transpose(perm = input_99_perm_0, x = attn_output_55_cast_fp16)[name = string("transpose_7")]; + tensor input_101_cast_fp16 = add(x = input_97_cast_fp16, y = input_99_cast_fp16)[name = string("input_101_cast_fp16")]; + tensor input_103_axes_0 = const()[name = string("input_103_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_6_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_6_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8403648)))]; + tensor module_model_encoder_layers_6_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8404352)))]; + tensor input_103_cast_fp16 = layer_norm(axes = input_103_axes_0, beta = module_model_encoder_layers_6_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_6_norm2_weight_to_fp16, x = input_101_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_layers_6_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8405056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8814720))))[name = string("module_model_encoder_layers_6_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_6_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8817344)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_layers_6_linear1_bias_to_fp16, weight = module_model_encoder_layers_6_linear1_weight_to_fp16_quantized, x = input_103_cast_fp16)[name = string("linear_30_cast_fp16")]; + string input_105_mode_0 = const()[name = string("input_105_mode_0"), val = string("EXACT")]; + tensor input_105_cast_fp16 = gelu(mode = input_105_mode_0, x = linear_30_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_layers_6_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8819968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9229632))))[name = string("module_model_encoder_layers_6_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_6_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_6_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9230336)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_layers_6_linear2_bias_to_fp16, weight = module_model_encoder_layers_6_linear2_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = input_101_cast_fp16, y = linear_31_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor query_29_axes_0 = const()[name = string("query_29_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_7_norm1_weight_to_fp16 = const()[name = string("module_model_encoder_layers_7_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9231040)))]; + tensor module_model_encoder_layers_7_norm1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9231744)))]; + tensor query_29_cast_fp16 = layer_norm(axes = query_29_axes_0, beta = module_model_encoder_layers_7_norm1_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_7_norm1_weight_to_fp16, x = input_111_cast_fp16)[name = string("query_29_cast_fp16")]; + tensor query_perm_0 = const()[name = string("query_perm_0"), val = tensor([1, 0, 2])]; + tensor module_model_encoder_layers_7_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9232448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9539712))))[name = string("module_model_encoder_layers_7_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_7_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9541696)))]; + tensor query_cast_fp16 = transpose(perm = query_perm_0, x = query_29_cast_fp16)[name = string("transpose_6")]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_layers_7_self_attn_in_proj_bias_to_fp16, weight = module_model_encoder_layers_7_self_attn_in_proj_weight_to_fp16_quantized, x = query_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor concat_18 = const()[name = string("concat_18"), val = tensor([512, 1, 3, 320])]; + tensor var_1012_cast_fp16 = reshape(shape = concat_18, x = linear_32_cast_fp16)[name = string("op_1012_cast_fp16")]; + tensor var_1013_axes_0 = const()[name = string("op_1013_axes_0"), val = tensor([0])]; + tensor var_1013_cast_fp16 = expand_dims(axes = var_1013_axes_0, x = var_1012_cast_fp16)[name = string("op_1013_cast_fp16")]; + tensor var_1014_perm_0 = const()[name = string("op_1014_perm_0"), val = tensor([-2, 1, 2, 0, 4])]; + tensor var_1015_axes_0 = const()[name = string("op_1015_axes_0"), val = tensor([-2])]; + tensor var_1014_cast_fp16 = transpose(perm = var_1014_perm_0, x = var_1013_cast_fp16)[name = string("transpose_5")]; + tensor var_1015_cast_fp16 = squeeze(axes = var_1015_axes_0, x = var_1014_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor q_43_begin_0 = const()[name = string("q_43_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor q_43_end_0 = const()[name = string("q_43_end_0"), val = tensor([1, 512, 1, 320])]; + tensor q_43_end_mask_0 = const()[name = string("q_43_end_mask_0"), val = tensor([false, true, true, true])]; + tensor q_43_squeeze_mask_0 = const()[name = string("q_43_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor q_43_cast_fp16 = slice_by_index(begin = q_43_begin_0, end = q_43_end_0, end_mask = q_43_end_mask_0, squeeze_mask = q_43_squeeze_mask_0, x = var_1015_cast_fp16)[name = string("q_43_cast_fp16")]; + tensor k_43_begin_0 = const()[name = string("k_43_begin_0"), val = tensor([1, 0, 0, 0])]; + tensor k_43_end_0 = const()[name = string("k_43_end_0"), val = tensor([2, 512, 1, 320])]; + tensor k_43_end_mask_0 = const()[name = string("k_43_end_mask_0"), val = tensor([false, true, true, true])]; + tensor k_43_squeeze_mask_0 = const()[name = string("k_43_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor k_43_cast_fp16 = slice_by_index(begin = k_43_begin_0, end = k_43_end_0, end_mask = k_43_end_mask_0, squeeze_mask = k_43_squeeze_mask_0, x = var_1015_cast_fp16)[name = string("k_43_cast_fp16")]; + tensor v_43_begin_0 = const()[name = string("v_43_begin_0"), val = tensor([2, 0, 0, 0])]; + tensor v_43_end_0 = const()[name = string("v_43_end_0"), val = tensor([3, 512, 1, 320])]; + tensor v_43_end_mask_0 = const()[name = string("v_43_end_mask_0"), val = tensor([false, true, true, true])]; + tensor v_43_squeeze_mask_0 = const()[name = string("v_43_squeeze_mask_0"), val = tensor([true, false, false, false])]; + tensor v_43_cast_fp16 = slice_by_index(begin = v_43_begin_0, end = v_43_end_0, end_mask = v_43_end_mask_0, squeeze_mask = v_43_squeeze_mask_0, x = var_1015_cast_fp16)[name = string("v_43_cast_fp16")]; + tensor var_1023 = const()[name = string("op_1023"), val = tensor([512, 8, 40])]; + tensor var_1024_cast_fp16 = reshape(shape = var_1023, x = q_43_cast_fp16)[name = string("op_1024_cast_fp16")]; + tensor q_45_perm_0 = const()[name = string("q_45_perm_0"), val = tensor([1, 0, 2])]; + tensor var_1030 = const()[name = string("op_1030"), val = tensor([512, 8, 40])]; + tensor var_1031_cast_fp16 = reshape(shape = var_1030, x = k_43_cast_fp16)[name = string("op_1031_cast_fp16")]; + tensor k_45_perm_0 = const()[name = string("k_45_perm_0"), val = tensor([1, 0, 2])]; + tensor var_1037 = const()[name = string("op_1037"), val = tensor([512, 8, 40])]; + tensor var_1038_cast_fp16 = reshape(shape = var_1037, x = v_43_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor v_45_perm_0 = const()[name = string("v_45_perm_0"), val = tensor([1, 0, 2])]; + tensor var_1052 = const()[name = string("op_1052"), val = tensor([1, 8, 512, 40])]; + tensor q_45_cast_fp16 = transpose(perm = q_45_perm_0, x = var_1024_cast_fp16)[name = string("transpose_4")]; + tensor q_cast_fp16 = reshape(shape = var_1052, x = q_45_cast_fp16)[name = string("q_cast_fp16")]; + tensor var_1054 = const()[name = string("op_1054"), val = tensor([1, 8, 512, 40])]; + tensor k_45_cast_fp16 = transpose(perm = k_45_perm_0, x = var_1031_cast_fp16)[name = string("transpose_3")]; + tensor k_cast_fp16 = reshape(shape = var_1054, x = k_45_cast_fp16)[name = string("k_cast_fp16")]; + tensor var_1056 = const()[name = string("op_1056"), val = tensor([1, 8, 512, 40])]; + tensor v_45_cast_fp16 = transpose(perm = v_45_perm_0, x = var_1038_cast_fp16)[name = string("transpose_2")]; + tensor v_cast_fp16 = reshape(shape = var_1056, x = v_45_cast_fp16)[name = string("v_cast_fp16")]; + tensor attn_output_57_cast_fp16 = scaled_dot_product_attention(attn_mask = var_295_cast_fp16, key = k_cast_fp16, query = q_cast_fp16, value = v_cast_fp16)[name = string("attn_output_57_cast_fp16")]; + tensor var_1059 = const()[name = string("op_1059"), val = tensor([2, 0, 1, 3])]; + tensor var_1064 = const()[name = string("op_1064"), val = tensor([512, 320])]; + tensor var_1060_cast_fp16 = transpose(perm = var_1059, x = attn_output_57_cast_fp16)[name = string("transpose_1")]; + tensor attn_output_59_cast_fp16 = reshape(shape = var_1064, x = var_1060_cast_fp16)[name = string("attn_output_59_cast_fp16")]; + tensor module_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9543680))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9646144))))[name = string("module_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9646848)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16, weight = module_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16_quantized, x = attn_output_59_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_1068 = const()[name = string("op_1068"), val = tensor([512, 1, 320])]; + tensor attn_output_cast_fp16 = reshape(shape = var_1068, x = linear_33_cast_fp16)[name = string("attn_output_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([1, 0, 2])]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = attn_output_cast_fp16)[name = string("transpose_0")]; + tensor input_115_cast_fp16 = add(x = input_111_cast_fp16, y = input_113_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor input_117_axes_0 = const()[name = string("input_117_axes_0"), val = tensor([-1])]; + tensor module_model_encoder_layers_7_norm2_weight_to_fp16 = const()[name = string("module_model_encoder_layers_7_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9647552)))]; + tensor module_model_encoder_layers_7_norm2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9648256)))]; + tensor input_117_cast_fp16 = layer_norm(axes = input_117_axes_0, beta = module_model_encoder_layers_7_norm2_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_encoder_layers_7_norm2_weight_to_fp16, x = input_115_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_layers_7_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9648960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10058624))))[name = string("module_model_encoder_layers_7_linear1_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_7_linear1_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10061248)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_layers_7_linear1_bias_to_fp16, weight = module_model_encoder_layers_7_linear1_weight_to_fp16_quantized, x = input_117_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_119_mode_0 = const()[name = string("input_119_mode_0"), val = string("EXACT")]; + tensor input_119_cast_fp16 = gelu(mode = input_119_mode_0, x = linear_34_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_layers_7_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10063872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10473536))))[name = string("module_model_encoder_layers_7_linear2_weight_to_fp16_quantized")]; + tensor module_model_encoder_layers_7_linear2_bias_to_fp16 = const()[name = string("module_model_encoder_layers_7_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10474240)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_layers_7_linear2_bias_to_fp16, weight = module_model_encoder_layers_7_linear2_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_115_cast_fp16, y = linear_35_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor input_127_axes_0 = const()[name = string("input_127_axes_0"), val = tensor([-1])]; + tensor module_model_norm_weight_to_fp16 = const()[name = string("module_model_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10474944)))]; + tensor module_model_norm_bias_to_fp16 = const()[name = string("module_model_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10475648)))]; + tensor input_127_cast_fp16 = layer_norm(axes = input_127_axes_0, beta = module_model_norm_bias_to_fp16, epsilon = var_45_to_fp16, gamma = module_model_norm_weight_to_fp16, x = input_125_cast_fp16)[name = string("input_127_cast_fp16")]; + tensor module_model_head_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10476352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10578816))))[name = string("module_model_head_0_weight_to_fp16_quantized")]; + tensor module_model_head_0_bias_to_fp16 = const()[name = string("module_model_head_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10579520)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_head_0_bias_to_fp16, weight = module_model_head_0_weight_to_fp16_quantized, x = input_127_cast_fp16)[name = string("linear_36_cast_fp16")]; + string input_131_mode_0 = const()[name = string("input_131_mode_0"), val = string("EXACT")]; + tensor input_131_cast_fp16 = gelu(mode = input_131_mode_0, x = linear_36_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_head_3_weight_to_fp16 = const()[name = string("module_model_head_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10580224)))]; + tensor module_model_head_3_bias_to_fp16 = const()[name = string("module_model_head_3_bias_to_fp16"), val = tensor([0x1.778p-1])]; + tensor linear_37_cast_fp16 = linear(bias = module_model_head_3_bias_to_fp16, weight = module_model_head_3_weight_to_fp16, x = input_131_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor log_frames_axes_0 = const()[name = string("log_frames_axes_0"), val = tensor([-1])]; + tensor log_frames_cast_fp16 = squeeze(axes = log_frames_axes_0, x = linear_37_cast_fp16)[name = string("log_frames_cast_fp16")]; + fp16 var_34_to_fp16 = const()[name = string("op_34_to_fp16"), val = fp16(-0x1p+2)]; + fp16 var_33_to_fp16 = const()[name = string("op_33_to_fp16"), val = fp16(0x1p+3)]; + tensor clip_2_cast_fp16 = clip(alpha = var_34_to_fp16, beta = var_33_to_fp16, x = log_frames_cast_fp16)[name = string("clip_2_cast_fp16")]; + tensor var_1103_cast_fp16 = exp(x = clip_2_cast_fp16)[name = string("op_1103_cast_fp16")]; + fp16 var_1104_to_fp16 = const()[name = string("op_1104_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1105_cast_fp16 = sub(x = var_1103_cast_fp16, y = var_1104_to_fp16)[name = string("op_1105_cast_fp16")]; + tensor var_1106_cast_fp16 = maximum(x = var_1105_cast_fp16, y = var_51_to_fp16)[name = string("op_1106_cast_fp16")]; + string var_1106_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1106_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor durations = cast(dtype = var_1106_cast_fp16_to_fp32_dtype_0, x = var_1106_cast_fp16)[name = string("cast_1")]; + } -> (durations); +} \ No newline at end of file diff --git a/coreml/coreml/duration_predictor.mlmodelc/weights/weight.bin b/coreml/coreml/duration_predictor.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..ea8ec4253cdfdeca2ecc04d408f173bc66dbe0fb --- /dev/null +++ b/coreml/coreml/duration_predictor.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e6314ef13cc2edaffdc96840a5bf4c7e729630fb22e562f5383be48e3458038 +size 10580928 diff --git a/coreml/coreml/g2p.mlmodelc/analytics/coremldata.bin b/coreml/coreml/g2p.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..3839b05580affc3b1256f860c01ff561ccc9b88d --- /dev/null +++ b/coreml/coreml/g2p.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7741bcd28fc26f23c56042008129e50f9b17b0644b70c7980659a7cabf7a668a +size 243 diff --git a/coreml/coreml/g2p.mlmodelc/coremldata.bin b/coreml/coreml/g2p.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..2ff1a68088f4fbf7352225e1ef0264f583f1a13f --- /dev/null +++ b/coreml/coreml/g2p.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f5fc8bee0186f33fa14d35a59a58ec3393676d07bdf03802a3836d806662cd03 +size 377 diff --git a/coreml/coreml/g2p.mlmodelc/model.mil b/coreml/coreml/g2p.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..8ca2158e00759b1324097247d1902774774b26a2 --- /dev/null +++ b/coreml/coreml/g2p.mlmodelc/model.mil @@ -0,0 +1,2100 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func g2p(tensor text) { + int32 var_24 = const()[name = string("op_24"), val = int32(-1)]; + int32 var_26 = const()[name = string("op_26"), val = int32(0)]; + tensor src_pad_mask = equal(x = text, y = var_26)[name = string("src_pad_mask")]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor module_embedding_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257408))))[name = string("module_embedding_weight_to_fp16_quantized")]; + string text_to_int16_dtype_0 = const()[name = string("text_to_int16_dtype_0"), val = string("int16")]; + string cast_49_dtype_0 = const()[name = string("cast_49_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_to_int16 = cast(dtype = text_to_int16_dtype_0, x = text)[name = string("cast_6")]; + tensor cast_49 = cast(dtype = cast_49_dtype_0, x = text_to_int16)[name = string("cast_5")]; + tensor greater_equal_0 = greater_equal(x = cast_49, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(402)]; + tensor add_0 = add(x = cast_49, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_49, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_4")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_3")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(402)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 x_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_1, validate_indices = x_1_validate_indices_0, x = module_embedding_weight_to_fp16_quantized)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor op_286_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27017920))), scale = tensor([[[0x1.724p-7]]]))[name = string("op_286_to_fp16_quantized")]; + tensor input_1_cast_fp16 = add(x = x_1_cast_fp16_cast_uint16_cast_uint16, y = op_286_to_fp16_quantized)[name = string("input_1_cast_fp16")]; + tensor module_layers_0_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1528192))))[name = string("module_layers_0_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1532096)))]; + tensor linear_0_cast_fp16 = linear(bias = module_layers_0_self_attn_in_proj_bias_to_fp16, weight = module_layers_0_self_attn_in_proj_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_292_split_sizes_0 = const()[name = string("op_292_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_292_axis_0 = const()[name = string("op_292_axis_0"), val = int32(-1)]; + tensor var_292_cast_fp16_0, tensor var_292_cast_fp16_1, tensor var_292_cast_fp16_2 = split(axis = var_292_axis_0, split_sizes = var_292_split_sizes_0, x = linear_0_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor var_295 = const()[name = string("op_295"), val = tensor([1, 512, 4, 160])]; + tensor var_296_cast_fp16 = reshape(shape = var_295, x = var_292_cast_fp16_0)[name = string("op_296_cast_fp16")]; + tensor var_298 = const()[name = string("op_298"), val = tensor([1, 512, 4, 160])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = var_292_cast_fp16_1)[name = string("op_299_cast_fp16")]; + tensor var_301 = const()[name = string("op_301"), val = tensor([1, 512, 4, 160])]; + tensor var_302_cast_fp16 = reshape(shape = var_301, x = var_292_cast_fp16_2)[name = string("op_302_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_305_transpose_x_0 = const()[name = string("op_305_transpose_x_0"), val = bool(false)]; + bool var_305_transpose_y_0 = const()[name = string("op_305_transpose_y_0"), val = bool(false)]; + tensor transpose_32_perm_0 = const()[name = string("transpose_32_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_33_perm_0 = const()[name = string("transpose_33_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_33 = transpose(perm = transpose_33_perm_0, x = var_299_cast_fp16)[name = string("transpose_78")]; + tensor transpose_32 = transpose(perm = transpose_32_perm_0, x = var_296_cast_fp16)[name = string("transpose_79")]; + tensor var_305_cast_fp16 = matmul(transpose_x = var_305_transpose_x_0, transpose_y = var_305_transpose_y_0, x = transpose_32, y = transpose_33)[name = string("op_305_cast_fp16")]; + fp16 var_306_to_fp16 = const()[name = string("op_306_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_1_cast_fp16 = mul(x = var_305_cast_fp16, y = var_306_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor weights_1_cast_fp16 = softmax(axis = var_24, x = scores_1_cast_fp16)[name = string("weights_1_cast_fp16")]; + fp16 var_13_to_fp16 = const()[name = string("op_13_to_fp16"), val = fp16(0x1p+0)]; + string var_309_to_fp16_dtype_0 = const()[name = string("op_309_to_fp16_dtype_0"), val = string("fp16")]; + tensor src_pad_mask_to_fp16 = cast(dtype = var_309_to_fp16_dtype_0, x = src_pad_mask)[name = string("cast_2")]; + tensor var_310_cast_fp16 = sub(x = var_13_to_fp16, y = src_pad_mask_to_fp16)[name = string("op_310_cast_fp16")]; + tensor var_312_axes_0 = const()[name = string("op_312_axes_0"), val = tensor([1])]; + tensor var_312_cast_fp16 = expand_dims(axes = var_312_axes_0, x = var_310_cast_fp16)[name = string("op_312_cast_fp16")]; + tensor var_313_axes_0 = const()[name = string("op_313_axes_0"), val = tensor([2])]; + tensor var_313_cast_fp16 = expand_dims(axes = var_313_axes_0, x = var_312_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor weights_3_cast_fp16 = mul(x = weights_1_cast_fp16, y = var_313_cast_fp16)[name = string("weights_3_cast_fp16")]; + tensor var_317_axes_0 = const()[name = string("op_317_axes_0"), val = tensor([-1])]; + bool var_317_keep_dims_0 = const()[name = string("op_317_keep_dims_0"), val = bool(true)]; + tensor var_317_cast_fp16 = reduce_sum(axes = var_317_axes_0, keep_dims = var_317_keep_dims_0, x = weights_3_cast_fp16)[name = string("op_317_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_318_cast_fp16 = maximum(x = var_317_cast_fp16, y = var_11_to_fp16)[name = string("op_318_cast_fp16")]; + tensor weights_5_cast_fp16 = real_div(x = weights_3_cast_fp16, y = var_318_cast_fp16)[name = string("weights_5_cast_fp16")]; + bool context_1_transpose_x_0 = const()[name = string("context_1_transpose_x_0"), val = bool(false)]; + bool context_1_transpose_y_0 = const()[name = string("context_1_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_302_cast_fp16)[name = string("transpose_77")]; + tensor context_1_cast_fp16 = matmul(transpose_x = context_1_transpose_x_0, transpose_y = context_1_transpose_y_0, x = weights_5_cast_fp16, y = v_3_cast_fp16)[name = string("context_1_cast_fp16")]; + tensor var_321_perm_0 = const()[name = string("op_321_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_322 = const()[name = string("op_322"), val = tensor([1, 512, 640])]; + tensor var_321_cast_fp16 = transpose(perm = var_321_perm_0, x = context_1_cast_fp16)[name = string("transpose_76")]; + tensor context_3_cast_fp16 = reshape(shape = var_322, x = var_321_cast_fp16)[name = string("context_3_cast_fp16")]; + tensor module_layers_0_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1536000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1945664))))[name = string("module_layers_0_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1947008)))]; + tensor linear_1_cast_fp16 = linear(bias = module_layers_0_self_attn_out_proj_bias_to_fp16, weight = module_layers_0_self_attn_out_proj_weight_to_fp16_quantized, x = context_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = input_1_cast_fp16, y = linear_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm1_weight_to_fp16 = const()[name = string("module_layers_0_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1948352)))]; + tensor module_layers_0_norm1_bias_to_fp16 = const()[name = string("module_layers_0_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1949696)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_5_cast_fp16 = layer_norm(axes = input_5_axes_0, beta = module_layers_0_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm1_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor module_layers_0_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1951040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2770304))))[name = string("module_layers_0_linear1_weight_to_fp16_quantized")]; + tensor module_layers_0_linear1_bias_to_fp16 = const()[name = string("module_layers_0_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2772928)))]; + tensor linear_2_cast_fp16 = linear(bias = module_layers_0_linear1_bias_to_fp16, weight = module_layers_0_linear1_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor input_9_cast_fp16 = relu(x = linear_2_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_layers_0_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2775552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3594816))))[name = string("module_layers_0_linear2_weight_to_fp16_quantized")]; + tensor module_layers_0_linear2_bias_to_fp16 = const()[name = string("module_layers_0_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3596160)))]; + tensor linear_3_cast_fp16 = linear(bias = module_layers_0_linear2_bias_to_fp16, weight = module_layers_0_linear2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = input_5_cast_fp16, y = linear_3_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor x_5_axes_0 = const()[name = string("x_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm2_weight_to_fp16 = const()[name = string("module_layers_0_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3597504)))]; + tensor module_layers_0_norm2_bias_to_fp16 = const()[name = string("module_layers_0_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3598848)))]; + tensor x_5_cast_fp16 = layer_norm(axes = x_5_axes_0, beta = module_layers_0_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm2_weight_to_fp16, x = input_11_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_layers_1_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3600192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4829056))))[name = string("module_layers_1_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4832960)))]; + tensor linear_4_cast_fp16 = linear(bias = module_layers_1_self_attn_in_proj_bias_to_fp16, weight = module_layers_1_self_attn_in_proj_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_345_split_sizes_0 = const()[name = string("op_345_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_345_axis_0 = const()[name = string("op_345_axis_0"), val = int32(-1)]; + tensor var_345_cast_fp16_0, tensor var_345_cast_fp16_1, tensor var_345_cast_fp16_2 = split(axis = var_345_axis_0, split_sizes = var_345_split_sizes_0, x = linear_4_cast_fp16)[name = string("op_345_cast_fp16")]; + tensor var_348 = const()[name = string("op_348"), val = tensor([1, 512, 4, 160])]; + tensor var_349_cast_fp16 = reshape(shape = var_348, x = var_345_cast_fp16_0)[name = string("op_349_cast_fp16")]; + tensor var_351 = const()[name = string("op_351"), val = tensor([1, 512, 4, 160])]; + tensor var_352_cast_fp16 = reshape(shape = var_351, x = var_345_cast_fp16_1)[name = string("op_352_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 512, 4, 160])]; + tensor var_355_cast_fp16 = reshape(shape = var_354, x = var_345_cast_fp16_2)[name = string("op_355_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_358_transpose_x_0 = const()[name = string("op_358_transpose_x_0"), val = bool(false)]; + bool var_358_transpose_y_0 = const()[name = string("op_358_transpose_y_0"), val = bool(false)]; + tensor transpose_34_perm_0 = const()[name = string("transpose_34_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_35_perm_0 = const()[name = string("transpose_35_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_35 = transpose(perm = transpose_35_perm_0, x = var_352_cast_fp16)[name = string("transpose_74")]; + tensor transpose_34 = transpose(perm = transpose_34_perm_0, x = var_349_cast_fp16)[name = string("transpose_75")]; + tensor var_358_cast_fp16 = matmul(transpose_x = var_358_transpose_x_0, transpose_y = var_358_transpose_y_0, x = transpose_34, y = transpose_35)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_3_cast_fp16 = mul(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("scores_3_cast_fp16")]; + tensor weights_7_cast_fp16 = softmax(axis = var_24, x = scores_3_cast_fp16)[name = string("weights_7_cast_fp16")]; + tensor weights_9_cast_fp16 = mul(x = weights_7_cast_fp16, y = var_313_cast_fp16)[name = string("weights_9_cast_fp16")]; + tensor var_370_axes_0 = const()[name = string("op_370_axes_0"), val = tensor([-1])]; + bool var_370_keep_dims_0 = const()[name = string("op_370_keep_dims_0"), val = bool(true)]; + tensor var_370_cast_fp16 = reduce_sum(axes = var_370_axes_0, keep_dims = var_370_keep_dims_0, x = weights_9_cast_fp16)[name = string("op_370_cast_fp16")]; + tensor var_371_cast_fp16 = maximum(x = var_370_cast_fp16, y = var_11_to_fp16)[name = string("op_371_cast_fp16")]; + tensor weights_11_cast_fp16 = real_div(x = weights_9_cast_fp16, y = var_371_cast_fp16)[name = string("weights_11_cast_fp16")]; + bool context_5_transpose_x_0 = const()[name = string("context_5_transpose_x_0"), val = bool(false)]; + bool context_5_transpose_y_0 = const()[name = string("context_5_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_355_cast_fp16)[name = string("transpose_73")]; + tensor context_5_cast_fp16 = matmul(transpose_x = context_5_transpose_x_0, transpose_y = context_5_transpose_y_0, x = weights_11_cast_fp16, y = v_7_cast_fp16)[name = string("context_5_cast_fp16")]; + tensor var_374_perm_0 = const()[name = string("op_374_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_375 = const()[name = string("op_375"), val = tensor([1, 512, 640])]; + tensor var_374_cast_fp16 = transpose(perm = var_374_perm_0, x = context_5_cast_fp16)[name = string("transpose_72")]; + tensor context_7_cast_fp16 = reshape(shape = var_375, x = var_374_cast_fp16)[name = string("context_7_cast_fp16")]; + tensor module_layers_1_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4836864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5246528))))[name = string("module_layers_1_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5247872)))]; + tensor linear_5_cast_fp16 = linear(bias = module_layers_1_self_attn_out_proj_bias_to_fp16, weight = module_layers_1_self_attn_out_proj_weight_to_fp16_quantized, x = context_7_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = x_5_cast_fp16, y = linear_5_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm1_weight_to_fp16 = const()[name = string("module_layers_1_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5249216)))]; + tensor module_layers_1_norm1_bias_to_fp16 = const()[name = string("module_layers_1_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5250560)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_layers_1_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm1_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_layers_1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5251904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6071168))))[name = string("module_layers_1_linear1_weight_to_fp16_quantized")]; + tensor module_layers_1_linear1_bias_to_fp16 = const()[name = string("module_layers_1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6073792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_layers_1_linear1_bias_to_fp16, weight = module_layers_1_linear1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor input_19_cast_fp16 = relu(x = linear_6_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_layers_1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6076416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6895680))))[name = string("module_layers_1_linear2_weight_to_fp16_quantized")]; + tensor module_layers_1_linear2_bias_to_fp16 = const()[name = string("module_layers_1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6897024)))]; + tensor linear_7_cast_fp16 = linear(bias = module_layers_1_linear2_bias_to_fp16, weight = module_layers_1_linear2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = input_15_cast_fp16, y = linear_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm2_weight_to_fp16 = const()[name = string("module_layers_1_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6898368)))]; + tensor module_layers_1_norm2_bias_to_fp16 = const()[name = string("module_layers_1_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6899712)))]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = module_layers_1_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm2_weight_to_fp16, x = input_21_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor module_layers_2_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8129920))))[name = string("module_layers_2_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8133824)))]; + tensor linear_8_cast_fp16 = linear(bias = module_layers_2_self_attn_in_proj_bias_to_fp16, weight = module_layers_2_self_attn_in_proj_weight_to_fp16_quantized, x = x_7_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_398_split_sizes_0 = const()[name = string("op_398_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_398_axis_0 = const()[name = string("op_398_axis_0"), val = int32(-1)]; + tensor var_398_cast_fp16_0, tensor var_398_cast_fp16_1, tensor var_398_cast_fp16_2 = split(axis = var_398_axis_0, split_sizes = var_398_split_sizes_0, x = linear_8_cast_fp16)[name = string("op_398_cast_fp16")]; + tensor var_401 = const()[name = string("op_401"), val = tensor([1, 512, 4, 160])]; + tensor var_402_cast_fp16 = reshape(shape = var_401, x = var_398_cast_fp16_0)[name = string("op_402_cast_fp16")]; + tensor var_404 = const()[name = string("op_404"), val = tensor([1, 512, 4, 160])]; + tensor var_405_cast_fp16 = reshape(shape = var_404, x = var_398_cast_fp16_1)[name = string("op_405_cast_fp16")]; + tensor var_407 = const()[name = string("op_407"), val = tensor([1, 512, 4, 160])]; + tensor var_408_cast_fp16 = reshape(shape = var_407, x = var_398_cast_fp16_2)[name = string("op_408_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_411_transpose_x_0 = const()[name = string("op_411_transpose_x_0"), val = bool(false)]; + bool var_411_transpose_y_0 = const()[name = string("op_411_transpose_y_0"), val = bool(false)]; + tensor transpose_36_perm_0 = const()[name = string("transpose_36_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_37_perm_0 = const()[name = string("transpose_37_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_37 = transpose(perm = transpose_37_perm_0, x = var_405_cast_fp16)[name = string("transpose_70")]; + tensor transpose_36 = transpose(perm = transpose_36_perm_0, x = var_402_cast_fp16)[name = string("transpose_71")]; + tensor var_411_cast_fp16 = matmul(transpose_x = var_411_transpose_x_0, transpose_y = var_411_transpose_y_0, x = transpose_36, y = transpose_37)[name = string("op_411_cast_fp16")]; + fp16 var_412_to_fp16 = const()[name = string("op_412_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_5_cast_fp16 = mul(x = var_411_cast_fp16, y = var_412_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor weights_13_cast_fp16 = softmax(axis = var_24, x = scores_5_cast_fp16)[name = string("weights_13_cast_fp16")]; + tensor weights_15_cast_fp16 = mul(x = weights_13_cast_fp16, y = var_313_cast_fp16)[name = string("weights_15_cast_fp16")]; + tensor var_423_axes_0 = const()[name = string("op_423_axes_0"), val = tensor([-1])]; + bool var_423_keep_dims_0 = const()[name = string("op_423_keep_dims_0"), val = bool(true)]; + tensor var_423_cast_fp16 = reduce_sum(axes = var_423_axes_0, keep_dims = var_423_keep_dims_0, x = weights_15_cast_fp16)[name = string("op_423_cast_fp16")]; + tensor var_424_cast_fp16 = maximum(x = var_423_cast_fp16, y = var_11_to_fp16)[name = string("op_424_cast_fp16")]; + tensor weights_17_cast_fp16 = real_div(x = weights_15_cast_fp16, y = var_424_cast_fp16)[name = string("weights_17_cast_fp16")]; + bool context_9_transpose_x_0 = const()[name = string("context_9_transpose_x_0"), val = bool(false)]; + bool context_9_transpose_y_0 = const()[name = string("context_9_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_408_cast_fp16)[name = string("transpose_69")]; + tensor context_9_cast_fp16 = matmul(transpose_x = context_9_transpose_x_0, transpose_y = context_9_transpose_y_0, x = weights_17_cast_fp16, y = v_11_cast_fp16)[name = string("context_9_cast_fp16")]; + tensor var_427_perm_0 = const()[name = string("op_427_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_428 = const()[name = string("op_428"), val = tensor([1, 512, 640])]; + tensor var_427_cast_fp16 = transpose(perm = var_427_perm_0, x = context_9_cast_fp16)[name = string("transpose_68")]; + tensor context_11_cast_fp16 = reshape(shape = var_428, x = var_427_cast_fp16)[name = string("context_11_cast_fp16")]; + tensor module_layers_2_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8137728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8547392))))[name = string("module_layers_2_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548736)))]; + tensor linear_9_cast_fp16 = linear(bias = module_layers_2_self_attn_out_proj_bias_to_fp16, weight = module_layers_2_self_attn_out_proj_weight_to_fp16_quantized, x = context_11_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = x_7_cast_fp16, y = linear_9_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor input_25_axes_0 = const()[name = string("input_25_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm1_weight_to_fp16 = const()[name = string("module_layers_2_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8550080)))]; + tensor module_layers_2_norm1_bias_to_fp16 = const()[name = string("module_layers_2_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8551424)))]; + tensor input_25_cast_fp16 = layer_norm(axes = input_25_axes_0, beta = module_layers_2_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm1_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8552768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9372032))))[name = string("module_layers_2_linear1_weight_to_fp16_quantized")]; + tensor module_layers_2_linear1_bias_to_fp16 = const()[name = string("module_layers_2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9374656)))]; + tensor linear_10_cast_fp16 = linear(bias = module_layers_2_linear1_bias_to_fp16, weight = module_layers_2_linear1_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_29_cast_fp16 = relu(x = linear_10_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor module_layers_2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9377280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10196544))))[name = string("module_layers_2_linear2_weight_to_fp16_quantized")]; + tensor module_layers_2_linear2_bias_to_fp16 = const()[name = string("module_layers_2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10197888)))]; + tensor linear_11_cast_fp16 = linear(bias = module_layers_2_linear2_bias_to_fp16, weight = module_layers_2_linear2_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = input_25_cast_fp16, y = linear_11_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm2_weight_to_fp16 = const()[name = string("module_layers_2_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10199232)))]; + tensor module_layers_2_norm2_bias_to_fp16 = const()[name = string("module_layers_2_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10200576)))]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, beta = module_layers_2_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor module_layers_3_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10201920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11430784))))[name = string("module_layers_3_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11434688)))]; + tensor linear_12_cast_fp16 = linear(bias = module_layers_3_self_attn_in_proj_bias_to_fp16, weight = module_layers_3_self_attn_in_proj_weight_to_fp16_quantized, x = x_9_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_451_split_sizes_0 = const()[name = string("op_451_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_451_axis_0 = const()[name = string("op_451_axis_0"), val = int32(-1)]; + tensor var_451_cast_fp16_0, tensor var_451_cast_fp16_1, tensor var_451_cast_fp16_2 = split(axis = var_451_axis_0, split_sizes = var_451_split_sizes_0, x = linear_12_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor var_454 = const()[name = string("op_454"), val = tensor([1, 512, 4, 160])]; + tensor var_455_cast_fp16 = reshape(shape = var_454, x = var_451_cast_fp16_0)[name = string("op_455_cast_fp16")]; + tensor var_457 = const()[name = string("op_457"), val = tensor([1, 512, 4, 160])]; + tensor var_458_cast_fp16 = reshape(shape = var_457, x = var_451_cast_fp16_1)[name = string("op_458_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([1, 512, 4, 160])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = var_451_cast_fp16_2)[name = string("op_461_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_464_transpose_x_0 = const()[name = string("op_464_transpose_x_0"), val = bool(false)]; + bool var_464_transpose_y_0 = const()[name = string("op_464_transpose_y_0"), val = bool(false)]; + tensor transpose_38_perm_0 = const()[name = string("transpose_38_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_39_perm_0 = const()[name = string("transpose_39_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_39 = transpose(perm = transpose_39_perm_0, x = var_458_cast_fp16)[name = string("transpose_66")]; + tensor transpose_38 = transpose(perm = transpose_38_perm_0, x = var_455_cast_fp16)[name = string("transpose_67")]; + tensor var_464_cast_fp16 = matmul(transpose_x = var_464_transpose_x_0, transpose_y = var_464_transpose_y_0, x = transpose_38, y = transpose_39)[name = string("op_464_cast_fp16")]; + fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_7_cast_fp16 = mul(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("scores_7_cast_fp16")]; + tensor weights_19_cast_fp16 = softmax(axis = var_24, x = scores_7_cast_fp16)[name = string("weights_19_cast_fp16")]; + tensor weights_21_cast_fp16 = mul(x = weights_19_cast_fp16, y = var_313_cast_fp16)[name = string("weights_21_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + bool var_476_keep_dims_0 = const()[name = string("op_476_keep_dims_0"), val = bool(true)]; + tensor var_476_cast_fp16 = reduce_sum(axes = var_476_axes_0, keep_dims = var_476_keep_dims_0, x = weights_21_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor var_477_cast_fp16 = maximum(x = var_476_cast_fp16, y = var_11_to_fp16)[name = string("op_477_cast_fp16")]; + tensor weights_23_cast_fp16 = real_div(x = weights_21_cast_fp16, y = var_477_cast_fp16)[name = string("weights_23_cast_fp16")]; + bool context_13_transpose_x_0 = const()[name = string("context_13_transpose_x_0"), val = bool(false)]; + bool context_13_transpose_y_0 = const()[name = string("context_13_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_461_cast_fp16)[name = string("transpose_65")]; + tensor context_13_cast_fp16 = matmul(transpose_x = context_13_transpose_x_0, transpose_y = context_13_transpose_y_0, x = weights_23_cast_fp16, y = v_15_cast_fp16)[name = string("context_13_cast_fp16")]; + tensor var_480_perm_0 = const()[name = string("op_480_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_481 = const()[name = string("op_481"), val = tensor([1, 512, 640])]; + tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = context_13_cast_fp16)[name = string("transpose_64")]; + tensor context_15_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("context_15_cast_fp16")]; + tensor module_layers_3_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11438592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11848256))))[name = string("module_layers_3_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11849600)))]; + tensor linear_13_cast_fp16 = linear(bias = module_layers_3_self_attn_out_proj_bias_to_fp16, weight = module_layers_3_self_attn_out_proj_weight_to_fp16_quantized, x = context_15_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = x_9_cast_fp16, y = linear_13_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_axes_0 = const()[name = string("input_35_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm1_weight_to_fp16 = const()[name = string("module_layers_3_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11850944)))]; + tensor module_layers_3_norm1_bias_to_fp16 = const()[name = string("module_layers_3_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11852288)))]; + tensor input_35_cast_fp16 = layer_norm(axes = input_35_axes_0, beta = module_layers_3_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm1_weight_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_3_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11853632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12672896))))[name = string("module_layers_3_linear1_weight_to_fp16_quantized")]; + tensor module_layers_3_linear1_bias_to_fp16 = const()[name = string("module_layers_3_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12675520)))]; + tensor linear_14_cast_fp16 = linear(bias = module_layers_3_linear1_bias_to_fp16, weight = module_layers_3_linear1_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor input_39_cast_fp16 = relu(x = linear_14_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_layers_3_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12678144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13497408))))[name = string("module_layers_3_linear2_weight_to_fp16_quantized")]; + tensor module_layers_3_linear2_bias_to_fp16 = const()[name = string("module_layers_3_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13498752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_layers_3_linear2_bias_to_fp16, weight = module_layers_3_linear2_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = input_35_cast_fp16, y = linear_15_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm2_weight_to_fp16 = const()[name = string("module_layers_3_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13500096)))]; + tensor module_layers_3_norm2_bias_to_fp16 = const()[name = string("module_layers_3_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13501440)))]; + tensor x_11_cast_fp16 = layer_norm(axes = x_11_axes_0, beta = module_layers_3_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm2_weight_to_fp16, x = input_41_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_layers_4_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13502784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14731648))))[name = string("module_layers_4_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14735552)))]; + tensor linear_16_cast_fp16 = linear(bias = module_layers_4_self_attn_in_proj_bias_to_fp16, weight = module_layers_4_self_attn_in_proj_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_504_split_sizes_0 = const()[name = string("op_504_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_504_axis_0 = const()[name = string("op_504_axis_0"), val = int32(-1)]; + tensor var_504_cast_fp16_0, tensor var_504_cast_fp16_1, tensor var_504_cast_fp16_2 = split(axis = var_504_axis_0, split_sizes = var_504_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_504_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 512, 4, 160])]; + tensor var_508_cast_fp16 = reshape(shape = var_507, x = var_504_cast_fp16_0)[name = string("op_508_cast_fp16")]; + tensor var_510 = const()[name = string("op_510"), val = tensor([1, 512, 4, 160])]; + tensor var_511_cast_fp16 = reshape(shape = var_510, x = var_504_cast_fp16_1)[name = string("op_511_cast_fp16")]; + tensor var_513 = const()[name = string("op_513"), val = tensor([1, 512, 4, 160])]; + tensor var_514_cast_fp16 = reshape(shape = var_513, x = var_504_cast_fp16_2)[name = string("op_514_cast_fp16")]; + tensor v_19_perm_0 = const()[name = string("v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_517_transpose_x_0 = const()[name = string("op_517_transpose_x_0"), val = bool(false)]; + bool var_517_transpose_y_0 = const()[name = string("op_517_transpose_y_0"), val = bool(false)]; + tensor transpose_40_perm_0 = const()[name = string("transpose_40_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_41_perm_0 = const()[name = string("transpose_41_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_41 = transpose(perm = transpose_41_perm_0, x = var_511_cast_fp16)[name = string("transpose_62")]; + tensor transpose_40 = transpose(perm = transpose_40_perm_0, x = var_508_cast_fp16)[name = string("transpose_63")]; + tensor var_517_cast_fp16 = matmul(transpose_x = var_517_transpose_x_0, transpose_y = var_517_transpose_y_0, x = transpose_40, y = transpose_41)[name = string("op_517_cast_fp16")]; + fp16 var_518_to_fp16 = const()[name = string("op_518_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_9_cast_fp16 = mul(x = var_517_cast_fp16, y = var_518_to_fp16)[name = string("scores_9_cast_fp16")]; + tensor weights_25_cast_fp16 = softmax(axis = var_24, x = scores_9_cast_fp16)[name = string("weights_25_cast_fp16")]; + tensor weights_27_cast_fp16 = mul(x = weights_25_cast_fp16, y = var_313_cast_fp16)[name = string("weights_27_cast_fp16")]; + tensor var_529_axes_0 = const()[name = string("op_529_axes_0"), val = tensor([-1])]; + bool var_529_keep_dims_0 = const()[name = string("op_529_keep_dims_0"), val = bool(true)]; + tensor var_529_cast_fp16 = reduce_sum(axes = var_529_axes_0, keep_dims = var_529_keep_dims_0, x = weights_27_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = maximum(x = var_529_cast_fp16, y = var_11_to_fp16)[name = string("op_530_cast_fp16")]; + tensor weights_29_cast_fp16 = real_div(x = weights_27_cast_fp16, y = var_530_cast_fp16)[name = string("weights_29_cast_fp16")]; + bool context_17_transpose_x_0 = const()[name = string("context_17_transpose_x_0"), val = bool(false)]; + bool context_17_transpose_y_0 = const()[name = string("context_17_transpose_y_0"), val = bool(false)]; + tensor v_19_cast_fp16 = transpose(perm = v_19_perm_0, x = var_514_cast_fp16)[name = string("transpose_61")]; + tensor context_17_cast_fp16 = matmul(transpose_x = context_17_transpose_x_0, transpose_y = context_17_transpose_y_0, x = weights_29_cast_fp16, y = v_19_cast_fp16)[name = string("context_17_cast_fp16")]; + tensor var_533_perm_0 = const()[name = string("op_533_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_534 = const()[name = string("op_534"), val = tensor([1, 512, 640])]; + tensor var_533_cast_fp16 = transpose(perm = var_533_perm_0, x = context_17_cast_fp16)[name = string("transpose_60")]; + tensor context_19_cast_fp16 = reshape(shape = var_534, x = var_533_cast_fp16)[name = string("context_19_cast_fp16")]; + tensor module_layers_4_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14739456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15149120))))[name = string("module_layers_4_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15150464)))]; + tensor linear_17_cast_fp16 = linear(bias = module_layers_4_self_attn_out_proj_bias_to_fp16, weight = module_layers_4_self_attn_out_proj_weight_to_fp16_quantized, x = context_19_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = x_11_cast_fp16, y = linear_17_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm1_weight_to_fp16 = const()[name = string("module_layers_4_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15151808)))]; + tensor module_layers_4_norm1_bias_to_fp16 = const()[name = string("module_layers_4_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15153152)))]; + tensor input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = module_layers_4_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_layers_4_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15154496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15973760))))[name = string("module_layers_4_linear1_weight_to_fp16_quantized")]; + tensor module_layers_4_linear1_bias_to_fp16 = const()[name = string("module_layers_4_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15976384)))]; + tensor linear_18_cast_fp16 = linear(bias = module_layers_4_linear1_bias_to_fp16, weight = module_layers_4_linear1_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor input_49_cast_fp16 = relu(x = linear_18_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_layers_4_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15979008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16798272))))[name = string("module_layers_4_linear2_weight_to_fp16_quantized")]; + tensor module_layers_4_linear2_bias_to_fp16 = const()[name = string("module_layers_4_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16799616)))]; + tensor linear_19_cast_fp16 = linear(bias = module_layers_4_linear2_bias_to_fp16, weight = module_layers_4_linear2_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = input_45_cast_fp16, y = linear_19_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm2_weight_to_fp16 = const()[name = string("module_layers_4_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16800960)))]; + tensor module_layers_4_norm2_bias_to_fp16 = const()[name = string("module_layers_4_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16802304)))]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = module_layers_4_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm2_weight_to_fp16, x = input_51_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor module_layers_5_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16803648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18032512))))[name = string("module_layers_5_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18036416)))]; + tensor linear_20_cast_fp16 = linear(bias = module_layers_5_self_attn_in_proj_bias_to_fp16, weight = module_layers_5_self_attn_in_proj_weight_to_fp16_quantized, x = x_13_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_557_split_sizes_0 = const()[name = string("op_557_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_557_axis_0 = const()[name = string("op_557_axis_0"), val = int32(-1)]; + tensor var_557_cast_fp16_0, tensor var_557_cast_fp16_1, tensor var_557_cast_fp16_2 = split(axis = var_557_axis_0, split_sizes = var_557_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_560 = const()[name = string("op_560"), val = tensor([1, 512, 4, 160])]; + tensor var_561_cast_fp16 = reshape(shape = var_560, x = var_557_cast_fp16_0)[name = string("op_561_cast_fp16")]; + tensor var_563 = const()[name = string("op_563"), val = tensor([1, 512, 4, 160])]; + tensor var_564_cast_fp16 = reshape(shape = var_563, x = var_557_cast_fp16_1)[name = string("op_564_cast_fp16")]; + tensor var_566 = const()[name = string("op_566"), val = tensor([1, 512, 4, 160])]; + tensor var_567_cast_fp16 = reshape(shape = var_566, x = var_557_cast_fp16_2)[name = string("op_567_cast_fp16")]; + tensor v_23_perm_0 = const()[name = string("v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_570_transpose_x_0 = const()[name = string("op_570_transpose_x_0"), val = bool(false)]; + bool var_570_transpose_y_0 = const()[name = string("op_570_transpose_y_0"), val = bool(false)]; + tensor transpose_42_perm_0 = const()[name = string("transpose_42_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_43_perm_0 = const()[name = string("transpose_43_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_43 = transpose(perm = transpose_43_perm_0, x = var_564_cast_fp16)[name = string("transpose_58")]; + tensor transpose_42 = transpose(perm = transpose_42_perm_0, x = var_561_cast_fp16)[name = string("transpose_59")]; + tensor var_570_cast_fp16 = matmul(transpose_x = var_570_transpose_x_0, transpose_y = var_570_transpose_y_0, x = transpose_42, y = transpose_43)[name = string("op_570_cast_fp16")]; + fp16 var_571_to_fp16 = const()[name = string("op_571_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_11_cast_fp16 = mul(x = var_570_cast_fp16, y = var_571_to_fp16)[name = string("scores_11_cast_fp16")]; + tensor weights_31_cast_fp16 = softmax(axis = var_24, x = scores_11_cast_fp16)[name = string("weights_31_cast_fp16")]; + tensor weights_33_cast_fp16 = mul(x = weights_31_cast_fp16, y = var_313_cast_fp16)[name = string("weights_33_cast_fp16")]; + tensor var_582_axes_0 = const()[name = string("op_582_axes_0"), val = tensor([-1])]; + bool var_582_keep_dims_0 = const()[name = string("op_582_keep_dims_0"), val = bool(true)]; + tensor var_582_cast_fp16 = reduce_sum(axes = var_582_axes_0, keep_dims = var_582_keep_dims_0, x = weights_33_cast_fp16)[name = string("op_582_cast_fp16")]; + tensor var_583_cast_fp16 = maximum(x = var_582_cast_fp16, y = var_11_to_fp16)[name = string("op_583_cast_fp16")]; + tensor weights_35_cast_fp16 = real_div(x = weights_33_cast_fp16, y = var_583_cast_fp16)[name = string("weights_35_cast_fp16")]; + bool context_21_transpose_x_0 = const()[name = string("context_21_transpose_x_0"), val = bool(false)]; + bool context_21_transpose_y_0 = const()[name = string("context_21_transpose_y_0"), val = bool(false)]; + tensor v_23_cast_fp16 = transpose(perm = v_23_perm_0, x = var_567_cast_fp16)[name = string("transpose_57")]; + tensor context_21_cast_fp16 = matmul(transpose_x = context_21_transpose_x_0, transpose_y = context_21_transpose_y_0, x = weights_35_cast_fp16, y = v_23_cast_fp16)[name = string("context_21_cast_fp16")]; + tensor var_586_perm_0 = const()[name = string("op_586_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_587 = const()[name = string("op_587"), val = tensor([1, 512, 640])]; + tensor var_586_cast_fp16 = transpose(perm = var_586_perm_0, x = context_21_cast_fp16)[name = string("transpose_56")]; + tensor context_23_cast_fp16 = reshape(shape = var_587, x = var_586_cast_fp16)[name = string("context_23_cast_fp16")]; + tensor module_layers_5_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18040320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18449984))))[name = string("module_layers_5_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18451328)))]; + tensor linear_21_cast_fp16 = linear(bias = module_layers_5_self_attn_out_proj_bias_to_fp16, weight = module_layers_5_self_attn_out_proj_weight_to_fp16_quantized, x = context_23_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = x_13_cast_fp16, y = linear_21_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm1_weight_to_fp16 = const()[name = string("module_layers_5_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18452672)))]; + tensor module_layers_5_norm1_bias_to_fp16 = const()[name = string("module_layers_5_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18454016)))]; + tensor input_55_cast_fp16 = layer_norm(axes = input_55_axes_0, beta = module_layers_5_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm1_weight_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_layers_5_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18455360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19274624))))[name = string("module_layers_5_linear1_weight_to_fp16_quantized")]; + tensor module_layers_5_linear1_bias_to_fp16 = const()[name = string("module_layers_5_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19277248)))]; + tensor linear_22_cast_fp16 = linear(bias = module_layers_5_linear1_bias_to_fp16, weight = module_layers_5_linear1_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor input_59_cast_fp16 = relu(x = linear_22_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_5_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19279872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20099136))))[name = string("module_layers_5_linear2_weight_to_fp16_quantized")]; + tensor module_layers_5_linear2_bias_to_fp16 = const()[name = string("module_layers_5_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20100480)))]; + tensor linear_23_cast_fp16 = linear(bias = module_layers_5_linear2_bias_to_fp16, weight = module_layers_5_linear2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_55_cast_fp16, y = linear_23_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm2_weight_to_fp16 = const()[name = string("module_layers_5_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20101824)))]; + tensor module_layers_5_norm2_bias_to_fp16 = const()[name = string("module_layers_5_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20103168)))]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, beta = module_layers_5_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm2_weight_to_fp16, x = input_61_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor module_layers_6_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20104512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21333376))))[name = string("module_layers_6_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21337280)))]; + tensor linear_24_cast_fp16 = linear(bias = module_layers_6_self_attn_in_proj_bias_to_fp16, weight = module_layers_6_self_attn_in_proj_weight_to_fp16_quantized, x = x_15_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_610_split_sizes_0 = const()[name = string("op_610_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_610_axis_0 = const()[name = string("op_610_axis_0"), val = int32(-1)]; + tensor var_610_cast_fp16_0, tensor var_610_cast_fp16_1, tensor var_610_cast_fp16_2 = split(axis = var_610_axis_0, split_sizes = var_610_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_610_cast_fp16")]; + tensor var_613 = const()[name = string("op_613"), val = tensor([1, 512, 4, 160])]; + tensor var_614_cast_fp16 = reshape(shape = var_613, x = var_610_cast_fp16_0)[name = string("op_614_cast_fp16")]; + tensor var_616 = const()[name = string("op_616"), val = tensor([1, 512, 4, 160])]; + tensor var_617_cast_fp16 = reshape(shape = var_616, x = var_610_cast_fp16_1)[name = string("op_617_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([1, 512, 4, 160])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = var_610_cast_fp16_2)[name = string("op_620_cast_fp16")]; + tensor v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_623_transpose_x_0 = const()[name = string("op_623_transpose_x_0"), val = bool(false)]; + bool var_623_transpose_y_0 = const()[name = string("op_623_transpose_y_0"), val = bool(false)]; + tensor transpose_44_perm_0 = const()[name = string("transpose_44_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_45_perm_0 = const()[name = string("transpose_45_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_45 = transpose(perm = transpose_45_perm_0, x = var_617_cast_fp16)[name = string("transpose_54")]; + tensor transpose_44 = transpose(perm = transpose_44_perm_0, x = var_614_cast_fp16)[name = string("transpose_55")]; + tensor var_623_cast_fp16 = matmul(transpose_x = var_623_transpose_x_0, transpose_y = var_623_transpose_y_0, x = transpose_44, y = transpose_45)[name = string("op_623_cast_fp16")]; + fp16 var_624_to_fp16 = const()[name = string("op_624_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_13_cast_fp16 = mul(x = var_623_cast_fp16, y = var_624_to_fp16)[name = string("scores_13_cast_fp16")]; + tensor weights_37_cast_fp16 = softmax(axis = var_24, x = scores_13_cast_fp16)[name = string("weights_37_cast_fp16")]; + tensor weights_39_cast_fp16 = mul(x = weights_37_cast_fp16, y = var_313_cast_fp16)[name = string("weights_39_cast_fp16")]; + tensor var_635_axes_0 = const()[name = string("op_635_axes_0"), val = tensor([-1])]; + bool var_635_keep_dims_0 = const()[name = string("op_635_keep_dims_0"), val = bool(true)]; + tensor var_635_cast_fp16 = reduce_sum(axes = var_635_axes_0, keep_dims = var_635_keep_dims_0, x = weights_39_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_cast_fp16 = maximum(x = var_635_cast_fp16, y = var_11_to_fp16)[name = string("op_636_cast_fp16")]; + tensor weights_41_cast_fp16 = real_div(x = weights_39_cast_fp16, y = var_636_cast_fp16)[name = string("weights_41_cast_fp16")]; + bool context_25_transpose_x_0 = const()[name = string("context_25_transpose_x_0"), val = bool(false)]; + bool context_25_transpose_y_0 = const()[name = string("context_25_transpose_y_0"), val = bool(false)]; + tensor v_27_cast_fp16 = transpose(perm = v_27_perm_0, x = var_620_cast_fp16)[name = string("transpose_53")]; + tensor context_25_cast_fp16 = matmul(transpose_x = context_25_transpose_x_0, transpose_y = context_25_transpose_y_0, x = weights_41_cast_fp16, y = v_27_cast_fp16)[name = string("context_25_cast_fp16")]; + tensor var_639_perm_0 = const()[name = string("op_639_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_640 = const()[name = string("op_640"), val = tensor([1, 512, 640])]; + tensor var_639_cast_fp16 = transpose(perm = var_639_perm_0, x = context_25_cast_fp16)[name = string("transpose_52")]; + tensor context_27_cast_fp16 = reshape(shape = var_640, x = var_639_cast_fp16)[name = string("context_27_cast_fp16")]; + tensor module_layers_6_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21341184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750848))))[name = string("module_layers_6_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21752192)))]; + tensor linear_25_cast_fp16 = linear(bias = module_layers_6_self_attn_out_proj_bias_to_fp16, weight = module_layers_6_self_attn_out_proj_weight_to_fp16_quantized, x = context_27_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = x_15_cast_fp16, y = linear_25_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm1_weight_to_fp16 = const()[name = string("module_layers_6_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21753536)))]; + tensor module_layers_6_norm1_bias_to_fp16 = const()[name = string("module_layers_6_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21754880)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = module_layers_6_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm1_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_layers_6_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21756224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22575488))))[name = string("module_layers_6_linear1_weight_to_fp16_quantized")]; + tensor module_layers_6_linear1_bias_to_fp16 = const()[name = string("module_layers_6_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22578112)))]; + tensor linear_26_cast_fp16 = linear(bias = module_layers_6_linear1_bias_to_fp16, weight = module_layers_6_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_69_cast_fp16 = relu(x = linear_26_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_layers_6_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22580736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23400000))))[name = string("module_layers_6_linear2_weight_to_fp16_quantized")]; + tensor module_layers_6_linear2_bias_to_fp16 = const()[name = string("module_layers_6_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23401344)))]; + tensor linear_27_cast_fp16 = linear(bias = module_layers_6_linear2_bias_to_fp16, weight = module_layers_6_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = input_65_cast_fp16, y = linear_27_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm2_weight_to_fp16 = const()[name = string("module_layers_6_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23402688)))]; + tensor module_layers_6_norm2_bias_to_fp16 = const()[name = string("module_layers_6_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23404032)))]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, beta = module_layers_6_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm2_weight_to_fp16, x = input_71_cast_fp16)[name = string("x_cast_fp16")]; + tensor module_layers_7_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23405376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24634240))))[name = string("module_layers_7_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24638144)))]; + tensor linear_28_cast_fp16 = linear(bias = module_layers_7_self_attn_in_proj_bias_to_fp16, weight = module_layers_7_self_attn_in_proj_weight_to_fp16_quantized, x = x_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_663_split_sizes_0 = const()[name = string("op_663_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_663_axis_0 = const()[name = string("op_663_axis_0"), val = int32(-1)]; + tensor var_663_cast_fp16_0, tensor var_663_cast_fp16_1, tensor var_663_cast_fp16_2 = split(axis = var_663_axis_0, split_sizes = var_663_split_sizes_0, x = linear_28_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor var_666 = const()[name = string("op_666"), val = tensor([1, 512, 4, 160])]; + tensor var_667_cast_fp16 = reshape(shape = var_666, x = var_663_cast_fp16_0)[name = string("op_667_cast_fp16")]; + tensor var_669 = const()[name = string("op_669"), val = tensor([1, 512, 4, 160])]; + tensor var_670_cast_fp16 = reshape(shape = var_669, x = var_663_cast_fp16_1)[name = string("op_670_cast_fp16")]; + tensor var_672 = const()[name = string("op_672"), val = tensor([1, 512, 4, 160])]; + tensor var_673_cast_fp16 = reshape(shape = var_672, x = var_663_cast_fp16_2)[name = string("op_673_cast_fp16")]; + tensor v_perm_0 = const()[name = string("v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_676_transpose_x_0 = const()[name = string("op_676_transpose_x_0"), val = bool(false)]; + bool var_676_transpose_y_0 = const()[name = string("op_676_transpose_y_0"), val = bool(false)]; + tensor transpose_46_perm_0 = const()[name = string("transpose_46_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_47_perm_0 = const()[name = string("transpose_47_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_47 = transpose(perm = transpose_47_perm_0, x = var_670_cast_fp16)[name = string("transpose_50")]; + tensor transpose_46 = transpose(perm = transpose_46_perm_0, x = var_667_cast_fp16)[name = string("transpose_51")]; + tensor var_676_cast_fp16 = matmul(transpose_x = var_676_transpose_x_0, transpose_y = var_676_transpose_y_0, x = transpose_46, y = transpose_47)[name = string("op_676_cast_fp16")]; + fp16 var_677_to_fp16 = const()[name = string("op_677_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_cast_fp16 = mul(x = var_676_cast_fp16, y = var_677_to_fp16)[name = string("scores_cast_fp16")]; + tensor weights_43_cast_fp16 = softmax(axis = var_24, x = scores_cast_fp16)[name = string("weights_43_cast_fp16")]; + tensor weights_45_cast_fp16 = mul(x = weights_43_cast_fp16, y = var_313_cast_fp16)[name = string("weights_45_cast_fp16")]; + tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([-1])]; + bool var_688_keep_dims_0 = const()[name = string("op_688_keep_dims_0"), val = bool(true)]; + tensor var_688_cast_fp16 = reduce_sum(axes = var_688_axes_0, keep_dims = var_688_keep_dims_0, x = weights_45_cast_fp16)[name = string("op_688_cast_fp16")]; + tensor var_689_cast_fp16 = maximum(x = var_688_cast_fp16, y = var_11_to_fp16)[name = string("op_689_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_45_cast_fp16, y = var_689_cast_fp16)[name = string("weights_cast_fp16")]; + bool context_29_transpose_x_0 = const()[name = string("context_29_transpose_x_0"), val = bool(false)]; + bool context_29_transpose_y_0 = const()[name = string("context_29_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = v_perm_0, x = var_673_cast_fp16)[name = string("transpose_49")]; + tensor context_29_cast_fp16 = matmul(transpose_x = context_29_transpose_x_0, transpose_y = context_29_transpose_y_0, x = weights_cast_fp16, y = v_cast_fp16)[name = string("context_29_cast_fp16")]; + tensor var_692_perm_0 = const()[name = string("op_692_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_693 = const()[name = string("op_693"), val = tensor([1, 512, 640])]; + tensor var_692_cast_fp16 = transpose(perm = var_692_perm_0, x = context_29_cast_fp16)[name = string("transpose_48")]; + tensor context_cast_fp16 = reshape(shape = var_693, x = var_692_cast_fp16)[name = string("context_cast_fp16")]; + tensor module_layers_7_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24642048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25051712))))[name = string("module_layers_7_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25053056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_layers_7_self_attn_out_proj_bias_to_fp16, weight = module_layers_7_self_attn_out_proj_weight_to_fp16_quantized, x = context_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor input_73_cast_fp16 = add(x = x_cast_fp16, y = linear_29_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm1_weight_to_fp16 = const()[name = string("module_layers_7_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25054400)))]; + tensor module_layers_7_norm1_bias_to_fp16 = const()[name = string("module_layers_7_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25055744)))]; + tensor input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = module_layers_7_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm1_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_layers_7_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25057088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25876352))))[name = string("module_layers_7_linear1_weight_to_fp16_quantized")]; + tensor module_layers_7_linear1_bias_to_fp16 = const()[name = string("module_layers_7_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25878976)))]; + tensor linear_30_cast_fp16 = linear(bias = module_layers_7_linear1_bias_to_fp16, weight = module_layers_7_linear1_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor input_79_cast_fp16 = relu(x = linear_30_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_layers_7_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25881600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26700864))))[name = string("module_layers_7_linear2_weight_to_fp16_quantized")]; + tensor module_layers_7_linear2_bias_to_fp16 = const()[name = string("module_layers_7_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26702208)))]; + tensor linear_31_cast_fp16 = linear(bias = module_layers_7_linear2_bias_to_fp16, weight = module_layers_7_linear2_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor input_81_cast_fp16 = add(x = input_75_cast_fp16, y = linear_31_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor input_83_axes_0 = const()[name = string("input_83_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm2_weight_to_fp16 = const()[name = string("module_layers_7_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26703552)))]; + tensor module_layers_7_norm2_bias_to_fp16 = const()[name = string("module_layers_7_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26704896)))]; + tensor input_83_cast_fp16 = layer_norm(axes = input_83_axes_0, beta = module_layers_7_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm2_weight_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([-1])]; + tensor module_encoder_norm_weight_to_fp16 = const()[name = string("module_encoder_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26706240)))]; + tensor module_encoder_norm_bias_to_fp16 = const()[name = string("module_encoder_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26707584)))]; + tensor input_cast_fp16 = layer_norm(axes = input_axes_0, beta = module_encoder_norm_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_encoder_norm_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_cast_fp16")]; + tensor module_fc_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26708928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770432))))[name = string("module_fc_out_weight_to_fp16_quantized")]; + tensor module_fc_out_bias_to_fp16 = const()[name = string("module_fc_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770688)))]; + tensor linear_32_cast_fp16 = linear(bias = module_fc_out_bias_to_fp16, weight = module_fc_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor padded_axes_0 = const()[name = string("padded_axes_0"), val = tensor([-1])]; + tensor padded = expand_dims(axes = padded_axes_0, x = src_pad_mask)[name = string("padded")]; + tensor var_723_begin_0 = const()[name = string("op_723_begin_0"), val = tensor([0, 0, 0])]; + tensor var_723_end_0 = const()[name = string("op_723_end_0"), val = tensor([1, 512, 1])]; + tensor var_723_end_mask_0 = const()[name = string("op_723_end_mask_0"), val = tensor([true, true, false])]; + tensor var_723_cast_fp16 = slice_by_index(begin = var_723_begin_0, end = var_723_end_0, end_mask = var_723_end_mask_0, x = linear_32_cast_fp16)[name = string("op_723_cast_fp16")]; + tensor var_722_to_fp16 = const()[name = string("op_722_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27345664)))]; + tensor blank_logits_cast_fp16 = select(a = var_722_to_fp16, b = var_723_cast_fp16, cond = padded)[name = string("blank_logits_cast_fp16")]; + tensor var_725_begin_0 = const()[name = string("op_725_begin_0"), val = tensor([0, 0, 1])]; + tensor var_725_end_0 = const()[name = string("op_725_end_0"), val = tensor([1, 512, 96])]; + tensor var_725_end_mask_0 = const()[name = string("op_725_end_mask_0"), val = tensor([true, true, true])]; + tensor var_725_cast_fp16 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = linear_32_cast_fp16)[name = string("op_725_cast_fp16")]; + fp16 var_726_value_0_to_fp16 = const()[name = string("op_726_value_0_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor var_726_cast_fp16 = fill_like(ref_tensor = var_725_cast_fp16, value = var_726_value_0_to_fp16)[name = string("op_726_cast_fp16")]; + tensor padded_after_broadcast_reps_0 = const()[name = string("padded_after_broadcast_reps_0"), val = tensor([1, 1, 95])]; + tensor padded_after_broadcast = tile(reps = padded_after_broadcast_reps_0, x = padded)[name = string("padded_after_broadcast")]; + tensor nonblank_logits_cast_fp16 = select(a = var_726_cast_fp16, b = var_725_cast_fp16, cond = padded_after_broadcast)[name = string("nonblank_logits_cast_fp16")]; + bool var_730_interleave_0 = const()[name = string("op_730_interleave_0"), val = bool(false)]; + tensor var_730_cast_fp16 = concat(axis = var_24, interleave = var_730_interleave_0, values = (blank_logits_cast_fp16, nonblank_logits_cast_fp16))[name = string("op_730_cast_fp16")]; + string var_730_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_730_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_730_cast_fp16_to_fp32_dtype_0, x = var_730_cast_fp16)[name = string("cast_1")]; + } -> (logits); + func g2p_128(tensor text) { + int32 var_24 = const()[name = string("op_24"), val = int32(-1)]; + int32 var_26 = const()[name = string("op_26"), val = int32(0)]; + tensor src_pad_mask = equal(x = text, y = var_26)[name = string("src_pad_mask")]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor module_embedding_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257408))))[name = string("module_embedding_weight_to_fp16_quantized")]; + string text_to_int16_dtype_0 = const()[name = string("text_to_int16_dtype_0"), val = string("int16")]; + string cast_49_dtype_0 = const()[name = string("cast_49_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_to_int16 = cast(dtype = text_to_int16_dtype_0, x = text)[name = string("cast_6")]; + tensor cast_49 = cast(dtype = cast_49_dtype_0, x = text_to_int16)[name = string("cast_5")]; + tensor greater_equal_0 = greater_equal(x = cast_49, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(402)]; + tensor add_0 = add(x = cast_49, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_49, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_4")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_3")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(402)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 x_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_1, validate_indices = x_1_validate_indices_0, x = module_embedding_weight_to_fp16_quantized)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor op_286_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26771136))), scale = tensor([[[0x1.724p-7]]]))[name = string("op_286_to_fp16_quantized")]; + tensor input_1_cast_fp16 = add(x = x_1_cast_fp16_cast_uint16_cast_uint16, y = op_286_to_fp16_quantized)[name = string("input_1_cast_fp16")]; + tensor module_layers_0_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1528192))))[name = string("module_layers_0_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1532096)))]; + tensor linear_0_cast_fp16 = linear(bias = module_layers_0_self_attn_in_proj_bias_to_fp16, weight = module_layers_0_self_attn_in_proj_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_292_split_sizes_0 = const()[name = string("op_292_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_292_axis_0 = const()[name = string("op_292_axis_0"), val = int32(-1)]; + tensor var_292_cast_fp16_0, tensor var_292_cast_fp16_1, tensor var_292_cast_fp16_2 = split(axis = var_292_axis_0, split_sizes = var_292_split_sizes_0, x = linear_0_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor var_295 = const()[name = string("op_295"), val = tensor([1, 128, 4, 160])]; + tensor var_296_cast_fp16 = reshape(shape = var_295, x = var_292_cast_fp16_0)[name = string("op_296_cast_fp16")]; + tensor var_298 = const()[name = string("op_298"), val = tensor([1, 128, 4, 160])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = var_292_cast_fp16_1)[name = string("op_299_cast_fp16")]; + tensor var_301 = const()[name = string("op_301"), val = tensor([1, 128, 4, 160])]; + tensor var_302_cast_fp16 = reshape(shape = var_301, x = var_292_cast_fp16_2)[name = string("op_302_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_305_transpose_x_0 = const()[name = string("op_305_transpose_x_0"), val = bool(false)]; + bool var_305_transpose_y_0 = const()[name = string("op_305_transpose_y_0"), val = bool(false)]; + tensor transpose_32_perm_0 = const()[name = string("transpose_32_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_33_perm_0 = const()[name = string("transpose_33_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_33 = transpose(perm = transpose_33_perm_0, x = var_299_cast_fp16)[name = string("transpose_78")]; + tensor transpose_32 = transpose(perm = transpose_32_perm_0, x = var_296_cast_fp16)[name = string("transpose_79")]; + tensor var_305_cast_fp16 = matmul(transpose_x = var_305_transpose_x_0, transpose_y = var_305_transpose_y_0, x = transpose_32, y = transpose_33)[name = string("op_305_cast_fp16")]; + fp16 var_306_to_fp16 = const()[name = string("op_306_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_1_cast_fp16 = mul(x = var_305_cast_fp16, y = var_306_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor weights_1_cast_fp16 = softmax(axis = var_24, x = scores_1_cast_fp16)[name = string("weights_1_cast_fp16")]; + fp16 var_13_to_fp16 = const()[name = string("op_13_to_fp16"), val = fp16(0x1p+0)]; + string var_309_to_fp16_dtype_0 = const()[name = string("op_309_to_fp16_dtype_0"), val = string("fp16")]; + tensor src_pad_mask_to_fp16 = cast(dtype = var_309_to_fp16_dtype_0, x = src_pad_mask)[name = string("cast_2")]; + tensor var_310_cast_fp16 = sub(x = var_13_to_fp16, y = src_pad_mask_to_fp16)[name = string("op_310_cast_fp16")]; + tensor var_312_axes_0 = const()[name = string("op_312_axes_0"), val = tensor([1])]; + tensor var_312_cast_fp16 = expand_dims(axes = var_312_axes_0, x = var_310_cast_fp16)[name = string("op_312_cast_fp16")]; + tensor var_313_axes_0 = const()[name = string("op_313_axes_0"), val = tensor([2])]; + tensor var_313_cast_fp16 = expand_dims(axes = var_313_axes_0, x = var_312_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor weights_3_cast_fp16 = mul(x = weights_1_cast_fp16, y = var_313_cast_fp16)[name = string("weights_3_cast_fp16")]; + tensor var_317_axes_0 = const()[name = string("op_317_axes_0"), val = tensor([-1])]; + bool var_317_keep_dims_0 = const()[name = string("op_317_keep_dims_0"), val = bool(true)]; + tensor var_317_cast_fp16 = reduce_sum(axes = var_317_axes_0, keep_dims = var_317_keep_dims_0, x = weights_3_cast_fp16)[name = string("op_317_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_318_cast_fp16 = maximum(x = var_317_cast_fp16, y = var_11_to_fp16)[name = string("op_318_cast_fp16")]; + tensor weights_5_cast_fp16 = real_div(x = weights_3_cast_fp16, y = var_318_cast_fp16)[name = string("weights_5_cast_fp16")]; + bool context_1_transpose_x_0 = const()[name = string("context_1_transpose_x_0"), val = bool(false)]; + bool context_1_transpose_y_0 = const()[name = string("context_1_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_302_cast_fp16)[name = string("transpose_77")]; + tensor context_1_cast_fp16 = matmul(transpose_x = context_1_transpose_x_0, transpose_y = context_1_transpose_y_0, x = weights_5_cast_fp16, y = v_3_cast_fp16)[name = string("context_1_cast_fp16")]; + tensor var_321_perm_0 = const()[name = string("op_321_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_322 = const()[name = string("op_322"), val = tensor([1, 128, 640])]; + tensor var_321_cast_fp16 = transpose(perm = var_321_perm_0, x = context_1_cast_fp16)[name = string("transpose_76")]; + tensor context_3_cast_fp16 = reshape(shape = var_322, x = var_321_cast_fp16)[name = string("context_3_cast_fp16")]; + tensor module_layers_0_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1536000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1945664))))[name = string("module_layers_0_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1947008)))]; + tensor linear_1_cast_fp16 = linear(bias = module_layers_0_self_attn_out_proj_bias_to_fp16, weight = module_layers_0_self_attn_out_proj_weight_to_fp16_quantized, x = context_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = input_1_cast_fp16, y = linear_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm1_weight_to_fp16 = const()[name = string("module_layers_0_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1948352)))]; + tensor module_layers_0_norm1_bias_to_fp16 = const()[name = string("module_layers_0_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1949696)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_5_cast_fp16 = layer_norm(axes = input_5_axes_0, beta = module_layers_0_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm1_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor module_layers_0_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1951040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2770304))))[name = string("module_layers_0_linear1_weight_to_fp16_quantized")]; + tensor module_layers_0_linear1_bias_to_fp16 = const()[name = string("module_layers_0_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2772928)))]; + tensor linear_2_cast_fp16 = linear(bias = module_layers_0_linear1_bias_to_fp16, weight = module_layers_0_linear1_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor input_9_cast_fp16 = relu(x = linear_2_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_layers_0_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2775552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3594816))))[name = string("module_layers_0_linear2_weight_to_fp16_quantized")]; + tensor module_layers_0_linear2_bias_to_fp16 = const()[name = string("module_layers_0_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3596160)))]; + tensor linear_3_cast_fp16 = linear(bias = module_layers_0_linear2_bias_to_fp16, weight = module_layers_0_linear2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = input_5_cast_fp16, y = linear_3_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor x_5_axes_0 = const()[name = string("x_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm2_weight_to_fp16 = const()[name = string("module_layers_0_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3597504)))]; + tensor module_layers_0_norm2_bias_to_fp16 = const()[name = string("module_layers_0_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3598848)))]; + tensor x_5_cast_fp16 = layer_norm(axes = x_5_axes_0, beta = module_layers_0_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm2_weight_to_fp16, x = input_11_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_layers_1_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3600192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4829056))))[name = string("module_layers_1_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4832960)))]; + tensor linear_4_cast_fp16 = linear(bias = module_layers_1_self_attn_in_proj_bias_to_fp16, weight = module_layers_1_self_attn_in_proj_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_345_split_sizes_0 = const()[name = string("op_345_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_345_axis_0 = const()[name = string("op_345_axis_0"), val = int32(-1)]; + tensor var_345_cast_fp16_0, tensor var_345_cast_fp16_1, tensor var_345_cast_fp16_2 = split(axis = var_345_axis_0, split_sizes = var_345_split_sizes_0, x = linear_4_cast_fp16)[name = string("op_345_cast_fp16")]; + tensor var_348 = const()[name = string("op_348"), val = tensor([1, 128, 4, 160])]; + tensor var_349_cast_fp16 = reshape(shape = var_348, x = var_345_cast_fp16_0)[name = string("op_349_cast_fp16")]; + tensor var_351 = const()[name = string("op_351"), val = tensor([1, 128, 4, 160])]; + tensor var_352_cast_fp16 = reshape(shape = var_351, x = var_345_cast_fp16_1)[name = string("op_352_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 128, 4, 160])]; + tensor var_355_cast_fp16 = reshape(shape = var_354, x = var_345_cast_fp16_2)[name = string("op_355_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_358_transpose_x_0 = const()[name = string("op_358_transpose_x_0"), val = bool(false)]; + bool var_358_transpose_y_0 = const()[name = string("op_358_transpose_y_0"), val = bool(false)]; + tensor transpose_34_perm_0 = const()[name = string("transpose_34_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_35_perm_0 = const()[name = string("transpose_35_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_35 = transpose(perm = transpose_35_perm_0, x = var_352_cast_fp16)[name = string("transpose_74")]; + tensor transpose_34 = transpose(perm = transpose_34_perm_0, x = var_349_cast_fp16)[name = string("transpose_75")]; + tensor var_358_cast_fp16 = matmul(transpose_x = var_358_transpose_x_0, transpose_y = var_358_transpose_y_0, x = transpose_34, y = transpose_35)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_3_cast_fp16 = mul(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("scores_3_cast_fp16")]; + tensor weights_7_cast_fp16 = softmax(axis = var_24, x = scores_3_cast_fp16)[name = string("weights_7_cast_fp16")]; + tensor weights_9_cast_fp16 = mul(x = weights_7_cast_fp16, y = var_313_cast_fp16)[name = string("weights_9_cast_fp16")]; + tensor var_370_axes_0 = const()[name = string("op_370_axes_0"), val = tensor([-1])]; + bool var_370_keep_dims_0 = const()[name = string("op_370_keep_dims_0"), val = bool(true)]; + tensor var_370_cast_fp16 = reduce_sum(axes = var_370_axes_0, keep_dims = var_370_keep_dims_0, x = weights_9_cast_fp16)[name = string("op_370_cast_fp16")]; + tensor var_371_cast_fp16 = maximum(x = var_370_cast_fp16, y = var_11_to_fp16)[name = string("op_371_cast_fp16")]; + tensor weights_11_cast_fp16 = real_div(x = weights_9_cast_fp16, y = var_371_cast_fp16)[name = string("weights_11_cast_fp16")]; + bool context_5_transpose_x_0 = const()[name = string("context_5_transpose_x_0"), val = bool(false)]; + bool context_5_transpose_y_0 = const()[name = string("context_5_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_355_cast_fp16)[name = string("transpose_73")]; + tensor context_5_cast_fp16 = matmul(transpose_x = context_5_transpose_x_0, transpose_y = context_5_transpose_y_0, x = weights_11_cast_fp16, y = v_7_cast_fp16)[name = string("context_5_cast_fp16")]; + tensor var_374_perm_0 = const()[name = string("op_374_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_375 = const()[name = string("op_375"), val = tensor([1, 128, 640])]; + tensor var_374_cast_fp16 = transpose(perm = var_374_perm_0, x = context_5_cast_fp16)[name = string("transpose_72")]; + tensor context_7_cast_fp16 = reshape(shape = var_375, x = var_374_cast_fp16)[name = string("context_7_cast_fp16")]; + tensor module_layers_1_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4836864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5246528))))[name = string("module_layers_1_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5247872)))]; + tensor linear_5_cast_fp16 = linear(bias = module_layers_1_self_attn_out_proj_bias_to_fp16, weight = module_layers_1_self_attn_out_proj_weight_to_fp16_quantized, x = context_7_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = x_5_cast_fp16, y = linear_5_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm1_weight_to_fp16 = const()[name = string("module_layers_1_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5249216)))]; + tensor module_layers_1_norm1_bias_to_fp16 = const()[name = string("module_layers_1_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5250560)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_layers_1_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm1_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_layers_1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5251904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6071168))))[name = string("module_layers_1_linear1_weight_to_fp16_quantized")]; + tensor module_layers_1_linear1_bias_to_fp16 = const()[name = string("module_layers_1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6073792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_layers_1_linear1_bias_to_fp16, weight = module_layers_1_linear1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor input_19_cast_fp16 = relu(x = linear_6_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_layers_1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6076416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6895680))))[name = string("module_layers_1_linear2_weight_to_fp16_quantized")]; + tensor module_layers_1_linear2_bias_to_fp16 = const()[name = string("module_layers_1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6897024)))]; + tensor linear_7_cast_fp16 = linear(bias = module_layers_1_linear2_bias_to_fp16, weight = module_layers_1_linear2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = input_15_cast_fp16, y = linear_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm2_weight_to_fp16 = const()[name = string("module_layers_1_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6898368)))]; + tensor module_layers_1_norm2_bias_to_fp16 = const()[name = string("module_layers_1_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6899712)))]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = module_layers_1_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm2_weight_to_fp16, x = input_21_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor module_layers_2_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8129920))))[name = string("module_layers_2_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8133824)))]; + tensor linear_8_cast_fp16 = linear(bias = module_layers_2_self_attn_in_proj_bias_to_fp16, weight = module_layers_2_self_attn_in_proj_weight_to_fp16_quantized, x = x_7_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_398_split_sizes_0 = const()[name = string("op_398_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_398_axis_0 = const()[name = string("op_398_axis_0"), val = int32(-1)]; + tensor var_398_cast_fp16_0, tensor var_398_cast_fp16_1, tensor var_398_cast_fp16_2 = split(axis = var_398_axis_0, split_sizes = var_398_split_sizes_0, x = linear_8_cast_fp16)[name = string("op_398_cast_fp16")]; + tensor var_401 = const()[name = string("op_401"), val = tensor([1, 128, 4, 160])]; + tensor var_402_cast_fp16 = reshape(shape = var_401, x = var_398_cast_fp16_0)[name = string("op_402_cast_fp16")]; + tensor var_404 = const()[name = string("op_404"), val = tensor([1, 128, 4, 160])]; + tensor var_405_cast_fp16 = reshape(shape = var_404, x = var_398_cast_fp16_1)[name = string("op_405_cast_fp16")]; + tensor var_407 = const()[name = string("op_407"), val = tensor([1, 128, 4, 160])]; + tensor var_408_cast_fp16 = reshape(shape = var_407, x = var_398_cast_fp16_2)[name = string("op_408_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_411_transpose_x_0 = const()[name = string("op_411_transpose_x_0"), val = bool(false)]; + bool var_411_transpose_y_0 = const()[name = string("op_411_transpose_y_0"), val = bool(false)]; + tensor transpose_36_perm_0 = const()[name = string("transpose_36_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_37_perm_0 = const()[name = string("transpose_37_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_37 = transpose(perm = transpose_37_perm_0, x = var_405_cast_fp16)[name = string("transpose_70")]; + tensor transpose_36 = transpose(perm = transpose_36_perm_0, x = var_402_cast_fp16)[name = string("transpose_71")]; + tensor var_411_cast_fp16 = matmul(transpose_x = var_411_transpose_x_0, transpose_y = var_411_transpose_y_0, x = transpose_36, y = transpose_37)[name = string("op_411_cast_fp16")]; + fp16 var_412_to_fp16 = const()[name = string("op_412_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_5_cast_fp16 = mul(x = var_411_cast_fp16, y = var_412_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor weights_13_cast_fp16 = softmax(axis = var_24, x = scores_5_cast_fp16)[name = string("weights_13_cast_fp16")]; + tensor weights_15_cast_fp16 = mul(x = weights_13_cast_fp16, y = var_313_cast_fp16)[name = string("weights_15_cast_fp16")]; + tensor var_423_axes_0 = const()[name = string("op_423_axes_0"), val = tensor([-1])]; + bool var_423_keep_dims_0 = const()[name = string("op_423_keep_dims_0"), val = bool(true)]; + tensor var_423_cast_fp16 = reduce_sum(axes = var_423_axes_0, keep_dims = var_423_keep_dims_0, x = weights_15_cast_fp16)[name = string("op_423_cast_fp16")]; + tensor var_424_cast_fp16 = maximum(x = var_423_cast_fp16, y = var_11_to_fp16)[name = string("op_424_cast_fp16")]; + tensor weights_17_cast_fp16 = real_div(x = weights_15_cast_fp16, y = var_424_cast_fp16)[name = string("weights_17_cast_fp16")]; + bool context_9_transpose_x_0 = const()[name = string("context_9_transpose_x_0"), val = bool(false)]; + bool context_9_transpose_y_0 = const()[name = string("context_9_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_408_cast_fp16)[name = string("transpose_69")]; + tensor context_9_cast_fp16 = matmul(transpose_x = context_9_transpose_x_0, transpose_y = context_9_transpose_y_0, x = weights_17_cast_fp16, y = v_11_cast_fp16)[name = string("context_9_cast_fp16")]; + tensor var_427_perm_0 = const()[name = string("op_427_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_428 = const()[name = string("op_428"), val = tensor([1, 128, 640])]; + tensor var_427_cast_fp16 = transpose(perm = var_427_perm_0, x = context_9_cast_fp16)[name = string("transpose_68")]; + tensor context_11_cast_fp16 = reshape(shape = var_428, x = var_427_cast_fp16)[name = string("context_11_cast_fp16")]; + tensor module_layers_2_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8137728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8547392))))[name = string("module_layers_2_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548736)))]; + tensor linear_9_cast_fp16 = linear(bias = module_layers_2_self_attn_out_proj_bias_to_fp16, weight = module_layers_2_self_attn_out_proj_weight_to_fp16_quantized, x = context_11_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = x_7_cast_fp16, y = linear_9_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor input_25_axes_0 = const()[name = string("input_25_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm1_weight_to_fp16 = const()[name = string("module_layers_2_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8550080)))]; + tensor module_layers_2_norm1_bias_to_fp16 = const()[name = string("module_layers_2_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8551424)))]; + tensor input_25_cast_fp16 = layer_norm(axes = input_25_axes_0, beta = module_layers_2_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm1_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8552768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9372032))))[name = string("module_layers_2_linear1_weight_to_fp16_quantized")]; + tensor module_layers_2_linear1_bias_to_fp16 = const()[name = string("module_layers_2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9374656)))]; + tensor linear_10_cast_fp16 = linear(bias = module_layers_2_linear1_bias_to_fp16, weight = module_layers_2_linear1_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_29_cast_fp16 = relu(x = linear_10_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor module_layers_2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9377280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10196544))))[name = string("module_layers_2_linear2_weight_to_fp16_quantized")]; + tensor module_layers_2_linear2_bias_to_fp16 = const()[name = string("module_layers_2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10197888)))]; + tensor linear_11_cast_fp16 = linear(bias = module_layers_2_linear2_bias_to_fp16, weight = module_layers_2_linear2_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = input_25_cast_fp16, y = linear_11_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm2_weight_to_fp16 = const()[name = string("module_layers_2_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10199232)))]; + tensor module_layers_2_norm2_bias_to_fp16 = const()[name = string("module_layers_2_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10200576)))]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, beta = module_layers_2_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor module_layers_3_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10201920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11430784))))[name = string("module_layers_3_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11434688)))]; + tensor linear_12_cast_fp16 = linear(bias = module_layers_3_self_attn_in_proj_bias_to_fp16, weight = module_layers_3_self_attn_in_proj_weight_to_fp16_quantized, x = x_9_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_451_split_sizes_0 = const()[name = string("op_451_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_451_axis_0 = const()[name = string("op_451_axis_0"), val = int32(-1)]; + tensor var_451_cast_fp16_0, tensor var_451_cast_fp16_1, tensor var_451_cast_fp16_2 = split(axis = var_451_axis_0, split_sizes = var_451_split_sizes_0, x = linear_12_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor var_454 = const()[name = string("op_454"), val = tensor([1, 128, 4, 160])]; + tensor var_455_cast_fp16 = reshape(shape = var_454, x = var_451_cast_fp16_0)[name = string("op_455_cast_fp16")]; + tensor var_457 = const()[name = string("op_457"), val = tensor([1, 128, 4, 160])]; + tensor var_458_cast_fp16 = reshape(shape = var_457, x = var_451_cast_fp16_1)[name = string("op_458_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([1, 128, 4, 160])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = var_451_cast_fp16_2)[name = string("op_461_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_464_transpose_x_0 = const()[name = string("op_464_transpose_x_0"), val = bool(false)]; + bool var_464_transpose_y_0 = const()[name = string("op_464_transpose_y_0"), val = bool(false)]; + tensor transpose_38_perm_0 = const()[name = string("transpose_38_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_39_perm_0 = const()[name = string("transpose_39_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_39 = transpose(perm = transpose_39_perm_0, x = var_458_cast_fp16)[name = string("transpose_66")]; + tensor transpose_38 = transpose(perm = transpose_38_perm_0, x = var_455_cast_fp16)[name = string("transpose_67")]; + tensor var_464_cast_fp16 = matmul(transpose_x = var_464_transpose_x_0, transpose_y = var_464_transpose_y_0, x = transpose_38, y = transpose_39)[name = string("op_464_cast_fp16")]; + fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_7_cast_fp16 = mul(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("scores_7_cast_fp16")]; + tensor weights_19_cast_fp16 = softmax(axis = var_24, x = scores_7_cast_fp16)[name = string("weights_19_cast_fp16")]; + tensor weights_21_cast_fp16 = mul(x = weights_19_cast_fp16, y = var_313_cast_fp16)[name = string("weights_21_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + bool var_476_keep_dims_0 = const()[name = string("op_476_keep_dims_0"), val = bool(true)]; + tensor var_476_cast_fp16 = reduce_sum(axes = var_476_axes_0, keep_dims = var_476_keep_dims_0, x = weights_21_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor var_477_cast_fp16 = maximum(x = var_476_cast_fp16, y = var_11_to_fp16)[name = string("op_477_cast_fp16")]; + tensor weights_23_cast_fp16 = real_div(x = weights_21_cast_fp16, y = var_477_cast_fp16)[name = string("weights_23_cast_fp16")]; + bool context_13_transpose_x_0 = const()[name = string("context_13_transpose_x_0"), val = bool(false)]; + bool context_13_transpose_y_0 = const()[name = string("context_13_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_461_cast_fp16)[name = string("transpose_65")]; + tensor context_13_cast_fp16 = matmul(transpose_x = context_13_transpose_x_0, transpose_y = context_13_transpose_y_0, x = weights_23_cast_fp16, y = v_15_cast_fp16)[name = string("context_13_cast_fp16")]; + tensor var_480_perm_0 = const()[name = string("op_480_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_481 = const()[name = string("op_481"), val = tensor([1, 128, 640])]; + tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = context_13_cast_fp16)[name = string("transpose_64")]; + tensor context_15_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("context_15_cast_fp16")]; + tensor module_layers_3_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11438592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11848256))))[name = string("module_layers_3_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11849600)))]; + tensor linear_13_cast_fp16 = linear(bias = module_layers_3_self_attn_out_proj_bias_to_fp16, weight = module_layers_3_self_attn_out_proj_weight_to_fp16_quantized, x = context_15_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = x_9_cast_fp16, y = linear_13_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_axes_0 = const()[name = string("input_35_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm1_weight_to_fp16 = const()[name = string("module_layers_3_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11850944)))]; + tensor module_layers_3_norm1_bias_to_fp16 = const()[name = string("module_layers_3_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11852288)))]; + tensor input_35_cast_fp16 = layer_norm(axes = input_35_axes_0, beta = module_layers_3_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm1_weight_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_3_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11853632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12672896))))[name = string("module_layers_3_linear1_weight_to_fp16_quantized")]; + tensor module_layers_3_linear1_bias_to_fp16 = const()[name = string("module_layers_3_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12675520)))]; + tensor linear_14_cast_fp16 = linear(bias = module_layers_3_linear1_bias_to_fp16, weight = module_layers_3_linear1_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor input_39_cast_fp16 = relu(x = linear_14_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_layers_3_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12678144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13497408))))[name = string("module_layers_3_linear2_weight_to_fp16_quantized")]; + tensor module_layers_3_linear2_bias_to_fp16 = const()[name = string("module_layers_3_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13498752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_layers_3_linear2_bias_to_fp16, weight = module_layers_3_linear2_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = input_35_cast_fp16, y = linear_15_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm2_weight_to_fp16 = const()[name = string("module_layers_3_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13500096)))]; + tensor module_layers_3_norm2_bias_to_fp16 = const()[name = string("module_layers_3_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13501440)))]; + tensor x_11_cast_fp16 = layer_norm(axes = x_11_axes_0, beta = module_layers_3_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm2_weight_to_fp16, x = input_41_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_layers_4_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13502784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14731648))))[name = string("module_layers_4_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14735552)))]; + tensor linear_16_cast_fp16 = linear(bias = module_layers_4_self_attn_in_proj_bias_to_fp16, weight = module_layers_4_self_attn_in_proj_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_504_split_sizes_0 = const()[name = string("op_504_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_504_axis_0 = const()[name = string("op_504_axis_0"), val = int32(-1)]; + tensor var_504_cast_fp16_0, tensor var_504_cast_fp16_1, tensor var_504_cast_fp16_2 = split(axis = var_504_axis_0, split_sizes = var_504_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_504_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 128, 4, 160])]; + tensor var_508_cast_fp16 = reshape(shape = var_507, x = var_504_cast_fp16_0)[name = string("op_508_cast_fp16")]; + tensor var_510 = const()[name = string("op_510"), val = tensor([1, 128, 4, 160])]; + tensor var_511_cast_fp16 = reshape(shape = var_510, x = var_504_cast_fp16_1)[name = string("op_511_cast_fp16")]; + tensor var_513 = const()[name = string("op_513"), val = tensor([1, 128, 4, 160])]; + tensor var_514_cast_fp16 = reshape(shape = var_513, x = var_504_cast_fp16_2)[name = string("op_514_cast_fp16")]; + tensor v_19_perm_0 = const()[name = string("v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_517_transpose_x_0 = const()[name = string("op_517_transpose_x_0"), val = bool(false)]; + bool var_517_transpose_y_0 = const()[name = string("op_517_transpose_y_0"), val = bool(false)]; + tensor transpose_40_perm_0 = const()[name = string("transpose_40_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_41_perm_0 = const()[name = string("transpose_41_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_41 = transpose(perm = transpose_41_perm_0, x = var_511_cast_fp16)[name = string("transpose_62")]; + tensor transpose_40 = transpose(perm = transpose_40_perm_0, x = var_508_cast_fp16)[name = string("transpose_63")]; + tensor var_517_cast_fp16 = matmul(transpose_x = var_517_transpose_x_0, transpose_y = var_517_transpose_y_0, x = transpose_40, y = transpose_41)[name = string("op_517_cast_fp16")]; + fp16 var_518_to_fp16 = const()[name = string("op_518_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_9_cast_fp16 = mul(x = var_517_cast_fp16, y = var_518_to_fp16)[name = string("scores_9_cast_fp16")]; + tensor weights_25_cast_fp16 = softmax(axis = var_24, x = scores_9_cast_fp16)[name = string("weights_25_cast_fp16")]; + tensor weights_27_cast_fp16 = mul(x = weights_25_cast_fp16, y = var_313_cast_fp16)[name = string("weights_27_cast_fp16")]; + tensor var_529_axes_0 = const()[name = string("op_529_axes_0"), val = tensor([-1])]; + bool var_529_keep_dims_0 = const()[name = string("op_529_keep_dims_0"), val = bool(true)]; + tensor var_529_cast_fp16 = reduce_sum(axes = var_529_axes_0, keep_dims = var_529_keep_dims_0, x = weights_27_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = maximum(x = var_529_cast_fp16, y = var_11_to_fp16)[name = string("op_530_cast_fp16")]; + tensor weights_29_cast_fp16 = real_div(x = weights_27_cast_fp16, y = var_530_cast_fp16)[name = string("weights_29_cast_fp16")]; + bool context_17_transpose_x_0 = const()[name = string("context_17_transpose_x_0"), val = bool(false)]; + bool context_17_transpose_y_0 = const()[name = string("context_17_transpose_y_0"), val = bool(false)]; + tensor v_19_cast_fp16 = transpose(perm = v_19_perm_0, x = var_514_cast_fp16)[name = string("transpose_61")]; + tensor context_17_cast_fp16 = matmul(transpose_x = context_17_transpose_x_0, transpose_y = context_17_transpose_y_0, x = weights_29_cast_fp16, y = v_19_cast_fp16)[name = string("context_17_cast_fp16")]; + tensor var_533_perm_0 = const()[name = string("op_533_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_534 = const()[name = string("op_534"), val = tensor([1, 128, 640])]; + tensor var_533_cast_fp16 = transpose(perm = var_533_perm_0, x = context_17_cast_fp16)[name = string("transpose_60")]; + tensor context_19_cast_fp16 = reshape(shape = var_534, x = var_533_cast_fp16)[name = string("context_19_cast_fp16")]; + tensor module_layers_4_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14739456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15149120))))[name = string("module_layers_4_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15150464)))]; + tensor linear_17_cast_fp16 = linear(bias = module_layers_4_self_attn_out_proj_bias_to_fp16, weight = module_layers_4_self_attn_out_proj_weight_to_fp16_quantized, x = context_19_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = x_11_cast_fp16, y = linear_17_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm1_weight_to_fp16 = const()[name = string("module_layers_4_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15151808)))]; + tensor module_layers_4_norm1_bias_to_fp16 = const()[name = string("module_layers_4_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15153152)))]; + tensor input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = module_layers_4_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_layers_4_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15154496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15973760))))[name = string("module_layers_4_linear1_weight_to_fp16_quantized")]; + tensor module_layers_4_linear1_bias_to_fp16 = const()[name = string("module_layers_4_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15976384)))]; + tensor linear_18_cast_fp16 = linear(bias = module_layers_4_linear1_bias_to_fp16, weight = module_layers_4_linear1_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor input_49_cast_fp16 = relu(x = linear_18_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_layers_4_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15979008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16798272))))[name = string("module_layers_4_linear2_weight_to_fp16_quantized")]; + tensor module_layers_4_linear2_bias_to_fp16 = const()[name = string("module_layers_4_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16799616)))]; + tensor linear_19_cast_fp16 = linear(bias = module_layers_4_linear2_bias_to_fp16, weight = module_layers_4_linear2_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = input_45_cast_fp16, y = linear_19_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm2_weight_to_fp16 = const()[name = string("module_layers_4_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16800960)))]; + tensor module_layers_4_norm2_bias_to_fp16 = const()[name = string("module_layers_4_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16802304)))]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = module_layers_4_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm2_weight_to_fp16, x = input_51_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor module_layers_5_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16803648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18032512))))[name = string("module_layers_5_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18036416)))]; + tensor linear_20_cast_fp16 = linear(bias = module_layers_5_self_attn_in_proj_bias_to_fp16, weight = module_layers_5_self_attn_in_proj_weight_to_fp16_quantized, x = x_13_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_557_split_sizes_0 = const()[name = string("op_557_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_557_axis_0 = const()[name = string("op_557_axis_0"), val = int32(-1)]; + tensor var_557_cast_fp16_0, tensor var_557_cast_fp16_1, tensor var_557_cast_fp16_2 = split(axis = var_557_axis_0, split_sizes = var_557_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_560 = const()[name = string("op_560"), val = tensor([1, 128, 4, 160])]; + tensor var_561_cast_fp16 = reshape(shape = var_560, x = var_557_cast_fp16_0)[name = string("op_561_cast_fp16")]; + tensor var_563 = const()[name = string("op_563"), val = tensor([1, 128, 4, 160])]; + tensor var_564_cast_fp16 = reshape(shape = var_563, x = var_557_cast_fp16_1)[name = string("op_564_cast_fp16")]; + tensor var_566 = const()[name = string("op_566"), val = tensor([1, 128, 4, 160])]; + tensor var_567_cast_fp16 = reshape(shape = var_566, x = var_557_cast_fp16_2)[name = string("op_567_cast_fp16")]; + tensor v_23_perm_0 = const()[name = string("v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_570_transpose_x_0 = const()[name = string("op_570_transpose_x_0"), val = bool(false)]; + bool var_570_transpose_y_0 = const()[name = string("op_570_transpose_y_0"), val = bool(false)]; + tensor transpose_42_perm_0 = const()[name = string("transpose_42_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_43_perm_0 = const()[name = string("transpose_43_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_43 = transpose(perm = transpose_43_perm_0, x = var_564_cast_fp16)[name = string("transpose_58")]; + tensor transpose_42 = transpose(perm = transpose_42_perm_0, x = var_561_cast_fp16)[name = string("transpose_59")]; + tensor var_570_cast_fp16 = matmul(transpose_x = var_570_transpose_x_0, transpose_y = var_570_transpose_y_0, x = transpose_42, y = transpose_43)[name = string("op_570_cast_fp16")]; + fp16 var_571_to_fp16 = const()[name = string("op_571_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_11_cast_fp16 = mul(x = var_570_cast_fp16, y = var_571_to_fp16)[name = string("scores_11_cast_fp16")]; + tensor weights_31_cast_fp16 = softmax(axis = var_24, x = scores_11_cast_fp16)[name = string("weights_31_cast_fp16")]; + tensor weights_33_cast_fp16 = mul(x = weights_31_cast_fp16, y = var_313_cast_fp16)[name = string("weights_33_cast_fp16")]; + tensor var_582_axes_0 = const()[name = string("op_582_axes_0"), val = tensor([-1])]; + bool var_582_keep_dims_0 = const()[name = string("op_582_keep_dims_0"), val = bool(true)]; + tensor var_582_cast_fp16 = reduce_sum(axes = var_582_axes_0, keep_dims = var_582_keep_dims_0, x = weights_33_cast_fp16)[name = string("op_582_cast_fp16")]; + tensor var_583_cast_fp16 = maximum(x = var_582_cast_fp16, y = var_11_to_fp16)[name = string("op_583_cast_fp16")]; + tensor weights_35_cast_fp16 = real_div(x = weights_33_cast_fp16, y = var_583_cast_fp16)[name = string("weights_35_cast_fp16")]; + bool context_21_transpose_x_0 = const()[name = string("context_21_transpose_x_0"), val = bool(false)]; + bool context_21_transpose_y_0 = const()[name = string("context_21_transpose_y_0"), val = bool(false)]; + tensor v_23_cast_fp16 = transpose(perm = v_23_perm_0, x = var_567_cast_fp16)[name = string("transpose_57")]; + tensor context_21_cast_fp16 = matmul(transpose_x = context_21_transpose_x_0, transpose_y = context_21_transpose_y_0, x = weights_35_cast_fp16, y = v_23_cast_fp16)[name = string("context_21_cast_fp16")]; + tensor var_586_perm_0 = const()[name = string("op_586_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_587 = const()[name = string("op_587"), val = tensor([1, 128, 640])]; + tensor var_586_cast_fp16 = transpose(perm = var_586_perm_0, x = context_21_cast_fp16)[name = string("transpose_56")]; + tensor context_23_cast_fp16 = reshape(shape = var_587, x = var_586_cast_fp16)[name = string("context_23_cast_fp16")]; + tensor module_layers_5_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18040320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18449984))))[name = string("module_layers_5_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18451328)))]; + tensor linear_21_cast_fp16 = linear(bias = module_layers_5_self_attn_out_proj_bias_to_fp16, weight = module_layers_5_self_attn_out_proj_weight_to_fp16_quantized, x = context_23_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = x_13_cast_fp16, y = linear_21_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm1_weight_to_fp16 = const()[name = string("module_layers_5_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18452672)))]; + tensor module_layers_5_norm1_bias_to_fp16 = const()[name = string("module_layers_5_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18454016)))]; + tensor input_55_cast_fp16 = layer_norm(axes = input_55_axes_0, beta = module_layers_5_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm1_weight_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_layers_5_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18455360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19274624))))[name = string("module_layers_5_linear1_weight_to_fp16_quantized")]; + tensor module_layers_5_linear1_bias_to_fp16 = const()[name = string("module_layers_5_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19277248)))]; + tensor linear_22_cast_fp16 = linear(bias = module_layers_5_linear1_bias_to_fp16, weight = module_layers_5_linear1_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor input_59_cast_fp16 = relu(x = linear_22_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_5_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19279872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20099136))))[name = string("module_layers_5_linear2_weight_to_fp16_quantized")]; + tensor module_layers_5_linear2_bias_to_fp16 = const()[name = string("module_layers_5_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20100480)))]; + tensor linear_23_cast_fp16 = linear(bias = module_layers_5_linear2_bias_to_fp16, weight = module_layers_5_linear2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_55_cast_fp16, y = linear_23_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm2_weight_to_fp16 = const()[name = string("module_layers_5_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20101824)))]; + tensor module_layers_5_norm2_bias_to_fp16 = const()[name = string("module_layers_5_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20103168)))]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, beta = module_layers_5_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm2_weight_to_fp16, x = input_61_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor module_layers_6_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20104512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21333376))))[name = string("module_layers_6_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21337280)))]; + tensor linear_24_cast_fp16 = linear(bias = module_layers_6_self_attn_in_proj_bias_to_fp16, weight = module_layers_6_self_attn_in_proj_weight_to_fp16_quantized, x = x_15_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_610_split_sizes_0 = const()[name = string("op_610_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_610_axis_0 = const()[name = string("op_610_axis_0"), val = int32(-1)]; + tensor var_610_cast_fp16_0, tensor var_610_cast_fp16_1, tensor var_610_cast_fp16_2 = split(axis = var_610_axis_0, split_sizes = var_610_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_610_cast_fp16")]; + tensor var_613 = const()[name = string("op_613"), val = tensor([1, 128, 4, 160])]; + tensor var_614_cast_fp16 = reshape(shape = var_613, x = var_610_cast_fp16_0)[name = string("op_614_cast_fp16")]; + tensor var_616 = const()[name = string("op_616"), val = tensor([1, 128, 4, 160])]; + tensor var_617_cast_fp16 = reshape(shape = var_616, x = var_610_cast_fp16_1)[name = string("op_617_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([1, 128, 4, 160])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = var_610_cast_fp16_2)[name = string("op_620_cast_fp16")]; + tensor v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_623_transpose_x_0 = const()[name = string("op_623_transpose_x_0"), val = bool(false)]; + bool var_623_transpose_y_0 = const()[name = string("op_623_transpose_y_0"), val = bool(false)]; + tensor transpose_44_perm_0 = const()[name = string("transpose_44_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_45_perm_0 = const()[name = string("transpose_45_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_45 = transpose(perm = transpose_45_perm_0, x = var_617_cast_fp16)[name = string("transpose_54")]; + tensor transpose_44 = transpose(perm = transpose_44_perm_0, x = var_614_cast_fp16)[name = string("transpose_55")]; + tensor var_623_cast_fp16 = matmul(transpose_x = var_623_transpose_x_0, transpose_y = var_623_transpose_y_0, x = transpose_44, y = transpose_45)[name = string("op_623_cast_fp16")]; + fp16 var_624_to_fp16 = const()[name = string("op_624_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_13_cast_fp16 = mul(x = var_623_cast_fp16, y = var_624_to_fp16)[name = string("scores_13_cast_fp16")]; + tensor weights_37_cast_fp16 = softmax(axis = var_24, x = scores_13_cast_fp16)[name = string("weights_37_cast_fp16")]; + tensor weights_39_cast_fp16 = mul(x = weights_37_cast_fp16, y = var_313_cast_fp16)[name = string("weights_39_cast_fp16")]; + tensor var_635_axes_0 = const()[name = string("op_635_axes_0"), val = tensor([-1])]; + bool var_635_keep_dims_0 = const()[name = string("op_635_keep_dims_0"), val = bool(true)]; + tensor var_635_cast_fp16 = reduce_sum(axes = var_635_axes_0, keep_dims = var_635_keep_dims_0, x = weights_39_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_cast_fp16 = maximum(x = var_635_cast_fp16, y = var_11_to_fp16)[name = string("op_636_cast_fp16")]; + tensor weights_41_cast_fp16 = real_div(x = weights_39_cast_fp16, y = var_636_cast_fp16)[name = string("weights_41_cast_fp16")]; + bool context_25_transpose_x_0 = const()[name = string("context_25_transpose_x_0"), val = bool(false)]; + bool context_25_transpose_y_0 = const()[name = string("context_25_transpose_y_0"), val = bool(false)]; + tensor v_27_cast_fp16 = transpose(perm = v_27_perm_0, x = var_620_cast_fp16)[name = string("transpose_53")]; + tensor context_25_cast_fp16 = matmul(transpose_x = context_25_transpose_x_0, transpose_y = context_25_transpose_y_0, x = weights_41_cast_fp16, y = v_27_cast_fp16)[name = string("context_25_cast_fp16")]; + tensor var_639_perm_0 = const()[name = string("op_639_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_640 = const()[name = string("op_640"), val = tensor([1, 128, 640])]; + tensor var_639_cast_fp16 = transpose(perm = var_639_perm_0, x = context_25_cast_fp16)[name = string("transpose_52")]; + tensor context_27_cast_fp16 = reshape(shape = var_640, x = var_639_cast_fp16)[name = string("context_27_cast_fp16")]; + tensor module_layers_6_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21341184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750848))))[name = string("module_layers_6_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21752192)))]; + tensor linear_25_cast_fp16 = linear(bias = module_layers_6_self_attn_out_proj_bias_to_fp16, weight = module_layers_6_self_attn_out_proj_weight_to_fp16_quantized, x = context_27_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = x_15_cast_fp16, y = linear_25_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm1_weight_to_fp16 = const()[name = string("module_layers_6_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21753536)))]; + tensor module_layers_6_norm1_bias_to_fp16 = const()[name = string("module_layers_6_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21754880)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = module_layers_6_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm1_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_layers_6_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21756224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22575488))))[name = string("module_layers_6_linear1_weight_to_fp16_quantized")]; + tensor module_layers_6_linear1_bias_to_fp16 = const()[name = string("module_layers_6_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22578112)))]; + tensor linear_26_cast_fp16 = linear(bias = module_layers_6_linear1_bias_to_fp16, weight = module_layers_6_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_69_cast_fp16 = relu(x = linear_26_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_layers_6_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22580736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23400000))))[name = string("module_layers_6_linear2_weight_to_fp16_quantized")]; + tensor module_layers_6_linear2_bias_to_fp16 = const()[name = string("module_layers_6_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23401344)))]; + tensor linear_27_cast_fp16 = linear(bias = module_layers_6_linear2_bias_to_fp16, weight = module_layers_6_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = input_65_cast_fp16, y = linear_27_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm2_weight_to_fp16 = const()[name = string("module_layers_6_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23402688)))]; + tensor module_layers_6_norm2_bias_to_fp16 = const()[name = string("module_layers_6_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23404032)))]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, beta = module_layers_6_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm2_weight_to_fp16, x = input_71_cast_fp16)[name = string("x_cast_fp16")]; + tensor module_layers_7_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23405376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24634240))))[name = string("module_layers_7_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24638144)))]; + tensor linear_28_cast_fp16 = linear(bias = module_layers_7_self_attn_in_proj_bias_to_fp16, weight = module_layers_7_self_attn_in_proj_weight_to_fp16_quantized, x = x_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_663_split_sizes_0 = const()[name = string("op_663_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_663_axis_0 = const()[name = string("op_663_axis_0"), val = int32(-1)]; + tensor var_663_cast_fp16_0, tensor var_663_cast_fp16_1, tensor var_663_cast_fp16_2 = split(axis = var_663_axis_0, split_sizes = var_663_split_sizes_0, x = linear_28_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor var_666 = const()[name = string("op_666"), val = tensor([1, 128, 4, 160])]; + tensor var_667_cast_fp16 = reshape(shape = var_666, x = var_663_cast_fp16_0)[name = string("op_667_cast_fp16")]; + tensor var_669 = const()[name = string("op_669"), val = tensor([1, 128, 4, 160])]; + tensor var_670_cast_fp16 = reshape(shape = var_669, x = var_663_cast_fp16_1)[name = string("op_670_cast_fp16")]; + tensor var_672 = const()[name = string("op_672"), val = tensor([1, 128, 4, 160])]; + tensor var_673_cast_fp16 = reshape(shape = var_672, x = var_663_cast_fp16_2)[name = string("op_673_cast_fp16")]; + tensor v_perm_0 = const()[name = string("v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_676_transpose_x_0 = const()[name = string("op_676_transpose_x_0"), val = bool(false)]; + bool var_676_transpose_y_0 = const()[name = string("op_676_transpose_y_0"), val = bool(false)]; + tensor transpose_46_perm_0 = const()[name = string("transpose_46_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_47_perm_0 = const()[name = string("transpose_47_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_47 = transpose(perm = transpose_47_perm_0, x = var_670_cast_fp16)[name = string("transpose_50")]; + tensor transpose_46 = transpose(perm = transpose_46_perm_0, x = var_667_cast_fp16)[name = string("transpose_51")]; + tensor var_676_cast_fp16 = matmul(transpose_x = var_676_transpose_x_0, transpose_y = var_676_transpose_y_0, x = transpose_46, y = transpose_47)[name = string("op_676_cast_fp16")]; + fp16 var_677_to_fp16 = const()[name = string("op_677_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_cast_fp16 = mul(x = var_676_cast_fp16, y = var_677_to_fp16)[name = string("scores_cast_fp16")]; + tensor weights_43_cast_fp16 = softmax(axis = var_24, x = scores_cast_fp16)[name = string("weights_43_cast_fp16")]; + tensor weights_45_cast_fp16 = mul(x = weights_43_cast_fp16, y = var_313_cast_fp16)[name = string("weights_45_cast_fp16")]; + tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([-1])]; + bool var_688_keep_dims_0 = const()[name = string("op_688_keep_dims_0"), val = bool(true)]; + tensor var_688_cast_fp16 = reduce_sum(axes = var_688_axes_0, keep_dims = var_688_keep_dims_0, x = weights_45_cast_fp16)[name = string("op_688_cast_fp16")]; + tensor var_689_cast_fp16 = maximum(x = var_688_cast_fp16, y = var_11_to_fp16)[name = string("op_689_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_45_cast_fp16, y = var_689_cast_fp16)[name = string("weights_cast_fp16")]; + bool context_29_transpose_x_0 = const()[name = string("context_29_transpose_x_0"), val = bool(false)]; + bool context_29_transpose_y_0 = const()[name = string("context_29_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = v_perm_0, x = var_673_cast_fp16)[name = string("transpose_49")]; + tensor context_29_cast_fp16 = matmul(transpose_x = context_29_transpose_x_0, transpose_y = context_29_transpose_y_0, x = weights_cast_fp16, y = v_cast_fp16)[name = string("context_29_cast_fp16")]; + tensor var_692_perm_0 = const()[name = string("op_692_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_693 = const()[name = string("op_693"), val = tensor([1, 128, 640])]; + tensor var_692_cast_fp16 = transpose(perm = var_692_perm_0, x = context_29_cast_fp16)[name = string("transpose_48")]; + tensor context_cast_fp16 = reshape(shape = var_693, x = var_692_cast_fp16)[name = string("context_cast_fp16")]; + tensor module_layers_7_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24642048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25051712))))[name = string("module_layers_7_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25053056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_layers_7_self_attn_out_proj_bias_to_fp16, weight = module_layers_7_self_attn_out_proj_weight_to_fp16_quantized, x = context_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor input_73_cast_fp16 = add(x = x_cast_fp16, y = linear_29_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm1_weight_to_fp16 = const()[name = string("module_layers_7_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25054400)))]; + tensor module_layers_7_norm1_bias_to_fp16 = const()[name = string("module_layers_7_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25055744)))]; + tensor input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = module_layers_7_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm1_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_layers_7_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25057088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25876352))))[name = string("module_layers_7_linear1_weight_to_fp16_quantized")]; + tensor module_layers_7_linear1_bias_to_fp16 = const()[name = string("module_layers_7_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25878976)))]; + tensor linear_30_cast_fp16 = linear(bias = module_layers_7_linear1_bias_to_fp16, weight = module_layers_7_linear1_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor input_79_cast_fp16 = relu(x = linear_30_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_layers_7_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25881600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26700864))))[name = string("module_layers_7_linear2_weight_to_fp16_quantized")]; + tensor module_layers_7_linear2_bias_to_fp16 = const()[name = string("module_layers_7_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26702208)))]; + tensor linear_31_cast_fp16 = linear(bias = module_layers_7_linear2_bias_to_fp16, weight = module_layers_7_linear2_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor input_81_cast_fp16 = add(x = input_75_cast_fp16, y = linear_31_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor input_83_axes_0 = const()[name = string("input_83_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm2_weight_to_fp16 = const()[name = string("module_layers_7_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26703552)))]; + tensor module_layers_7_norm2_bias_to_fp16 = const()[name = string("module_layers_7_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26704896)))]; + tensor input_83_cast_fp16 = layer_norm(axes = input_83_axes_0, beta = module_layers_7_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm2_weight_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([-1])]; + tensor module_encoder_norm_weight_to_fp16 = const()[name = string("module_encoder_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26706240)))]; + tensor module_encoder_norm_bias_to_fp16 = const()[name = string("module_encoder_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26707584)))]; + tensor input_cast_fp16 = layer_norm(axes = input_axes_0, beta = module_encoder_norm_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_encoder_norm_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_cast_fp16")]; + tensor module_fc_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26708928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770432))))[name = string("module_fc_out_weight_to_fp16_quantized")]; + tensor module_fc_out_bias_to_fp16 = const()[name = string("module_fc_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770688)))]; + tensor linear_32_cast_fp16 = linear(bias = module_fc_out_bias_to_fp16, weight = module_fc_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor padded_axes_0 = const()[name = string("padded_axes_0"), val = tensor([-1])]; + tensor padded = expand_dims(axes = padded_axes_0, x = src_pad_mask)[name = string("padded")]; + tensor var_723_begin_0 = const()[name = string("op_723_begin_0"), val = tensor([0, 0, 0])]; + tensor var_723_end_0 = const()[name = string("op_723_end_0"), val = tensor([1, 128, 1])]; + tensor var_723_end_mask_0 = const()[name = string("op_723_end_mask_0"), val = tensor([true, true, false])]; + tensor var_723_cast_fp16 = slice_by_index(begin = var_723_begin_0, end = var_723_end_0, end_mask = var_723_end_mask_0, x = linear_32_cast_fp16)[name = string("op_723_cast_fp16")]; + tensor var_722_to_fp16 = const()[name = string("op_722_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26853120)))]; + tensor blank_logits_cast_fp16 = select(a = var_722_to_fp16, b = var_723_cast_fp16, cond = padded)[name = string("blank_logits_cast_fp16")]; + tensor var_725_begin_0 = const()[name = string("op_725_begin_0"), val = tensor([0, 0, 1])]; + tensor var_725_end_0 = const()[name = string("op_725_end_0"), val = tensor([1, 128, 96])]; + tensor var_725_end_mask_0 = const()[name = string("op_725_end_mask_0"), val = tensor([true, true, true])]; + tensor var_725_cast_fp16 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = linear_32_cast_fp16)[name = string("op_725_cast_fp16")]; + fp16 var_726_value_0_to_fp16 = const()[name = string("op_726_value_0_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor var_726_cast_fp16 = fill_like(ref_tensor = var_725_cast_fp16, value = var_726_value_0_to_fp16)[name = string("op_726_cast_fp16")]; + tensor padded_after_broadcast_reps_0 = const()[name = string("padded_after_broadcast_reps_0"), val = tensor([1, 1, 95])]; + tensor padded_after_broadcast = tile(reps = padded_after_broadcast_reps_0, x = padded)[name = string("padded_after_broadcast")]; + tensor nonblank_logits_cast_fp16 = select(a = var_726_cast_fp16, b = var_725_cast_fp16, cond = padded_after_broadcast)[name = string("nonblank_logits_cast_fp16")]; + bool var_730_interleave_0 = const()[name = string("op_730_interleave_0"), val = bool(false)]; + tensor var_730_cast_fp16 = concat(axis = var_24, interleave = var_730_interleave_0, values = (blank_logits_cast_fp16, nonblank_logits_cast_fp16))[name = string("op_730_cast_fp16")]; + string var_730_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_730_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_730_cast_fp16_to_fp32_dtype_0, x = var_730_cast_fp16)[name = string("cast_1")]; + } -> (logits); + func g2p_256(tensor text) { + int32 var_24 = const()[name = string("op_24"), val = int32(-1)]; + int32 var_26 = const()[name = string("op_26"), val = int32(0)]; + tensor src_pad_mask = equal(x = text, y = var_26)[name = string("src_pad_mask")]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor module_embedding_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257408))))[name = string("module_embedding_weight_to_fp16_quantized")]; + string text_to_int16_dtype_0 = const()[name = string("text_to_int16_dtype_0"), val = string("int16")]; + string cast_49_dtype_0 = const()[name = string("cast_49_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_to_int16 = cast(dtype = text_to_int16_dtype_0, x = text)[name = string("cast_6")]; + tensor cast_49 = cast(dtype = cast_49_dtype_0, x = text_to_int16)[name = string("cast_5")]; + tensor greater_equal_0 = greater_equal(x = cast_49, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(402)]; + tensor add_0 = add(x = cast_49, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_49, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_4")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_3")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(402)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 x_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_1, validate_indices = x_1_validate_indices_0, x = module_embedding_weight_to_fp16_quantized)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor op_286_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26853440))), scale = tensor([[[0x1.724p-7]]]))[name = string("op_286_to_fp16_quantized")]; + tensor input_1_cast_fp16 = add(x = x_1_cast_fp16_cast_uint16_cast_uint16, y = op_286_to_fp16_quantized)[name = string("input_1_cast_fp16")]; + tensor module_layers_0_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1528192))))[name = string("module_layers_0_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1532096)))]; + tensor linear_0_cast_fp16 = linear(bias = module_layers_0_self_attn_in_proj_bias_to_fp16, weight = module_layers_0_self_attn_in_proj_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_292_split_sizes_0 = const()[name = string("op_292_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_292_axis_0 = const()[name = string("op_292_axis_0"), val = int32(-1)]; + tensor var_292_cast_fp16_0, tensor var_292_cast_fp16_1, tensor var_292_cast_fp16_2 = split(axis = var_292_axis_0, split_sizes = var_292_split_sizes_0, x = linear_0_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor var_295 = const()[name = string("op_295"), val = tensor([1, 256, 4, 160])]; + tensor var_296_cast_fp16 = reshape(shape = var_295, x = var_292_cast_fp16_0)[name = string("op_296_cast_fp16")]; + tensor var_298 = const()[name = string("op_298"), val = tensor([1, 256, 4, 160])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = var_292_cast_fp16_1)[name = string("op_299_cast_fp16")]; + tensor var_301 = const()[name = string("op_301"), val = tensor([1, 256, 4, 160])]; + tensor var_302_cast_fp16 = reshape(shape = var_301, x = var_292_cast_fp16_2)[name = string("op_302_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_305_transpose_x_0 = const()[name = string("op_305_transpose_x_0"), val = bool(false)]; + bool var_305_transpose_y_0 = const()[name = string("op_305_transpose_y_0"), val = bool(false)]; + tensor transpose_32_perm_0 = const()[name = string("transpose_32_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_33_perm_0 = const()[name = string("transpose_33_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_33 = transpose(perm = transpose_33_perm_0, x = var_299_cast_fp16)[name = string("transpose_78")]; + tensor transpose_32 = transpose(perm = transpose_32_perm_0, x = var_296_cast_fp16)[name = string("transpose_79")]; + tensor var_305_cast_fp16 = matmul(transpose_x = var_305_transpose_x_0, transpose_y = var_305_transpose_y_0, x = transpose_32, y = transpose_33)[name = string("op_305_cast_fp16")]; + fp16 var_306_to_fp16 = const()[name = string("op_306_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_1_cast_fp16 = mul(x = var_305_cast_fp16, y = var_306_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor weights_1_cast_fp16 = softmax(axis = var_24, x = scores_1_cast_fp16)[name = string("weights_1_cast_fp16")]; + fp16 var_13_to_fp16 = const()[name = string("op_13_to_fp16"), val = fp16(0x1p+0)]; + string var_309_to_fp16_dtype_0 = const()[name = string("op_309_to_fp16_dtype_0"), val = string("fp16")]; + tensor src_pad_mask_to_fp16 = cast(dtype = var_309_to_fp16_dtype_0, x = src_pad_mask)[name = string("cast_2")]; + tensor var_310_cast_fp16 = sub(x = var_13_to_fp16, y = src_pad_mask_to_fp16)[name = string("op_310_cast_fp16")]; + tensor var_312_axes_0 = const()[name = string("op_312_axes_0"), val = tensor([1])]; + tensor var_312_cast_fp16 = expand_dims(axes = var_312_axes_0, x = var_310_cast_fp16)[name = string("op_312_cast_fp16")]; + tensor var_313_axes_0 = const()[name = string("op_313_axes_0"), val = tensor([2])]; + tensor var_313_cast_fp16 = expand_dims(axes = var_313_axes_0, x = var_312_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor weights_3_cast_fp16 = mul(x = weights_1_cast_fp16, y = var_313_cast_fp16)[name = string("weights_3_cast_fp16")]; + tensor var_317_axes_0 = const()[name = string("op_317_axes_0"), val = tensor([-1])]; + bool var_317_keep_dims_0 = const()[name = string("op_317_keep_dims_0"), val = bool(true)]; + tensor var_317_cast_fp16 = reduce_sum(axes = var_317_axes_0, keep_dims = var_317_keep_dims_0, x = weights_3_cast_fp16)[name = string("op_317_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_318_cast_fp16 = maximum(x = var_317_cast_fp16, y = var_11_to_fp16)[name = string("op_318_cast_fp16")]; + tensor weights_5_cast_fp16 = real_div(x = weights_3_cast_fp16, y = var_318_cast_fp16)[name = string("weights_5_cast_fp16")]; + bool context_1_transpose_x_0 = const()[name = string("context_1_transpose_x_0"), val = bool(false)]; + bool context_1_transpose_y_0 = const()[name = string("context_1_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_302_cast_fp16)[name = string("transpose_77")]; + tensor context_1_cast_fp16 = matmul(transpose_x = context_1_transpose_x_0, transpose_y = context_1_transpose_y_0, x = weights_5_cast_fp16, y = v_3_cast_fp16)[name = string("context_1_cast_fp16")]; + tensor var_321_perm_0 = const()[name = string("op_321_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_322 = const()[name = string("op_322"), val = tensor([1, 256, 640])]; + tensor var_321_cast_fp16 = transpose(perm = var_321_perm_0, x = context_1_cast_fp16)[name = string("transpose_76")]; + tensor context_3_cast_fp16 = reshape(shape = var_322, x = var_321_cast_fp16)[name = string("context_3_cast_fp16")]; + tensor module_layers_0_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1536000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1945664))))[name = string("module_layers_0_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1947008)))]; + tensor linear_1_cast_fp16 = linear(bias = module_layers_0_self_attn_out_proj_bias_to_fp16, weight = module_layers_0_self_attn_out_proj_weight_to_fp16_quantized, x = context_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = input_1_cast_fp16, y = linear_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm1_weight_to_fp16 = const()[name = string("module_layers_0_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1948352)))]; + tensor module_layers_0_norm1_bias_to_fp16 = const()[name = string("module_layers_0_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1949696)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_5_cast_fp16 = layer_norm(axes = input_5_axes_0, beta = module_layers_0_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm1_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor module_layers_0_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1951040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2770304))))[name = string("module_layers_0_linear1_weight_to_fp16_quantized")]; + tensor module_layers_0_linear1_bias_to_fp16 = const()[name = string("module_layers_0_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2772928)))]; + tensor linear_2_cast_fp16 = linear(bias = module_layers_0_linear1_bias_to_fp16, weight = module_layers_0_linear1_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor input_9_cast_fp16 = relu(x = linear_2_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_layers_0_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2775552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3594816))))[name = string("module_layers_0_linear2_weight_to_fp16_quantized")]; + tensor module_layers_0_linear2_bias_to_fp16 = const()[name = string("module_layers_0_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3596160)))]; + tensor linear_3_cast_fp16 = linear(bias = module_layers_0_linear2_bias_to_fp16, weight = module_layers_0_linear2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = input_5_cast_fp16, y = linear_3_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor x_5_axes_0 = const()[name = string("x_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm2_weight_to_fp16 = const()[name = string("module_layers_0_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3597504)))]; + tensor module_layers_0_norm2_bias_to_fp16 = const()[name = string("module_layers_0_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3598848)))]; + tensor x_5_cast_fp16 = layer_norm(axes = x_5_axes_0, beta = module_layers_0_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm2_weight_to_fp16, x = input_11_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_layers_1_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3600192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4829056))))[name = string("module_layers_1_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4832960)))]; + tensor linear_4_cast_fp16 = linear(bias = module_layers_1_self_attn_in_proj_bias_to_fp16, weight = module_layers_1_self_attn_in_proj_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_345_split_sizes_0 = const()[name = string("op_345_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_345_axis_0 = const()[name = string("op_345_axis_0"), val = int32(-1)]; + tensor var_345_cast_fp16_0, tensor var_345_cast_fp16_1, tensor var_345_cast_fp16_2 = split(axis = var_345_axis_0, split_sizes = var_345_split_sizes_0, x = linear_4_cast_fp16)[name = string("op_345_cast_fp16")]; + tensor var_348 = const()[name = string("op_348"), val = tensor([1, 256, 4, 160])]; + tensor var_349_cast_fp16 = reshape(shape = var_348, x = var_345_cast_fp16_0)[name = string("op_349_cast_fp16")]; + tensor var_351 = const()[name = string("op_351"), val = tensor([1, 256, 4, 160])]; + tensor var_352_cast_fp16 = reshape(shape = var_351, x = var_345_cast_fp16_1)[name = string("op_352_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 256, 4, 160])]; + tensor var_355_cast_fp16 = reshape(shape = var_354, x = var_345_cast_fp16_2)[name = string("op_355_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_358_transpose_x_0 = const()[name = string("op_358_transpose_x_0"), val = bool(false)]; + bool var_358_transpose_y_0 = const()[name = string("op_358_transpose_y_0"), val = bool(false)]; + tensor transpose_34_perm_0 = const()[name = string("transpose_34_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_35_perm_0 = const()[name = string("transpose_35_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_35 = transpose(perm = transpose_35_perm_0, x = var_352_cast_fp16)[name = string("transpose_74")]; + tensor transpose_34 = transpose(perm = transpose_34_perm_0, x = var_349_cast_fp16)[name = string("transpose_75")]; + tensor var_358_cast_fp16 = matmul(transpose_x = var_358_transpose_x_0, transpose_y = var_358_transpose_y_0, x = transpose_34, y = transpose_35)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_3_cast_fp16 = mul(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("scores_3_cast_fp16")]; + tensor weights_7_cast_fp16 = softmax(axis = var_24, x = scores_3_cast_fp16)[name = string("weights_7_cast_fp16")]; + tensor weights_9_cast_fp16 = mul(x = weights_7_cast_fp16, y = var_313_cast_fp16)[name = string("weights_9_cast_fp16")]; + tensor var_370_axes_0 = const()[name = string("op_370_axes_0"), val = tensor([-1])]; + bool var_370_keep_dims_0 = const()[name = string("op_370_keep_dims_0"), val = bool(true)]; + tensor var_370_cast_fp16 = reduce_sum(axes = var_370_axes_0, keep_dims = var_370_keep_dims_0, x = weights_9_cast_fp16)[name = string("op_370_cast_fp16")]; + tensor var_371_cast_fp16 = maximum(x = var_370_cast_fp16, y = var_11_to_fp16)[name = string("op_371_cast_fp16")]; + tensor weights_11_cast_fp16 = real_div(x = weights_9_cast_fp16, y = var_371_cast_fp16)[name = string("weights_11_cast_fp16")]; + bool context_5_transpose_x_0 = const()[name = string("context_5_transpose_x_0"), val = bool(false)]; + bool context_5_transpose_y_0 = const()[name = string("context_5_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_355_cast_fp16)[name = string("transpose_73")]; + tensor context_5_cast_fp16 = matmul(transpose_x = context_5_transpose_x_0, transpose_y = context_5_transpose_y_0, x = weights_11_cast_fp16, y = v_7_cast_fp16)[name = string("context_5_cast_fp16")]; + tensor var_374_perm_0 = const()[name = string("op_374_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_375 = const()[name = string("op_375"), val = tensor([1, 256, 640])]; + tensor var_374_cast_fp16 = transpose(perm = var_374_perm_0, x = context_5_cast_fp16)[name = string("transpose_72")]; + tensor context_7_cast_fp16 = reshape(shape = var_375, x = var_374_cast_fp16)[name = string("context_7_cast_fp16")]; + tensor module_layers_1_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4836864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5246528))))[name = string("module_layers_1_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5247872)))]; + tensor linear_5_cast_fp16 = linear(bias = module_layers_1_self_attn_out_proj_bias_to_fp16, weight = module_layers_1_self_attn_out_proj_weight_to_fp16_quantized, x = context_7_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = x_5_cast_fp16, y = linear_5_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm1_weight_to_fp16 = const()[name = string("module_layers_1_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5249216)))]; + tensor module_layers_1_norm1_bias_to_fp16 = const()[name = string("module_layers_1_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5250560)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_layers_1_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm1_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_layers_1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5251904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6071168))))[name = string("module_layers_1_linear1_weight_to_fp16_quantized")]; + tensor module_layers_1_linear1_bias_to_fp16 = const()[name = string("module_layers_1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6073792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_layers_1_linear1_bias_to_fp16, weight = module_layers_1_linear1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor input_19_cast_fp16 = relu(x = linear_6_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_layers_1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6076416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6895680))))[name = string("module_layers_1_linear2_weight_to_fp16_quantized")]; + tensor module_layers_1_linear2_bias_to_fp16 = const()[name = string("module_layers_1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6897024)))]; + tensor linear_7_cast_fp16 = linear(bias = module_layers_1_linear2_bias_to_fp16, weight = module_layers_1_linear2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = input_15_cast_fp16, y = linear_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm2_weight_to_fp16 = const()[name = string("module_layers_1_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6898368)))]; + tensor module_layers_1_norm2_bias_to_fp16 = const()[name = string("module_layers_1_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6899712)))]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = module_layers_1_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm2_weight_to_fp16, x = input_21_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor module_layers_2_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8129920))))[name = string("module_layers_2_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8133824)))]; + tensor linear_8_cast_fp16 = linear(bias = module_layers_2_self_attn_in_proj_bias_to_fp16, weight = module_layers_2_self_attn_in_proj_weight_to_fp16_quantized, x = x_7_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_398_split_sizes_0 = const()[name = string("op_398_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_398_axis_0 = const()[name = string("op_398_axis_0"), val = int32(-1)]; + tensor var_398_cast_fp16_0, tensor var_398_cast_fp16_1, tensor var_398_cast_fp16_2 = split(axis = var_398_axis_0, split_sizes = var_398_split_sizes_0, x = linear_8_cast_fp16)[name = string("op_398_cast_fp16")]; + tensor var_401 = const()[name = string("op_401"), val = tensor([1, 256, 4, 160])]; + tensor var_402_cast_fp16 = reshape(shape = var_401, x = var_398_cast_fp16_0)[name = string("op_402_cast_fp16")]; + tensor var_404 = const()[name = string("op_404"), val = tensor([1, 256, 4, 160])]; + tensor var_405_cast_fp16 = reshape(shape = var_404, x = var_398_cast_fp16_1)[name = string("op_405_cast_fp16")]; + tensor var_407 = const()[name = string("op_407"), val = tensor([1, 256, 4, 160])]; + tensor var_408_cast_fp16 = reshape(shape = var_407, x = var_398_cast_fp16_2)[name = string("op_408_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_411_transpose_x_0 = const()[name = string("op_411_transpose_x_0"), val = bool(false)]; + bool var_411_transpose_y_0 = const()[name = string("op_411_transpose_y_0"), val = bool(false)]; + tensor transpose_36_perm_0 = const()[name = string("transpose_36_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_37_perm_0 = const()[name = string("transpose_37_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_37 = transpose(perm = transpose_37_perm_0, x = var_405_cast_fp16)[name = string("transpose_70")]; + tensor transpose_36 = transpose(perm = transpose_36_perm_0, x = var_402_cast_fp16)[name = string("transpose_71")]; + tensor var_411_cast_fp16 = matmul(transpose_x = var_411_transpose_x_0, transpose_y = var_411_transpose_y_0, x = transpose_36, y = transpose_37)[name = string("op_411_cast_fp16")]; + fp16 var_412_to_fp16 = const()[name = string("op_412_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_5_cast_fp16 = mul(x = var_411_cast_fp16, y = var_412_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor weights_13_cast_fp16 = softmax(axis = var_24, x = scores_5_cast_fp16)[name = string("weights_13_cast_fp16")]; + tensor weights_15_cast_fp16 = mul(x = weights_13_cast_fp16, y = var_313_cast_fp16)[name = string("weights_15_cast_fp16")]; + tensor var_423_axes_0 = const()[name = string("op_423_axes_0"), val = tensor([-1])]; + bool var_423_keep_dims_0 = const()[name = string("op_423_keep_dims_0"), val = bool(true)]; + tensor var_423_cast_fp16 = reduce_sum(axes = var_423_axes_0, keep_dims = var_423_keep_dims_0, x = weights_15_cast_fp16)[name = string("op_423_cast_fp16")]; + tensor var_424_cast_fp16 = maximum(x = var_423_cast_fp16, y = var_11_to_fp16)[name = string("op_424_cast_fp16")]; + tensor weights_17_cast_fp16 = real_div(x = weights_15_cast_fp16, y = var_424_cast_fp16)[name = string("weights_17_cast_fp16")]; + bool context_9_transpose_x_0 = const()[name = string("context_9_transpose_x_0"), val = bool(false)]; + bool context_9_transpose_y_0 = const()[name = string("context_9_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_408_cast_fp16)[name = string("transpose_69")]; + tensor context_9_cast_fp16 = matmul(transpose_x = context_9_transpose_x_0, transpose_y = context_9_transpose_y_0, x = weights_17_cast_fp16, y = v_11_cast_fp16)[name = string("context_9_cast_fp16")]; + tensor var_427_perm_0 = const()[name = string("op_427_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_428 = const()[name = string("op_428"), val = tensor([1, 256, 640])]; + tensor var_427_cast_fp16 = transpose(perm = var_427_perm_0, x = context_9_cast_fp16)[name = string("transpose_68")]; + tensor context_11_cast_fp16 = reshape(shape = var_428, x = var_427_cast_fp16)[name = string("context_11_cast_fp16")]; + tensor module_layers_2_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8137728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8547392))))[name = string("module_layers_2_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548736)))]; + tensor linear_9_cast_fp16 = linear(bias = module_layers_2_self_attn_out_proj_bias_to_fp16, weight = module_layers_2_self_attn_out_proj_weight_to_fp16_quantized, x = context_11_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = x_7_cast_fp16, y = linear_9_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor input_25_axes_0 = const()[name = string("input_25_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm1_weight_to_fp16 = const()[name = string("module_layers_2_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8550080)))]; + tensor module_layers_2_norm1_bias_to_fp16 = const()[name = string("module_layers_2_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8551424)))]; + tensor input_25_cast_fp16 = layer_norm(axes = input_25_axes_0, beta = module_layers_2_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm1_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8552768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9372032))))[name = string("module_layers_2_linear1_weight_to_fp16_quantized")]; + tensor module_layers_2_linear1_bias_to_fp16 = const()[name = string("module_layers_2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9374656)))]; + tensor linear_10_cast_fp16 = linear(bias = module_layers_2_linear1_bias_to_fp16, weight = module_layers_2_linear1_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_29_cast_fp16 = relu(x = linear_10_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor module_layers_2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9377280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10196544))))[name = string("module_layers_2_linear2_weight_to_fp16_quantized")]; + tensor module_layers_2_linear2_bias_to_fp16 = const()[name = string("module_layers_2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10197888)))]; + tensor linear_11_cast_fp16 = linear(bias = module_layers_2_linear2_bias_to_fp16, weight = module_layers_2_linear2_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = input_25_cast_fp16, y = linear_11_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm2_weight_to_fp16 = const()[name = string("module_layers_2_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10199232)))]; + tensor module_layers_2_norm2_bias_to_fp16 = const()[name = string("module_layers_2_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10200576)))]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, beta = module_layers_2_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor module_layers_3_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10201920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11430784))))[name = string("module_layers_3_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11434688)))]; + tensor linear_12_cast_fp16 = linear(bias = module_layers_3_self_attn_in_proj_bias_to_fp16, weight = module_layers_3_self_attn_in_proj_weight_to_fp16_quantized, x = x_9_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_451_split_sizes_0 = const()[name = string("op_451_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_451_axis_0 = const()[name = string("op_451_axis_0"), val = int32(-1)]; + tensor var_451_cast_fp16_0, tensor var_451_cast_fp16_1, tensor var_451_cast_fp16_2 = split(axis = var_451_axis_0, split_sizes = var_451_split_sizes_0, x = linear_12_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor var_454 = const()[name = string("op_454"), val = tensor([1, 256, 4, 160])]; + tensor var_455_cast_fp16 = reshape(shape = var_454, x = var_451_cast_fp16_0)[name = string("op_455_cast_fp16")]; + tensor var_457 = const()[name = string("op_457"), val = tensor([1, 256, 4, 160])]; + tensor var_458_cast_fp16 = reshape(shape = var_457, x = var_451_cast_fp16_1)[name = string("op_458_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([1, 256, 4, 160])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = var_451_cast_fp16_2)[name = string("op_461_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_464_transpose_x_0 = const()[name = string("op_464_transpose_x_0"), val = bool(false)]; + bool var_464_transpose_y_0 = const()[name = string("op_464_transpose_y_0"), val = bool(false)]; + tensor transpose_38_perm_0 = const()[name = string("transpose_38_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_39_perm_0 = const()[name = string("transpose_39_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_39 = transpose(perm = transpose_39_perm_0, x = var_458_cast_fp16)[name = string("transpose_66")]; + tensor transpose_38 = transpose(perm = transpose_38_perm_0, x = var_455_cast_fp16)[name = string("transpose_67")]; + tensor var_464_cast_fp16 = matmul(transpose_x = var_464_transpose_x_0, transpose_y = var_464_transpose_y_0, x = transpose_38, y = transpose_39)[name = string("op_464_cast_fp16")]; + fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_7_cast_fp16 = mul(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("scores_7_cast_fp16")]; + tensor weights_19_cast_fp16 = softmax(axis = var_24, x = scores_7_cast_fp16)[name = string("weights_19_cast_fp16")]; + tensor weights_21_cast_fp16 = mul(x = weights_19_cast_fp16, y = var_313_cast_fp16)[name = string("weights_21_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + bool var_476_keep_dims_0 = const()[name = string("op_476_keep_dims_0"), val = bool(true)]; + tensor var_476_cast_fp16 = reduce_sum(axes = var_476_axes_0, keep_dims = var_476_keep_dims_0, x = weights_21_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor var_477_cast_fp16 = maximum(x = var_476_cast_fp16, y = var_11_to_fp16)[name = string("op_477_cast_fp16")]; + tensor weights_23_cast_fp16 = real_div(x = weights_21_cast_fp16, y = var_477_cast_fp16)[name = string("weights_23_cast_fp16")]; + bool context_13_transpose_x_0 = const()[name = string("context_13_transpose_x_0"), val = bool(false)]; + bool context_13_transpose_y_0 = const()[name = string("context_13_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_461_cast_fp16)[name = string("transpose_65")]; + tensor context_13_cast_fp16 = matmul(transpose_x = context_13_transpose_x_0, transpose_y = context_13_transpose_y_0, x = weights_23_cast_fp16, y = v_15_cast_fp16)[name = string("context_13_cast_fp16")]; + tensor var_480_perm_0 = const()[name = string("op_480_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_481 = const()[name = string("op_481"), val = tensor([1, 256, 640])]; + tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = context_13_cast_fp16)[name = string("transpose_64")]; + tensor context_15_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("context_15_cast_fp16")]; + tensor module_layers_3_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11438592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11848256))))[name = string("module_layers_3_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11849600)))]; + tensor linear_13_cast_fp16 = linear(bias = module_layers_3_self_attn_out_proj_bias_to_fp16, weight = module_layers_3_self_attn_out_proj_weight_to_fp16_quantized, x = context_15_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = x_9_cast_fp16, y = linear_13_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_axes_0 = const()[name = string("input_35_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm1_weight_to_fp16 = const()[name = string("module_layers_3_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11850944)))]; + tensor module_layers_3_norm1_bias_to_fp16 = const()[name = string("module_layers_3_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11852288)))]; + tensor input_35_cast_fp16 = layer_norm(axes = input_35_axes_0, beta = module_layers_3_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm1_weight_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_3_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11853632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12672896))))[name = string("module_layers_3_linear1_weight_to_fp16_quantized")]; + tensor module_layers_3_linear1_bias_to_fp16 = const()[name = string("module_layers_3_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12675520)))]; + tensor linear_14_cast_fp16 = linear(bias = module_layers_3_linear1_bias_to_fp16, weight = module_layers_3_linear1_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor input_39_cast_fp16 = relu(x = linear_14_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_layers_3_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12678144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13497408))))[name = string("module_layers_3_linear2_weight_to_fp16_quantized")]; + tensor module_layers_3_linear2_bias_to_fp16 = const()[name = string("module_layers_3_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13498752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_layers_3_linear2_bias_to_fp16, weight = module_layers_3_linear2_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = input_35_cast_fp16, y = linear_15_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm2_weight_to_fp16 = const()[name = string("module_layers_3_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13500096)))]; + tensor module_layers_3_norm2_bias_to_fp16 = const()[name = string("module_layers_3_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13501440)))]; + tensor x_11_cast_fp16 = layer_norm(axes = x_11_axes_0, beta = module_layers_3_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm2_weight_to_fp16, x = input_41_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_layers_4_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13502784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14731648))))[name = string("module_layers_4_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14735552)))]; + tensor linear_16_cast_fp16 = linear(bias = module_layers_4_self_attn_in_proj_bias_to_fp16, weight = module_layers_4_self_attn_in_proj_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_504_split_sizes_0 = const()[name = string("op_504_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_504_axis_0 = const()[name = string("op_504_axis_0"), val = int32(-1)]; + tensor var_504_cast_fp16_0, tensor var_504_cast_fp16_1, tensor var_504_cast_fp16_2 = split(axis = var_504_axis_0, split_sizes = var_504_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_504_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 256, 4, 160])]; + tensor var_508_cast_fp16 = reshape(shape = var_507, x = var_504_cast_fp16_0)[name = string("op_508_cast_fp16")]; + tensor var_510 = const()[name = string("op_510"), val = tensor([1, 256, 4, 160])]; + tensor var_511_cast_fp16 = reshape(shape = var_510, x = var_504_cast_fp16_1)[name = string("op_511_cast_fp16")]; + tensor var_513 = const()[name = string("op_513"), val = tensor([1, 256, 4, 160])]; + tensor var_514_cast_fp16 = reshape(shape = var_513, x = var_504_cast_fp16_2)[name = string("op_514_cast_fp16")]; + tensor v_19_perm_0 = const()[name = string("v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_517_transpose_x_0 = const()[name = string("op_517_transpose_x_0"), val = bool(false)]; + bool var_517_transpose_y_0 = const()[name = string("op_517_transpose_y_0"), val = bool(false)]; + tensor transpose_40_perm_0 = const()[name = string("transpose_40_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_41_perm_0 = const()[name = string("transpose_41_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_41 = transpose(perm = transpose_41_perm_0, x = var_511_cast_fp16)[name = string("transpose_62")]; + tensor transpose_40 = transpose(perm = transpose_40_perm_0, x = var_508_cast_fp16)[name = string("transpose_63")]; + tensor var_517_cast_fp16 = matmul(transpose_x = var_517_transpose_x_0, transpose_y = var_517_transpose_y_0, x = transpose_40, y = transpose_41)[name = string("op_517_cast_fp16")]; + fp16 var_518_to_fp16 = const()[name = string("op_518_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_9_cast_fp16 = mul(x = var_517_cast_fp16, y = var_518_to_fp16)[name = string("scores_9_cast_fp16")]; + tensor weights_25_cast_fp16 = softmax(axis = var_24, x = scores_9_cast_fp16)[name = string("weights_25_cast_fp16")]; + tensor weights_27_cast_fp16 = mul(x = weights_25_cast_fp16, y = var_313_cast_fp16)[name = string("weights_27_cast_fp16")]; + tensor var_529_axes_0 = const()[name = string("op_529_axes_0"), val = tensor([-1])]; + bool var_529_keep_dims_0 = const()[name = string("op_529_keep_dims_0"), val = bool(true)]; + tensor var_529_cast_fp16 = reduce_sum(axes = var_529_axes_0, keep_dims = var_529_keep_dims_0, x = weights_27_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = maximum(x = var_529_cast_fp16, y = var_11_to_fp16)[name = string("op_530_cast_fp16")]; + tensor weights_29_cast_fp16 = real_div(x = weights_27_cast_fp16, y = var_530_cast_fp16)[name = string("weights_29_cast_fp16")]; + bool context_17_transpose_x_0 = const()[name = string("context_17_transpose_x_0"), val = bool(false)]; + bool context_17_transpose_y_0 = const()[name = string("context_17_transpose_y_0"), val = bool(false)]; + tensor v_19_cast_fp16 = transpose(perm = v_19_perm_0, x = var_514_cast_fp16)[name = string("transpose_61")]; + tensor context_17_cast_fp16 = matmul(transpose_x = context_17_transpose_x_0, transpose_y = context_17_transpose_y_0, x = weights_29_cast_fp16, y = v_19_cast_fp16)[name = string("context_17_cast_fp16")]; + tensor var_533_perm_0 = const()[name = string("op_533_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_534 = const()[name = string("op_534"), val = tensor([1, 256, 640])]; + tensor var_533_cast_fp16 = transpose(perm = var_533_perm_0, x = context_17_cast_fp16)[name = string("transpose_60")]; + tensor context_19_cast_fp16 = reshape(shape = var_534, x = var_533_cast_fp16)[name = string("context_19_cast_fp16")]; + tensor module_layers_4_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14739456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15149120))))[name = string("module_layers_4_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15150464)))]; + tensor linear_17_cast_fp16 = linear(bias = module_layers_4_self_attn_out_proj_bias_to_fp16, weight = module_layers_4_self_attn_out_proj_weight_to_fp16_quantized, x = context_19_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = x_11_cast_fp16, y = linear_17_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm1_weight_to_fp16 = const()[name = string("module_layers_4_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15151808)))]; + tensor module_layers_4_norm1_bias_to_fp16 = const()[name = string("module_layers_4_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15153152)))]; + tensor input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = module_layers_4_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_layers_4_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15154496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15973760))))[name = string("module_layers_4_linear1_weight_to_fp16_quantized")]; + tensor module_layers_4_linear1_bias_to_fp16 = const()[name = string("module_layers_4_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15976384)))]; + tensor linear_18_cast_fp16 = linear(bias = module_layers_4_linear1_bias_to_fp16, weight = module_layers_4_linear1_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor input_49_cast_fp16 = relu(x = linear_18_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_layers_4_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15979008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16798272))))[name = string("module_layers_4_linear2_weight_to_fp16_quantized")]; + tensor module_layers_4_linear2_bias_to_fp16 = const()[name = string("module_layers_4_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16799616)))]; + tensor linear_19_cast_fp16 = linear(bias = module_layers_4_linear2_bias_to_fp16, weight = module_layers_4_linear2_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = input_45_cast_fp16, y = linear_19_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm2_weight_to_fp16 = const()[name = string("module_layers_4_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16800960)))]; + tensor module_layers_4_norm2_bias_to_fp16 = const()[name = string("module_layers_4_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16802304)))]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = module_layers_4_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm2_weight_to_fp16, x = input_51_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor module_layers_5_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16803648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18032512))))[name = string("module_layers_5_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18036416)))]; + tensor linear_20_cast_fp16 = linear(bias = module_layers_5_self_attn_in_proj_bias_to_fp16, weight = module_layers_5_self_attn_in_proj_weight_to_fp16_quantized, x = x_13_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_557_split_sizes_0 = const()[name = string("op_557_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_557_axis_0 = const()[name = string("op_557_axis_0"), val = int32(-1)]; + tensor var_557_cast_fp16_0, tensor var_557_cast_fp16_1, tensor var_557_cast_fp16_2 = split(axis = var_557_axis_0, split_sizes = var_557_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_560 = const()[name = string("op_560"), val = tensor([1, 256, 4, 160])]; + tensor var_561_cast_fp16 = reshape(shape = var_560, x = var_557_cast_fp16_0)[name = string("op_561_cast_fp16")]; + tensor var_563 = const()[name = string("op_563"), val = tensor([1, 256, 4, 160])]; + tensor var_564_cast_fp16 = reshape(shape = var_563, x = var_557_cast_fp16_1)[name = string("op_564_cast_fp16")]; + tensor var_566 = const()[name = string("op_566"), val = tensor([1, 256, 4, 160])]; + tensor var_567_cast_fp16 = reshape(shape = var_566, x = var_557_cast_fp16_2)[name = string("op_567_cast_fp16")]; + tensor v_23_perm_0 = const()[name = string("v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_570_transpose_x_0 = const()[name = string("op_570_transpose_x_0"), val = bool(false)]; + bool var_570_transpose_y_0 = const()[name = string("op_570_transpose_y_0"), val = bool(false)]; + tensor transpose_42_perm_0 = const()[name = string("transpose_42_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_43_perm_0 = const()[name = string("transpose_43_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_43 = transpose(perm = transpose_43_perm_0, x = var_564_cast_fp16)[name = string("transpose_58")]; + tensor transpose_42 = transpose(perm = transpose_42_perm_0, x = var_561_cast_fp16)[name = string("transpose_59")]; + tensor var_570_cast_fp16 = matmul(transpose_x = var_570_transpose_x_0, transpose_y = var_570_transpose_y_0, x = transpose_42, y = transpose_43)[name = string("op_570_cast_fp16")]; + fp16 var_571_to_fp16 = const()[name = string("op_571_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_11_cast_fp16 = mul(x = var_570_cast_fp16, y = var_571_to_fp16)[name = string("scores_11_cast_fp16")]; + tensor weights_31_cast_fp16 = softmax(axis = var_24, x = scores_11_cast_fp16)[name = string("weights_31_cast_fp16")]; + tensor weights_33_cast_fp16 = mul(x = weights_31_cast_fp16, y = var_313_cast_fp16)[name = string("weights_33_cast_fp16")]; + tensor var_582_axes_0 = const()[name = string("op_582_axes_0"), val = tensor([-1])]; + bool var_582_keep_dims_0 = const()[name = string("op_582_keep_dims_0"), val = bool(true)]; + tensor var_582_cast_fp16 = reduce_sum(axes = var_582_axes_0, keep_dims = var_582_keep_dims_0, x = weights_33_cast_fp16)[name = string("op_582_cast_fp16")]; + tensor var_583_cast_fp16 = maximum(x = var_582_cast_fp16, y = var_11_to_fp16)[name = string("op_583_cast_fp16")]; + tensor weights_35_cast_fp16 = real_div(x = weights_33_cast_fp16, y = var_583_cast_fp16)[name = string("weights_35_cast_fp16")]; + bool context_21_transpose_x_0 = const()[name = string("context_21_transpose_x_0"), val = bool(false)]; + bool context_21_transpose_y_0 = const()[name = string("context_21_transpose_y_0"), val = bool(false)]; + tensor v_23_cast_fp16 = transpose(perm = v_23_perm_0, x = var_567_cast_fp16)[name = string("transpose_57")]; + tensor context_21_cast_fp16 = matmul(transpose_x = context_21_transpose_x_0, transpose_y = context_21_transpose_y_0, x = weights_35_cast_fp16, y = v_23_cast_fp16)[name = string("context_21_cast_fp16")]; + tensor var_586_perm_0 = const()[name = string("op_586_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_587 = const()[name = string("op_587"), val = tensor([1, 256, 640])]; + tensor var_586_cast_fp16 = transpose(perm = var_586_perm_0, x = context_21_cast_fp16)[name = string("transpose_56")]; + tensor context_23_cast_fp16 = reshape(shape = var_587, x = var_586_cast_fp16)[name = string("context_23_cast_fp16")]; + tensor module_layers_5_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18040320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18449984))))[name = string("module_layers_5_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18451328)))]; + tensor linear_21_cast_fp16 = linear(bias = module_layers_5_self_attn_out_proj_bias_to_fp16, weight = module_layers_5_self_attn_out_proj_weight_to_fp16_quantized, x = context_23_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = x_13_cast_fp16, y = linear_21_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm1_weight_to_fp16 = const()[name = string("module_layers_5_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18452672)))]; + tensor module_layers_5_norm1_bias_to_fp16 = const()[name = string("module_layers_5_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18454016)))]; + tensor input_55_cast_fp16 = layer_norm(axes = input_55_axes_0, beta = module_layers_5_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm1_weight_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_layers_5_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18455360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19274624))))[name = string("module_layers_5_linear1_weight_to_fp16_quantized")]; + tensor module_layers_5_linear1_bias_to_fp16 = const()[name = string("module_layers_5_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19277248)))]; + tensor linear_22_cast_fp16 = linear(bias = module_layers_5_linear1_bias_to_fp16, weight = module_layers_5_linear1_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor input_59_cast_fp16 = relu(x = linear_22_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_5_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19279872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20099136))))[name = string("module_layers_5_linear2_weight_to_fp16_quantized")]; + tensor module_layers_5_linear2_bias_to_fp16 = const()[name = string("module_layers_5_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20100480)))]; + tensor linear_23_cast_fp16 = linear(bias = module_layers_5_linear2_bias_to_fp16, weight = module_layers_5_linear2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_55_cast_fp16, y = linear_23_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm2_weight_to_fp16 = const()[name = string("module_layers_5_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20101824)))]; + tensor module_layers_5_norm2_bias_to_fp16 = const()[name = string("module_layers_5_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20103168)))]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, beta = module_layers_5_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm2_weight_to_fp16, x = input_61_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor module_layers_6_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20104512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21333376))))[name = string("module_layers_6_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21337280)))]; + tensor linear_24_cast_fp16 = linear(bias = module_layers_6_self_attn_in_proj_bias_to_fp16, weight = module_layers_6_self_attn_in_proj_weight_to_fp16_quantized, x = x_15_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_610_split_sizes_0 = const()[name = string("op_610_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_610_axis_0 = const()[name = string("op_610_axis_0"), val = int32(-1)]; + tensor var_610_cast_fp16_0, tensor var_610_cast_fp16_1, tensor var_610_cast_fp16_2 = split(axis = var_610_axis_0, split_sizes = var_610_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_610_cast_fp16")]; + tensor var_613 = const()[name = string("op_613"), val = tensor([1, 256, 4, 160])]; + tensor var_614_cast_fp16 = reshape(shape = var_613, x = var_610_cast_fp16_0)[name = string("op_614_cast_fp16")]; + tensor var_616 = const()[name = string("op_616"), val = tensor([1, 256, 4, 160])]; + tensor var_617_cast_fp16 = reshape(shape = var_616, x = var_610_cast_fp16_1)[name = string("op_617_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([1, 256, 4, 160])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = var_610_cast_fp16_2)[name = string("op_620_cast_fp16")]; + tensor v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_623_transpose_x_0 = const()[name = string("op_623_transpose_x_0"), val = bool(false)]; + bool var_623_transpose_y_0 = const()[name = string("op_623_transpose_y_0"), val = bool(false)]; + tensor transpose_44_perm_0 = const()[name = string("transpose_44_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_45_perm_0 = const()[name = string("transpose_45_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_45 = transpose(perm = transpose_45_perm_0, x = var_617_cast_fp16)[name = string("transpose_54")]; + tensor transpose_44 = transpose(perm = transpose_44_perm_0, x = var_614_cast_fp16)[name = string("transpose_55")]; + tensor var_623_cast_fp16 = matmul(transpose_x = var_623_transpose_x_0, transpose_y = var_623_transpose_y_0, x = transpose_44, y = transpose_45)[name = string("op_623_cast_fp16")]; + fp16 var_624_to_fp16 = const()[name = string("op_624_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_13_cast_fp16 = mul(x = var_623_cast_fp16, y = var_624_to_fp16)[name = string("scores_13_cast_fp16")]; + tensor weights_37_cast_fp16 = softmax(axis = var_24, x = scores_13_cast_fp16)[name = string("weights_37_cast_fp16")]; + tensor weights_39_cast_fp16 = mul(x = weights_37_cast_fp16, y = var_313_cast_fp16)[name = string("weights_39_cast_fp16")]; + tensor var_635_axes_0 = const()[name = string("op_635_axes_0"), val = tensor([-1])]; + bool var_635_keep_dims_0 = const()[name = string("op_635_keep_dims_0"), val = bool(true)]; + tensor var_635_cast_fp16 = reduce_sum(axes = var_635_axes_0, keep_dims = var_635_keep_dims_0, x = weights_39_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_cast_fp16 = maximum(x = var_635_cast_fp16, y = var_11_to_fp16)[name = string("op_636_cast_fp16")]; + tensor weights_41_cast_fp16 = real_div(x = weights_39_cast_fp16, y = var_636_cast_fp16)[name = string("weights_41_cast_fp16")]; + bool context_25_transpose_x_0 = const()[name = string("context_25_transpose_x_0"), val = bool(false)]; + bool context_25_transpose_y_0 = const()[name = string("context_25_transpose_y_0"), val = bool(false)]; + tensor v_27_cast_fp16 = transpose(perm = v_27_perm_0, x = var_620_cast_fp16)[name = string("transpose_53")]; + tensor context_25_cast_fp16 = matmul(transpose_x = context_25_transpose_x_0, transpose_y = context_25_transpose_y_0, x = weights_41_cast_fp16, y = v_27_cast_fp16)[name = string("context_25_cast_fp16")]; + tensor var_639_perm_0 = const()[name = string("op_639_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_640 = const()[name = string("op_640"), val = tensor([1, 256, 640])]; + tensor var_639_cast_fp16 = transpose(perm = var_639_perm_0, x = context_25_cast_fp16)[name = string("transpose_52")]; + tensor context_27_cast_fp16 = reshape(shape = var_640, x = var_639_cast_fp16)[name = string("context_27_cast_fp16")]; + tensor module_layers_6_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21341184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750848))))[name = string("module_layers_6_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21752192)))]; + tensor linear_25_cast_fp16 = linear(bias = module_layers_6_self_attn_out_proj_bias_to_fp16, weight = module_layers_6_self_attn_out_proj_weight_to_fp16_quantized, x = context_27_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = x_15_cast_fp16, y = linear_25_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm1_weight_to_fp16 = const()[name = string("module_layers_6_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21753536)))]; + tensor module_layers_6_norm1_bias_to_fp16 = const()[name = string("module_layers_6_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21754880)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = module_layers_6_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm1_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_layers_6_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21756224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22575488))))[name = string("module_layers_6_linear1_weight_to_fp16_quantized")]; + tensor module_layers_6_linear1_bias_to_fp16 = const()[name = string("module_layers_6_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22578112)))]; + tensor linear_26_cast_fp16 = linear(bias = module_layers_6_linear1_bias_to_fp16, weight = module_layers_6_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_69_cast_fp16 = relu(x = linear_26_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_layers_6_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22580736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23400000))))[name = string("module_layers_6_linear2_weight_to_fp16_quantized")]; + tensor module_layers_6_linear2_bias_to_fp16 = const()[name = string("module_layers_6_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23401344)))]; + tensor linear_27_cast_fp16 = linear(bias = module_layers_6_linear2_bias_to_fp16, weight = module_layers_6_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = input_65_cast_fp16, y = linear_27_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm2_weight_to_fp16 = const()[name = string("module_layers_6_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23402688)))]; + tensor module_layers_6_norm2_bias_to_fp16 = const()[name = string("module_layers_6_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23404032)))]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, beta = module_layers_6_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm2_weight_to_fp16, x = input_71_cast_fp16)[name = string("x_cast_fp16")]; + tensor module_layers_7_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23405376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24634240))))[name = string("module_layers_7_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24638144)))]; + tensor linear_28_cast_fp16 = linear(bias = module_layers_7_self_attn_in_proj_bias_to_fp16, weight = module_layers_7_self_attn_in_proj_weight_to_fp16_quantized, x = x_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_663_split_sizes_0 = const()[name = string("op_663_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_663_axis_0 = const()[name = string("op_663_axis_0"), val = int32(-1)]; + tensor var_663_cast_fp16_0, tensor var_663_cast_fp16_1, tensor var_663_cast_fp16_2 = split(axis = var_663_axis_0, split_sizes = var_663_split_sizes_0, x = linear_28_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor var_666 = const()[name = string("op_666"), val = tensor([1, 256, 4, 160])]; + tensor var_667_cast_fp16 = reshape(shape = var_666, x = var_663_cast_fp16_0)[name = string("op_667_cast_fp16")]; + tensor var_669 = const()[name = string("op_669"), val = tensor([1, 256, 4, 160])]; + tensor var_670_cast_fp16 = reshape(shape = var_669, x = var_663_cast_fp16_1)[name = string("op_670_cast_fp16")]; + tensor var_672 = const()[name = string("op_672"), val = tensor([1, 256, 4, 160])]; + tensor var_673_cast_fp16 = reshape(shape = var_672, x = var_663_cast_fp16_2)[name = string("op_673_cast_fp16")]; + tensor v_perm_0 = const()[name = string("v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_676_transpose_x_0 = const()[name = string("op_676_transpose_x_0"), val = bool(false)]; + bool var_676_transpose_y_0 = const()[name = string("op_676_transpose_y_0"), val = bool(false)]; + tensor transpose_46_perm_0 = const()[name = string("transpose_46_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_47_perm_0 = const()[name = string("transpose_47_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_47 = transpose(perm = transpose_47_perm_0, x = var_670_cast_fp16)[name = string("transpose_50")]; + tensor transpose_46 = transpose(perm = transpose_46_perm_0, x = var_667_cast_fp16)[name = string("transpose_51")]; + tensor var_676_cast_fp16 = matmul(transpose_x = var_676_transpose_x_0, transpose_y = var_676_transpose_y_0, x = transpose_46, y = transpose_47)[name = string("op_676_cast_fp16")]; + fp16 var_677_to_fp16 = const()[name = string("op_677_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_cast_fp16 = mul(x = var_676_cast_fp16, y = var_677_to_fp16)[name = string("scores_cast_fp16")]; + tensor weights_43_cast_fp16 = softmax(axis = var_24, x = scores_cast_fp16)[name = string("weights_43_cast_fp16")]; + tensor weights_45_cast_fp16 = mul(x = weights_43_cast_fp16, y = var_313_cast_fp16)[name = string("weights_45_cast_fp16")]; + tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([-1])]; + bool var_688_keep_dims_0 = const()[name = string("op_688_keep_dims_0"), val = bool(true)]; + tensor var_688_cast_fp16 = reduce_sum(axes = var_688_axes_0, keep_dims = var_688_keep_dims_0, x = weights_45_cast_fp16)[name = string("op_688_cast_fp16")]; + tensor var_689_cast_fp16 = maximum(x = var_688_cast_fp16, y = var_11_to_fp16)[name = string("op_689_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_45_cast_fp16, y = var_689_cast_fp16)[name = string("weights_cast_fp16")]; + bool context_29_transpose_x_0 = const()[name = string("context_29_transpose_x_0"), val = bool(false)]; + bool context_29_transpose_y_0 = const()[name = string("context_29_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = v_perm_0, x = var_673_cast_fp16)[name = string("transpose_49")]; + tensor context_29_cast_fp16 = matmul(transpose_x = context_29_transpose_x_0, transpose_y = context_29_transpose_y_0, x = weights_cast_fp16, y = v_cast_fp16)[name = string("context_29_cast_fp16")]; + tensor var_692_perm_0 = const()[name = string("op_692_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_693 = const()[name = string("op_693"), val = tensor([1, 256, 640])]; + tensor var_692_cast_fp16 = transpose(perm = var_692_perm_0, x = context_29_cast_fp16)[name = string("transpose_48")]; + tensor context_cast_fp16 = reshape(shape = var_693, x = var_692_cast_fp16)[name = string("context_cast_fp16")]; + tensor module_layers_7_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24642048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25051712))))[name = string("module_layers_7_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25053056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_layers_7_self_attn_out_proj_bias_to_fp16, weight = module_layers_7_self_attn_out_proj_weight_to_fp16_quantized, x = context_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor input_73_cast_fp16 = add(x = x_cast_fp16, y = linear_29_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm1_weight_to_fp16 = const()[name = string("module_layers_7_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25054400)))]; + tensor module_layers_7_norm1_bias_to_fp16 = const()[name = string("module_layers_7_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25055744)))]; + tensor input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = module_layers_7_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm1_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_layers_7_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25057088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25876352))))[name = string("module_layers_7_linear1_weight_to_fp16_quantized")]; + tensor module_layers_7_linear1_bias_to_fp16 = const()[name = string("module_layers_7_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25878976)))]; + tensor linear_30_cast_fp16 = linear(bias = module_layers_7_linear1_bias_to_fp16, weight = module_layers_7_linear1_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor input_79_cast_fp16 = relu(x = linear_30_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_layers_7_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25881600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26700864))))[name = string("module_layers_7_linear2_weight_to_fp16_quantized")]; + tensor module_layers_7_linear2_bias_to_fp16 = const()[name = string("module_layers_7_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26702208)))]; + tensor linear_31_cast_fp16 = linear(bias = module_layers_7_linear2_bias_to_fp16, weight = module_layers_7_linear2_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor input_81_cast_fp16 = add(x = input_75_cast_fp16, y = linear_31_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor input_83_axes_0 = const()[name = string("input_83_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm2_weight_to_fp16 = const()[name = string("module_layers_7_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26703552)))]; + tensor module_layers_7_norm2_bias_to_fp16 = const()[name = string("module_layers_7_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26704896)))]; + tensor input_83_cast_fp16 = layer_norm(axes = input_83_axes_0, beta = module_layers_7_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm2_weight_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([-1])]; + tensor module_encoder_norm_weight_to_fp16 = const()[name = string("module_encoder_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26706240)))]; + tensor module_encoder_norm_bias_to_fp16 = const()[name = string("module_encoder_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26707584)))]; + tensor input_cast_fp16 = layer_norm(axes = input_axes_0, beta = module_encoder_norm_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_encoder_norm_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_cast_fp16")]; + tensor module_fc_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26708928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770432))))[name = string("module_fc_out_weight_to_fp16_quantized")]; + tensor module_fc_out_bias_to_fp16 = const()[name = string("module_fc_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770688)))]; + tensor linear_32_cast_fp16 = linear(bias = module_fc_out_bias_to_fp16, weight = module_fc_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor padded_axes_0 = const()[name = string("padded_axes_0"), val = tensor([-1])]; + tensor padded = expand_dims(axes = padded_axes_0, x = src_pad_mask)[name = string("padded")]; + tensor var_723_begin_0 = const()[name = string("op_723_begin_0"), val = tensor([0, 0, 0])]; + tensor var_723_end_0 = const()[name = string("op_723_end_0"), val = tensor([1, 256, 1])]; + tensor var_723_end_mask_0 = const()[name = string("op_723_end_mask_0"), val = tensor([true, true, false])]; + tensor var_723_cast_fp16 = slice_by_index(begin = var_723_begin_0, end = var_723_end_0, end_mask = var_723_end_mask_0, x = linear_32_cast_fp16)[name = string("op_723_cast_fp16")]; + tensor var_722_to_fp16 = const()[name = string("op_722_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27017344)))]; + tensor blank_logits_cast_fp16 = select(a = var_722_to_fp16, b = var_723_cast_fp16, cond = padded)[name = string("blank_logits_cast_fp16")]; + tensor var_725_begin_0 = const()[name = string("op_725_begin_0"), val = tensor([0, 0, 1])]; + tensor var_725_end_0 = const()[name = string("op_725_end_0"), val = tensor([1, 256, 96])]; + tensor var_725_end_mask_0 = const()[name = string("op_725_end_mask_0"), val = tensor([true, true, true])]; + tensor var_725_cast_fp16 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = linear_32_cast_fp16)[name = string("op_725_cast_fp16")]; + fp16 var_726_value_0_to_fp16 = const()[name = string("op_726_value_0_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor var_726_cast_fp16 = fill_like(ref_tensor = var_725_cast_fp16, value = var_726_value_0_to_fp16)[name = string("op_726_cast_fp16")]; + tensor padded_after_broadcast_reps_0 = const()[name = string("padded_after_broadcast_reps_0"), val = tensor([1, 1, 95])]; + tensor padded_after_broadcast = tile(reps = padded_after_broadcast_reps_0, x = padded)[name = string("padded_after_broadcast")]; + tensor nonblank_logits_cast_fp16 = select(a = var_726_cast_fp16, b = var_725_cast_fp16, cond = padded_after_broadcast)[name = string("nonblank_logits_cast_fp16")]; + bool var_730_interleave_0 = const()[name = string("op_730_interleave_0"), val = bool(false)]; + tensor var_730_cast_fp16 = concat(axis = var_24, interleave = var_730_interleave_0, values = (blank_logits_cast_fp16, nonblank_logits_cast_fp16))[name = string("op_730_cast_fp16")]; + string var_730_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_730_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_730_cast_fp16_to_fp32_dtype_0, x = var_730_cast_fp16)[name = string("cast_1")]; + } -> (logits); + func g2p_64(tensor text) { + int32 var_24 = const()[name = string("op_24"), val = int32(-1)]; + int32 var_26 = const()[name = string("op_26"), val = int32(0)]; + tensor src_pad_mask = equal(x = text, y = var_26)[name = string("src_pad_mask")]; + int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)]; + bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)]; + tensor module_embedding_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(257408))))[name = string("module_embedding_weight_to_fp16_quantized")]; + string text_to_int16_dtype_0 = const()[name = string("text_to_int16_dtype_0"), val = string("int16")]; + string cast_49_dtype_0 = const()[name = string("cast_49_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor text_to_int16 = cast(dtype = text_to_int16_dtype_0, x = text)[name = string("cast_6")]; + tensor cast_49 = cast(dtype = cast_49_dtype_0, x = text_to_int16)[name = string("cast_5")]; + tensor greater_equal_0 = greater_equal(x = cast_49, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(402)]; + tensor add_0 = add(x = cast_49, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_49, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_4")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_3")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(402)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 x_1_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_1, validate_indices = x_1_validate_indices_0, x = module_embedding_weight_to_fp16_quantized)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")]; + tensor op_286_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(258304))), scale = tensor([[[0x1.724p-7]]]))[name = string("op_286_to_fp16_quantized")]; + tensor input_1_cast_fp16 = add(x = x_1_cast_fp16_cast_uint16_cast_uint16, y = op_286_to_fp16_quantized)[name = string("input_1_cast_fp16")]; + tensor module_layers_0_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299328))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1528192))))[name = string("module_layers_0_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1532096)))]; + tensor linear_0_cast_fp16 = linear(bias = module_layers_0_self_attn_in_proj_bias_to_fp16, weight = module_layers_0_self_attn_in_proj_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_292_split_sizes_0 = const()[name = string("op_292_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_292_axis_0 = const()[name = string("op_292_axis_0"), val = int32(-1)]; + tensor var_292_cast_fp16_0, tensor var_292_cast_fp16_1, tensor var_292_cast_fp16_2 = split(axis = var_292_axis_0, split_sizes = var_292_split_sizes_0, x = linear_0_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor var_295 = const()[name = string("op_295"), val = tensor([1, 64, 4, 160])]; + tensor var_296_cast_fp16 = reshape(shape = var_295, x = var_292_cast_fp16_0)[name = string("op_296_cast_fp16")]; + tensor var_298 = const()[name = string("op_298"), val = tensor([1, 64, 4, 160])]; + tensor var_299_cast_fp16 = reshape(shape = var_298, x = var_292_cast_fp16_1)[name = string("op_299_cast_fp16")]; + tensor var_301 = const()[name = string("op_301"), val = tensor([1, 64, 4, 160])]; + tensor var_302_cast_fp16 = reshape(shape = var_301, x = var_292_cast_fp16_2)[name = string("op_302_cast_fp16")]; + tensor v_3_perm_0 = const()[name = string("v_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_305_transpose_x_0 = const()[name = string("op_305_transpose_x_0"), val = bool(false)]; + bool var_305_transpose_y_0 = const()[name = string("op_305_transpose_y_0"), val = bool(false)]; + tensor transpose_32_perm_0 = const()[name = string("transpose_32_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_33_perm_0 = const()[name = string("transpose_33_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_33 = transpose(perm = transpose_33_perm_0, x = var_299_cast_fp16)[name = string("transpose_78")]; + tensor transpose_32 = transpose(perm = transpose_32_perm_0, x = var_296_cast_fp16)[name = string("transpose_79")]; + tensor var_305_cast_fp16 = matmul(transpose_x = var_305_transpose_x_0, transpose_y = var_305_transpose_y_0, x = transpose_32, y = transpose_33)[name = string("op_305_cast_fp16")]; + fp16 var_306_to_fp16 = const()[name = string("op_306_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_1_cast_fp16 = mul(x = var_305_cast_fp16, y = var_306_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor weights_1_cast_fp16 = softmax(axis = var_24, x = scores_1_cast_fp16)[name = string("weights_1_cast_fp16")]; + fp16 var_13_to_fp16 = const()[name = string("op_13_to_fp16"), val = fp16(0x1p+0)]; + string var_309_to_fp16_dtype_0 = const()[name = string("op_309_to_fp16_dtype_0"), val = string("fp16")]; + tensor src_pad_mask_to_fp16 = cast(dtype = var_309_to_fp16_dtype_0, x = src_pad_mask)[name = string("cast_2")]; + tensor var_310_cast_fp16 = sub(x = var_13_to_fp16, y = src_pad_mask_to_fp16)[name = string("op_310_cast_fp16")]; + tensor var_312_axes_0 = const()[name = string("op_312_axes_0"), val = tensor([1])]; + tensor var_312_cast_fp16 = expand_dims(axes = var_312_axes_0, x = var_310_cast_fp16)[name = string("op_312_cast_fp16")]; + tensor var_313_axes_0 = const()[name = string("op_313_axes_0"), val = tensor([2])]; + tensor var_313_cast_fp16 = expand_dims(axes = var_313_axes_0, x = var_312_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor weights_3_cast_fp16 = mul(x = weights_1_cast_fp16, y = var_313_cast_fp16)[name = string("weights_3_cast_fp16")]; + tensor var_317_axes_0 = const()[name = string("op_317_axes_0"), val = tensor([-1])]; + bool var_317_keep_dims_0 = const()[name = string("op_317_keep_dims_0"), val = bool(true)]; + tensor var_317_cast_fp16 = reduce_sum(axes = var_317_axes_0, keep_dims = var_317_keep_dims_0, x = weights_3_cast_fp16)[name = string("op_317_cast_fp16")]; + fp16 var_11_to_fp16 = const()[name = string("op_11_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_318_cast_fp16 = maximum(x = var_317_cast_fp16, y = var_11_to_fp16)[name = string("op_318_cast_fp16")]; + tensor weights_5_cast_fp16 = real_div(x = weights_3_cast_fp16, y = var_318_cast_fp16)[name = string("weights_5_cast_fp16")]; + bool context_1_transpose_x_0 = const()[name = string("context_1_transpose_x_0"), val = bool(false)]; + bool context_1_transpose_y_0 = const()[name = string("context_1_transpose_y_0"), val = bool(false)]; + tensor v_3_cast_fp16 = transpose(perm = v_3_perm_0, x = var_302_cast_fp16)[name = string("transpose_77")]; + tensor context_1_cast_fp16 = matmul(transpose_x = context_1_transpose_x_0, transpose_y = context_1_transpose_y_0, x = weights_5_cast_fp16, y = v_3_cast_fp16)[name = string("context_1_cast_fp16")]; + tensor var_321_perm_0 = const()[name = string("op_321_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_322 = const()[name = string("op_322"), val = tensor([1, 64, 640])]; + tensor var_321_cast_fp16 = transpose(perm = var_321_perm_0, x = context_1_cast_fp16)[name = string("transpose_76")]; + tensor context_3_cast_fp16 = reshape(shape = var_322, x = var_321_cast_fp16)[name = string("context_3_cast_fp16")]; + tensor module_layers_0_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1536000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1945664))))[name = string("module_layers_0_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1947008)))]; + tensor linear_1_cast_fp16 = linear(bias = module_layers_0_self_attn_out_proj_bias_to_fp16, weight = module_layers_0_self_attn_out_proj_weight_to_fp16_quantized, x = context_3_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_3_cast_fp16 = add(x = input_1_cast_fp16, y = linear_1_cast_fp16)[name = string("input_3_cast_fp16")]; + tensor input_5_axes_0 = const()[name = string("input_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm1_weight_to_fp16 = const()[name = string("module_layers_0_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1948352)))]; + tensor module_layers_0_norm1_bias_to_fp16 = const()[name = string("module_layers_0_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1949696)))]; + fp16 var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_5_cast_fp16 = layer_norm(axes = input_5_axes_0, beta = module_layers_0_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm1_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")]; + tensor module_layers_0_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1951040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2770304))))[name = string("module_layers_0_linear1_weight_to_fp16_quantized")]; + tensor module_layers_0_linear1_bias_to_fp16 = const()[name = string("module_layers_0_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2772928)))]; + tensor linear_2_cast_fp16 = linear(bias = module_layers_0_linear1_bias_to_fp16, weight = module_layers_0_linear1_weight_to_fp16_quantized, x = input_5_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor input_9_cast_fp16 = relu(x = linear_2_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_layers_0_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2775552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3594816))))[name = string("module_layers_0_linear2_weight_to_fp16_quantized")]; + tensor module_layers_0_linear2_bias_to_fp16 = const()[name = string("module_layers_0_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3596160)))]; + tensor linear_3_cast_fp16 = linear(bias = module_layers_0_linear2_bias_to_fp16, weight = module_layers_0_linear2_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor input_11_cast_fp16 = add(x = input_5_cast_fp16, y = linear_3_cast_fp16)[name = string("input_11_cast_fp16")]; + tensor x_5_axes_0 = const()[name = string("x_5_axes_0"), val = tensor([-1])]; + tensor module_layers_0_norm2_weight_to_fp16 = const()[name = string("module_layers_0_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3597504)))]; + tensor module_layers_0_norm2_bias_to_fp16 = const()[name = string("module_layers_0_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3598848)))]; + tensor x_5_cast_fp16 = layer_norm(axes = x_5_axes_0, beta = module_layers_0_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_0_norm2_weight_to_fp16, x = input_11_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_layers_1_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3600192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4829056))))[name = string("module_layers_1_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4832960)))]; + tensor linear_4_cast_fp16 = linear(bias = module_layers_1_self_attn_in_proj_bias_to_fp16, weight = module_layers_1_self_attn_in_proj_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_345_split_sizes_0 = const()[name = string("op_345_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_345_axis_0 = const()[name = string("op_345_axis_0"), val = int32(-1)]; + tensor var_345_cast_fp16_0, tensor var_345_cast_fp16_1, tensor var_345_cast_fp16_2 = split(axis = var_345_axis_0, split_sizes = var_345_split_sizes_0, x = linear_4_cast_fp16)[name = string("op_345_cast_fp16")]; + tensor var_348 = const()[name = string("op_348"), val = tensor([1, 64, 4, 160])]; + tensor var_349_cast_fp16 = reshape(shape = var_348, x = var_345_cast_fp16_0)[name = string("op_349_cast_fp16")]; + tensor var_351 = const()[name = string("op_351"), val = tensor([1, 64, 4, 160])]; + tensor var_352_cast_fp16 = reshape(shape = var_351, x = var_345_cast_fp16_1)[name = string("op_352_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 64, 4, 160])]; + tensor var_355_cast_fp16 = reshape(shape = var_354, x = var_345_cast_fp16_2)[name = string("op_355_cast_fp16")]; + tensor v_7_perm_0 = const()[name = string("v_7_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_358_transpose_x_0 = const()[name = string("op_358_transpose_x_0"), val = bool(false)]; + bool var_358_transpose_y_0 = const()[name = string("op_358_transpose_y_0"), val = bool(false)]; + tensor transpose_34_perm_0 = const()[name = string("transpose_34_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_35_perm_0 = const()[name = string("transpose_35_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_35 = transpose(perm = transpose_35_perm_0, x = var_352_cast_fp16)[name = string("transpose_74")]; + tensor transpose_34 = transpose(perm = transpose_34_perm_0, x = var_349_cast_fp16)[name = string("transpose_75")]; + tensor var_358_cast_fp16 = matmul(transpose_x = var_358_transpose_x_0, transpose_y = var_358_transpose_y_0, x = transpose_34, y = transpose_35)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_3_cast_fp16 = mul(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("scores_3_cast_fp16")]; + tensor weights_7_cast_fp16 = softmax(axis = var_24, x = scores_3_cast_fp16)[name = string("weights_7_cast_fp16")]; + tensor weights_9_cast_fp16 = mul(x = weights_7_cast_fp16, y = var_313_cast_fp16)[name = string("weights_9_cast_fp16")]; + tensor var_370_axes_0 = const()[name = string("op_370_axes_0"), val = tensor([-1])]; + bool var_370_keep_dims_0 = const()[name = string("op_370_keep_dims_0"), val = bool(true)]; + tensor var_370_cast_fp16 = reduce_sum(axes = var_370_axes_0, keep_dims = var_370_keep_dims_0, x = weights_9_cast_fp16)[name = string("op_370_cast_fp16")]; + tensor var_371_cast_fp16 = maximum(x = var_370_cast_fp16, y = var_11_to_fp16)[name = string("op_371_cast_fp16")]; + tensor weights_11_cast_fp16 = real_div(x = weights_9_cast_fp16, y = var_371_cast_fp16)[name = string("weights_11_cast_fp16")]; + bool context_5_transpose_x_0 = const()[name = string("context_5_transpose_x_0"), val = bool(false)]; + bool context_5_transpose_y_0 = const()[name = string("context_5_transpose_y_0"), val = bool(false)]; + tensor v_7_cast_fp16 = transpose(perm = v_7_perm_0, x = var_355_cast_fp16)[name = string("transpose_73")]; + tensor context_5_cast_fp16 = matmul(transpose_x = context_5_transpose_x_0, transpose_y = context_5_transpose_y_0, x = weights_11_cast_fp16, y = v_7_cast_fp16)[name = string("context_5_cast_fp16")]; + tensor var_374_perm_0 = const()[name = string("op_374_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_375 = const()[name = string("op_375"), val = tensor([1, 64, 640])]; + tensor var_374_cast_fp16 = transpose(perm = var_374_perm_0, x = context_5_cast_fp16)[name = string("transpose_72")]; + tensor context_7_cast_fp16 = reshape(shape = var_375, x = var_374_cast_fp16)[name = string("context_7_cast_fp16")]; + tensor module_layers_1_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4836864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5246528))))[name = string("module_layers_1_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5247872)))]; + tensor linear_5_cast_fp16 = linear(bias = module_layers_1_self_attn_out_proj_bias_to_fp16, weight = module_layers_1_self_attn_out_proj_weight_to_fp16_quantized, x = context_7_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = x_5_cast_fp16, y = linear_5_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm1_weight_to_fp16 = const()[name = string("module_layers_1_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5249216)))]; + tensor module_layers_1_norm1_bias_to_fp16 = const()[name = string("module_layers_1_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5250560)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_layers_1_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm1_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_layers_1_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5251904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6071168))))[name = string("module_layers_1_linear1_weight_to_fp16_quantized")]; + tensor module_layers_1_linear1_bias_to_fp16 = const()[name = string("module_layers_1_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6073792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_layers_1_linear1_bias_to_fp16, weight = module_layers_1_linear1_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor input_19_cast_fp16 = relu(x = linear_6_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_layers_1_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6076416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6895680))))[name = string("module_layers_1_linear2_weight_to_fp16_quantized")]; + tensor module_layers_1_linear2_bias_to_fp16 = const()[name = string("module_layers_1_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6897024)))]; + tensor linear_7_cast_fp16 = linear(bias = module_layers_1_linear2_bias_to_fp16, weight = module_layers_1_linear2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor input_21_cast_fp16 = add(x = input_15_cast_fp16, y = linear_7_cast_fp16)[name = string("input_21_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor module_layers_1_norm2_weight_to_fp16 = const()[name = string("module_layers_1_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6898368)))]; + tensor module_layers_1_norm2_bias_to_fp16 = const()[name = string("module_layers_1_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6899712)))]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, beta = module_layers_1_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_1_norm2_weight_to_fp16, x = input_21_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor module_layers_2_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6901056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8129920))))[name = string("module_layers_2_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8133824)))]; + tensor linear_8_cast_fp16 = linear(bias = module_layers_2_self_attn_in_proj_bias_to_fp16, weight = module_layers_2_self_attn_in_proj_weight_to_fp16_quantized, x = x_7_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_398_split_sizes_0 = const()[name = string("op_398_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_398_axis_0 = const()[name = string("op_398_axis_0"), val = int32(-1)]; + tensor var_398_cast_fp16_0, tensor var_398_cast_fp16_1, tensor var_398_cast_fp16_2 = split(axis = var_398_axis_0, split_sizes = var_398_split_sizes_0, x = linear_8_cast_fp16)[name = string("op_398_cast_fp16")]; + tensor var_401 = const()[name = string("op_401"), val = tensor([1, 64, 4, 160])]; + tensor var_402_cast_fp16 = reshape(shape = var_401, x = var_398_cast_fp16_0)[name = string("op_402_cast_fp16")]; + tensor var_404 = const()[name = string("op_404"), val = tensor([1, 64, 4, 160])]; + tensor var_405_cast_fp16 = reshape(shape = var_404, x = var_398_cast_fp16_1)[name = string("op_405_cast_fp16")]; + tensor var_407 = const()[name = string("op_407"), val = tensor([1, 64, 4, 160])]; + tensor var_408_cast_fp16 = reshape(shape = var_407, x = var_398_cast_fp16_2)[name = string("op_408_cast_fp16")]; + tensor v_11_perm_0 = const()[name = string("v_11_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_411_transpose_x_0 = const()[name = string("op_411_transpose_x_0"), val = bool(false)]; + bool var_411_transpose_y_0 = const()[name = string("op_411_transpose_y_0"), val = bool(false)]; + tensor transpose_36_perm_0 = const()[name = string("transpose_36_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_37_perm_0 = const()[name = string("transpose_37_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_37 = transpose(perm = transpose_37_perm_0, x = var_405_cast_fp16)[name = string("transpose_70")]; + tensor transpose_36 = transpose(perm = transpose_36_perm_0, x = var_402_cast_fp16)[name = string("transpose_71")]; + tensor var_411_cast_fp16 = matmul(transpose_x = var_411_transpose_x_0, transpose_y = var_411_transpose_y_0, x = transpose_36, y = transpose_37)[name = string("op_411_cast_fp16")]; + fp16 var_412_to_fp16 = const()[name = string("op_412_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_5_cast_fp16 = mul(x = var_411_cast_fp16, y = var_412_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor weights_13_cast_fp16 = softmax(axis = var_24, x = scores_5_cast_fp16)[name = string("weights_13_cast_fp16")]; + tensor weights_15_cast_fp16 = mul(x = weights_13_cast_fp16, y = var_313_cast_fp16)[name = string("weights_15_cast_fp16")]; + tensor var_423_axes_0 = const()[name = string("op_423_axes_0"), val = tensor([-1])]; + bool var_423_keep_dims_0 = const()[name = string("op_423_keep_dims_0"), val = bool(true)]; + tensor var_423_cast_fp16 = reduce_sum(axes = var_423_axes_0, keep_dims = var_423_keep_dims_0, x = weights_15_cast_fp16)[name = string("op_423_cast_fp16")]; + tensor var_424_cast_fp16 = maximum(x = var_423_cast_fp16, y = var_11_to_fp16)[name = string("op_424_cast_fp16")]; + tensor weights_17_cast_fp16 = real_div(x = weights_15_cast_fp16, y = var_424_cast_fp16)[name = string("weights_17_cast_fp16")]; + bool context_9_transpose_x_0 = const()[name = string("context_9_transpose_x_0"), val = bool(false)]; + bool context_9_transpose_y_0 = const()[name = string("context_9_transpose_y_0"), val = bool(false)]; + tensor v_11_cast_fp16 = transpose(perm = v_11_perm_0, x = var_408_cast_fp16)[name = string("transpose_69")]; + tensor context_9_cast_fp16 = matmul(transpose_x = context_9_transpose_x_0, transpose_y = context_9_transpose_y_0, x = weights_17_cast_fp16, y = v_11_cast_fp16)[name = string("context_9_cast_fp16")]; + tensor var_427_perm_0 = const()[name = string("op_427_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_428 = const()[name = string("op_428"), val = tensor([1, 64, 640])]; + tensor var_427_cast_fp16 = transpose(perm = var_427_perm_0, x = context_9_cast_fp16)[name = string("transpose_68")]; + tensor context_11_cast_fp16 = reshape(shape = var_428, x = var_427_cast_fp16)[name = string("context_11_cast_fp16")]; + tensor module_layers_2_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8137728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8547392))))[name = string("module_layers_2_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8548736)))]; + tensor linear_9_cast_fp16 = linear(bias = module_layers_2_self_attn_out_proj_bias_to_fp16, weight = module_layers_2_self_attn_out_proj_weight_to_fp16_quantized, x = context_11_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = x_7_cast_fp16, y = linear_9_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor input_25_axes_0 = const()[name = string("input_25_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm1_weight_to_fp16 = const()[name = string("module_layers_2_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8550080)))]; + tensor module_layers_2_norm1_bias_to_fp16 = const()[name = string("module_layers_2_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8551424)))]; + tensor input_25_cast_fp16 = layer_norm(axes = input_25_axes_0, beta = module_layers_2_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm1_weight_to_fp16, x = input_23_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_layers_2_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8552768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9372032))))[name = string("module_layers_2_linear1_weight_to_fp16_quantized")]; + tensor module_layers_2_linear1_bias_to_fp16 = const()[name = string("module_layers_2_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9374656)))]; + tensor linear_10_cast_fp16 = linear(bias = module_layers_2_linear1_bias_to_fp16, weight = module_layers_2_linear1_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor input_29_cast_fp16 = relu(x = linear_10_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor module_layers_2_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9377280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10196544))))[name = string("module_layers_2_linear2_weight_to_fp16_quantized")]; + tensor module_layers_2_linear2_bias_to_fp16 = const()[name = string("module_layers_2_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10197888)))]; + tensor linear_11_cast_fp16 = linear(bias = module_layers_2_linear2_bias_to_fp16, weight = module_layers_2_linear2_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor input_31_cast_fp16 = add(x = input_25_cast_fp16, y = linear_11_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor module_layers_2_norm2_weight_to_fp16 = const()[name = string("module_layers_2_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10199232)))]; + tensor module_layers_2_norm2_bias_to_fp16 = const()[name = string("module_layers_2_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10200576)))]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, beta = module_layers_2_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_2_norm2_weight_to_fp16, x = input_31_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor module_layers_3_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10201920))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11430784))))[name = string("module_layers_3_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11434688)))]; + tensor linear_12_cast_fp16 = linear(bias = module_layers_3_self_attn_in_proj_bias_to_fp16, weight = module_layers_3_self_attn_in_proj_weight_to_fp16_quantized, x = x_9_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_451_split_sizes_0 = const()[name = string("op_451_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_451_axis_0 = const()[name = string("op_451_axis_0"), val = int32(-1)]; + tensor var_451_cast_fp16_0, tensor var_451_cast_fp16_1, tensor var_451_cast_fp16_2 = split(axis = var_451_axis_0, split_sizes = var_451_split_sizes_0, x = linear_12_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor var_454 = const()[name = string("op_454"), val = tensor([1, 64, 4, 160])]; + tensor var_455_cast_fp16 = reshape(shape = var_454, x = var_451_cast_fp16_0)[name = string("op_455_cast_fp16")]; + tensor var_457 = const()[name = string("op_457"), val = tensor([1, 64, 4, 160])]; + tensor var_458_cast_fp16 = reshape(shape = var_457, x = var_451_cast_fp16_1)[name = string("op_458_cast_fp16")]; + tensor var_460 = const()[name = string("op_460"), val = tensor([1, 64, 4, 160])]; + tensor var_461_cast_fp16 = reshape(shape = var_460, x = var_451_cast_fp16_2)[name = string("op_461_cast_fp16")]; + tensor v_15_perm_0 = const()[name = string("v_15_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_464_transpose_x_0 = const()[name = string("op_464_transpose_x_0"), val = bool(false)]; + bool var_464_transpose_y_0 = const()[name = string("op_464_transpose_y_0"), val = bool(false)]; + tensor transpose_38_perm_0 = const()[name = string("transpose_38_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_39_perm_0 = const()[name = string("transpose_39_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_39 = transpose(perm = transpose_39_perm_0, x = var_458_cast_fp16)[name = string("transpose_66")]; + tensor transpose_38 = transpose(perm = transpose_38_perm_0, x = var_455_cast_fp16)[name = string("transpose_67")]; + tensor var_464_cast_fp16 = matmul(transpose_x = var_464_transpose_x_0, transpose_y = var_464_transpose_y_0, x = transpose_38, y = transpose_39)[name = string("op_464_cast_fp16")]; + fp16 var_465_to_fp16 = const()[name = string("op_465_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_7_cast_fp16 = mul(x = var_464_cast_fp16, y = var_465_to_fp16)[name = string("scores_7_cast_fp16")]; + tensor weights_19_cast_fp16 = softmax(axis = var_24, x = scores_7_cast_fp16)[name = string("weights_19_cast_fp16")]; + tensor weights_21_cast_fp16 = mul(x = weights_19_cast_fp16, y = var_313_cast_fp16)[name = string("weights_21_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + bool var_476_keep_dims_0 = const()[name = string("op_476_keep_dims_0"), val = bool(true)]; + tensor var_476_cast_fp16 = reduce_sum(axes = var_476_axes_0, keep_dims = var_476_keep_dims_0, x = weights_21_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor var_477_cast_fp16 = maximum(x = var_476_cast_fp16, y = var_11_to_fp16)[name = string("op_477_cast_fp16")]; + tensor weights_23_cast_fp16 = real_div(x = weights_21_cast_fp16, y = var_477_cast_fp16)[name = string("weights_23_cast_fp16")]; + bool context_13_transpose_x_0 = const()[name = string("context_13_transpose_x_0"), val = bool(false)]; + bool context_13_transpose_y_0 = const()[name = string("context_13_transpose_y_0"), val = bool(false)]; + tensor v_15_cast_fp16 = transpose(perm = v_15_perm_0, x = var_461_cast_fp16)[name = string("transpose_65")]; + tensor context_13_cast_fp16 = matmul(transpose_x = context_13_transpose_x_0, transpose_y = context_13_transpose_y_0, x = weights_23_cast_fp16, y = v_15_cast_fp16)[name = string("context_13_cast_fp16")]; + tensor var_480_perm_0 = const()[name = string("op_480_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_481 = const()[name = string("op_481"), val = tensor([1, 64, 640])]; + tensor var_480_cast_fp16 = transpose(perm = var_480_perm_0, x = context_13_cast_fp16)[name = string("transpose_64")]; + tensor context_15_cast_fp16 = reshape(shape = var_481, x = var_480_cast_fp16)[name = string("context_15_cast_fp16")]; + tensor module_layers_3_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11438592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11848256))))[name = string("module_layers_3_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11849600)))]; + tensor linear_13_cast_fp16 = linear(bias = module_layers_3_self_attn_out_proj_bias_to_fp16, weight = module_layers_3_self_attn_out_proj_weight_to_fp16_quantized, x = context_15_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = x_9_cast_fp16, y = linear_13_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor input_35_axes_0 = const()[name = string("input_35_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm1_weight_to_fp16 = const()[name = string("module_layers_3_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11850944)))]; + tensor module_layers_3_norm1_bias_to_fp16 = const()[name = string("module_layers_3_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11852288)))]; + tensor input_35_cast_fp16 = layer_norm(axes = input_35_axes_0, beta = module_layers_3_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm1_weight_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_layers_3_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11853632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12672896))))[name = string("module_layers_3_linear1_weight_to_fp16_quantized")]; + tensor module_layers_3_linear1_bias_to_fp16 = const()[name = string("module_layers_3_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12675520)))]; + tensor linear_14_cast_fp16 = linear(bias = module_layers_3_linear1_bias_to_fp16, weight = module_layers_3_linear1_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor input_39_cast_fp16 = relu(x = linear_14_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_layers_3_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12678144))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13497408))))[name = string("module_layers_3_linear2_weight_to_fp16_quantized")]; + tensor module_layers_3_linear2_bias_to_fp16 = const()[name = string("module_layers_3_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13498752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_layers_3_linear2_bias_to_fp16, weight = module_layers_3_linear2_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = input_35_cast_fp16, y = linear_15_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor x_11_axes_0 = const()[name = string("x_11_axes_0"), val = tensor([-1])]; + tensor module_layers_3_norm2_weight_to_fp16 = const()[name = string("module_layers_3_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13500096)))]; + tensor module_layers_3_norm2_bias_to_fp16 = const()[name = string("module_layers_3_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13501440)))]; + tensor x_11_cast_fp16 = layer_norm(axes = x_11_axes_0, beta = module_layers_3_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_3_norm2_weight_to_fp16, x = input_41_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_layers_4_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13502784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14731648))))[name = string("module_layers_4_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14735552)))]; + tensor linear_16_cast_fp16 = linear(bias = module_layers_4_self_attn_in_proj_bias_to_fp16, weight = module_layers_4_self_attn_in_proj_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_504_split_sizes_0 = const()[name = string("op_504_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_504_axis_0 = const()[name = string("op_504_axis_0"), val = int32(-1)]; + tensor var_504_cast_fp16_0, tensor var_504_cast_fp16_1, tensor var_504_cast_fp16_2 = split(axis = var_504_axis_0, split_sizes = var_504_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_504_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 64, 4, 160])]; + tensor var_508_cast_fp16 = reshape(shape = var_507, x = var_504_cast_fp16_0)[name = string("op_508_cast_fp16")]; + tensor var_510 = const()[name = string("op_510"), val = tensor([1, 64, 4, 160])]; + tensor var_511_cast_fp16 = reshape(shape = var_510, x = var_504_cast_fp16_1)[name = string("op_511_cast_fp16")]; + tensor var_513 = const()[name = string("op_513"), val = tensor([1, 64, 4, 160])]; + tensor var_514_cast_fp16 = reshape(shape = var_513, x = var_504_cast_fp16_2)[name = string("op_514_cast_fp16")]; + tensor v_19_perm_0 = const()[name = string("v_19_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_517_transpose_x_0 = const()[name = string("op_517_transpose_x_0"), val = bool(false)]; + bool var_517_transpose_y_0 = const()[name = string("op_517_transpose_y_0"), val = bool(false)]; + tensor transpose_40_perm_0 = const()[name = string("transpose_40_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_41_perm_0 = const()[name = string("transpose_41_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_41 = transpose(perm = transpose_41_perm_0, x = var_511_cast_fp16)[name = string("transpose_62")]; + tensor transpose_40 = transpose(perm = transpose_40_perm_0, x = var_508_cast_fp16)[name = string("transpose_63")]; + tensor var_517_cast_fp16 = matmul(transpose_x = var_517_transpose_x_0, transpose_y = var_517_transpose_y_0, x = transpose_40, y = transpose_41)[name = string("op_517_cast_fp16")]; + fp16 var_518_to_fp16 = const()[name = string("op_518_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_9_cast_fp16 = mul(x = var_517_cast_fp16, y = var_518_to_fp16)[name = string("scores_9_cast_fp16")]; + tensor weights_25_cast_fp16 = softmax(axis = var_24, x = scores_9_cast_fp16)[name = string("weights_25_cast_fp16")]; + tensor weights_27_cast_fp16 = mul(x = weights_25_cast_fp16, y = var_313_cast_fp16)[name = string("weights_27_cast_fp16")]; + tensor var_529_axes_0 = const()[name = string("op_529_axes_0"), val = tensor([-1])]; + bool var_529_keep_dims_0 = const()[name = string("op_529_keep_dims_0"), val = bool(true)]; + tensor var_529_cast_fp16 = reduce_sum(axes = var_529_axes_0, keep_dims = var_529_keep_dims_0, x = weights_27_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = maximum(x = var_529_cast_fp16, y = var_11_to_fp16)[name = string("op_530_cast_fp16")]; + tensor weights_29_cast_fp16 = real_div(x = weights_27_cast_fp16, y = var_530_cast_fp16)[name = string("weights_29_cast_fp16")]; + bool context_17_transpose_x_0 = const()[name = string("context_17_transpose_x_0"), val = bool(false)]; + bool context_17_transpose_y_0 = const()[name = string("context_17_transpose_y_0"), val = bool(false)]; + tensor v_19_cast_fp16 = transpose(perm = v_19_perm_0, x = var_514_cast_fp16)[name = string("transpose_61")]; + tensor context_17_cast_fp16 = matmul(transpose_x = context_17_transpose_x_0, transpose_y = context_17_transpose_y_0, x = weights_29_cast_fp16, y = v_19_cast_fp16)[name = string("context_17_cast_fp16")]; + tensor var_533_perm_0 = const()[name = string("op_533_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_534 = const()[name = string("op_534"), val = tensor([1, 64, 640])]; + tensor var_533_cast_fp16 = transpose(perm = var_533_perm_0, x = context_17_cast_fp16)[name = string("transpose_60")]; + tensor context_19_cast_fp16 = reshape(shape = var_534, x = var_533_cast_fp16)[name = string("context_19_cast_fp16")]; + tensor module_layers_4_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14739456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15149120))))[name = string("module_layers_4_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15150464)))]; + tensor linear_17_cast_fp16 = linear(bias = module_layers_4_self_attn_out_proj_bias_to_fp16, weight = module_layers_4_self_attn_out_proj_weight_to_fp16_quantized, x = context_19_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = x_11_cast_fp16, y = linear_17_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor input_45_axes_0 = const()[name = string("input_45_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm1_weight_to_fp16 = const()[name = string("module_layers_4_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15151808)))]; + tensor module_layers_4_norm1_bias_to_fp16 = const()[name = string("module_layers_4_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15153152)))]; + tensor input_45_cast_fp16 = layer_norm(axes = input_45_axes_0, beta = module_layers_4_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm1_weight_to_fp16, x = input_43_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_layers_4_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15154496))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15973760))))[name = string("module_layers_4_linear1_weight_to_fp16_quantized")]; + tensor module_layers_4_linear1_bias_to_fp16 = const()[name = string("module_layers_4_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15976384)))]; + tensor linear_18_cast_fp16 = linear(bias = module_layers_4_linear1_bias_to_fp16, weight = module_layers_4_linear1_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor input_49_cast_fp16 = relu(x = linear_18_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_layers_4_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15979008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16798272))))[name = string("module_layers_4_linear2_weight_to_fp16_quantized")]; + tensor module_layers_4_linear2_bias_to_fp16 = const()[name = string("module_layers_4_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16799616)))]; + tensor linear_19_cast_fp16 = linear(bias = module_layers_4_linear2_bias_to_fp16, weight = module_layers_4_linear2_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = input_45_cast_fp16, y = linear_19_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor module_layers_4_norm2_weight_to_fp16 = const()[name = string("module_layers_4_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16800960)))]; + tensor module_layers_4_norm2_bias_to_fp16 = const()[name = string("module_layers_4_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16802304)))]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, beta = module_layers_4_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_4_norm2_weight_to_fp16, x = input_51_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor module_layers_5_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16803648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18032512))))[name = string("module_layers_5_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18036416)))]; + tensor linear_20_cast_fp16 = linear(bias = module_layers_5_self_attn_in_proj_bias_to_fp16, weight = module_layers_5_self_attn_in_proj_weight_to_fp16_quantized, x = x_13_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_557_split_sizes_0 = const()[name = string("op_557_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_557_axis_0 = const()[name = string("op_557_axis_0"), val = int32(-1)]; + tensor var_557_cast_fp16_0, tensor var_557_cast_fp16_1, tensor var_557_cast_fp16_2 = split(axis = var_557_axis_0, split_sizes = var_557_split_sizes_0, x = linear_20_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_560 = const()[name = string("op_560"), val = tensor([1, 64, 4, 160])]; + tensor var_561_cast_fp16 = reshape(shape = var_560, x = var_557_cast_fp16_0)[name = string("op_561_cast_fp16")]; + tensor var_563 = const()[name = string("op_563"), val = tensor([1, 64, 4, 160])]; + tensor var_564_cast_fp16 = reshape(shape = var_563, x = var_557_cast_fp16_1)[name = string("op_564_cast_fp16")]; + tensor var_566 = const()[name = string("op_566"), val = tensor([1, 64, 4, 160])]; + tensor var_567_cast_fp16 = reshape(shape = var_566, x = var_557_cast_fp16_2)[name = string("op_567_cast_fp16")]; + tensor v_23_perm_0 = const()[name = string("v_23_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_570_transpose_x_0 = const()[name = string("op_570_transpose_x_0"), val = bool(false)]; + bool var_570_transpose_y_0 = const()[name = string("op_570_transpose_y_0"), val = bool(false)]; + tensor transpose_42_perm_0 = const()[name = string("transpose_42_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_43_perm_0 = const()[name = string("transpose_43_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_43 = transpose(perm = transpose_43_perm_0, x = var_564_cast_fp16)[name = string("transpose_58")]; + tensor transpose_42 = transpose(perm = transpose_42_perm_0, x = var_561_cast_fp16)[name = string("transpose_59")]; + tensor var_570_cast_fp16 = matmul(transpose_x = var_570_transpose_x_0, transpose_y = var_570_transpose_y_0, x = transpose_42, y = transpose_43)[name = string("op_570_cast_fp16")]; + fp16 var_571_to_fp16 = const()[name = string("op_571_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_11_cast_fp16 = mul(x = var_570_cast_fp16, y = var_571_to_fp16)[name = string("scores_11_cast_fp16")]; + tensor weights_31_cast_fp16 = softmax(axis = var_24, x = scores_11_cast_fp16)[name = string("weights_31_cast_fp16")]; + tensor weights_33_cast_fp16 = mul(x = weights_31_cast_fp16, y = var_313_cast_fp16)[name = string("weights_33_cast_fp16")]; + tensor var_582_axes_0 = const()[name = string("op_582_axes_0"), val = tensor([-1])]; + bool var_582_keep_dims_0 = const()[name = string("op_582_keep_dims_0"), val = bool(true)]; + tensor var_582_cast_fp16 = reduce_sum(axes = var_582_axes_0, keep_dims = var_582_keep_dims_0, x = weights_33_cast_fp16)[name = string("op_582_cast_fp16")]; + tensor var_583_cast_fp16 = maximum(x = var_582_cast_fp16, y = var_11_to_fp16)[name = string("op_583_cast_fp16")]; + tensor weights_35_cast_fp16 = real_div(x = weights_33_cast_fp16, y = var_583_cast_fp16)[name = string("weights_35_cast_fp16")]; + bool context_21_transpose_x_0 = const()[name = string("context_21_transpose_x_0"), val = bool(false)]; + bool context_21_transpose_y_0 = const()[name = string("context_21_transpose_y_0"), val = bool(false)]; + tensor v_23_cast_fp16 = transpose(perm = v_23_perm_0, x = var_567_cast_fp16)[name = string("transpose_57")]; + tensor context_21_cast_fp16 = matmul(transpose_x = context_21_transpose_x_0, transpose_y = context_21_transpose_y_0, x = weights_35_cast_fp16, y = v_23_cast_fp16)[name = string("context_21_cast_fp16")]; + tensor var_586_perm_0 = const()[name = string("op_586_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_587 = const()[name = string("op_587"), val = tensor([1, 64, 640])]; + tensor var_586_cast_fp16 = transpose(perm = var_586_perm_0, x = context_21_cast_fp16)[name = string("transpose_56")]; + tensor context_23_cast_fp16 = reshape(shape = var_587, x = var_586_cast_fp16)[name = string("context_23_cast_fp16")]; + tensor module_layers_5_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18040320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18449984))))[name = string("module_layers_5_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18451328)))]; + tensor linear_21_cast_fp16 = linear(bias = module_layers_5_self_attn_out_proj_bias_to_fp16, weight = module_layers_5_self_attn_out_proj_weight_to_fp16_quantized, x = context_23_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = x_13_cast_fp16, y = linear_21_cast_fp16)[name = string("input_53_cast_fp16")]; + tensor input_55_axes_0 = const()[name = string("input_55_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm1_weight_to_fp16 = const()[name = string("module_layers_5_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18452672)))]; + tensor module_layers_5_norm1_bias_to_fp16 = const()[name = string("module_layers_5_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18454016)))]; + tensor input_55_cast_fp16 = layer_norm(axes = input_55_axes_0, beta = module_layers_5_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm1_weight_to_fp16, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_layers_5_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18455360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19274624))))[name = string("module_layers_5_linear1_weight_to_fp16_quantized")]; + tensor module_layers_5_linear1_bias_to_fp16 = const()[name = string("module_layers_5_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19277248)))]; + tensor linear_22_cast_fp16 = linear(bias = module_layers_5_linear1_bias_to_fp16, weight = module_layers_5_linear1_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor input_59_cast_fp16 = relu(x = linear_22_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_layers_5_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19279872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20099136))))[name = string("module_layers_5_linear2_weight_to_fp16_quantized")]; + tensor module_layers_5_linear2_bias_to_fp16 = const()[name = string("module_layers_5_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20100480)))]; + tensor linear_23_cast_fp16 = linear(bias = module_layers_5_linear2_bias_to_fp16, weight = module_layers_5_linear2_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_55_cast_fp16, y = linear_23_cast_fp16)[name = string("input_61_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor module_layers_5_norm2_weight_to_fp16 = const()[name = string("module_layers_5_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20101824)))]; + tensor module_layers_5_norm2_bias_to_fp16 = const()[name = string("module_layers_5_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20103168)))]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, beta = module_layers_5_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_5_norm2_weight_to_fp16, x = input_61_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor module_layers_6_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20104512))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21333376))))[name = string("module_layers_6_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21337280)))]; + tensor linear_24_cast_fp16 = linear(bias = module_layers_6_self_attn_in_proj_bias_to_fp16, weight = module_layers_6_self_attn_in_proj_weight_to_fp16_quantized, x = x_15_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_610_split_sizes_0 = const()[name = string("op_610_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_610_axis_0 = const()[name = string("op_610_axis_0"), val = int32(-1)]; + tensor var_610_cast_fp16_0, tensor var_610_cast_fp16_1, tensor var_610_cast_fp16_2 = split(axis = var_610_axis_0, split_sizes = var_610_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_610_cast_fp16")]; + tensor var_613 = const()[name = string("op_613"), val = tensor([1, 64, 4, 160])]; + tensor var_614_cast_fp16 = reshape(shape = var_613, x = var_610_cast_fp16_0)[name = string("op_614_cast_fp16")]; + tensor var_616 = const()[name = string("op_616"), val = tensor([1, 64, 4, 160])]; + tensor var_617_cast_fp16 = reshape(shape = var_616, x = var_610_cast_fp16_1)[name = string("op_617_cast_fp16")]; + tensor var_619 = const()[name = string("op_619"), val = tensor([1, 64, 4, 160])]; + tensor var_620_cast_fp16 = reshape(shape = var_619, x = var_610_cast_fp16_2)[name = string("op_620_cast_fp16")]; + tensor v_27_perm_0 = const()[name = string("v_27_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_623_transpose_x_0 = const()[name = string("op_623_transpose_x_0"), val = bool(false)]; + bool var_623_transpose_y_0 = const()[name = string("op_623_transpose_y_0"), val = bool(false)]; + tensor transpose_44_perm_0 = const()[name = string("transpose_44_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_45_perm_0 = const()[name = string("transpose_45_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_45 = transpose(perm = transpose_45_perm_0, x = var_617_cast_fp16)[name = string("transpose_54")]; + tensor transpose_44 = transpose(perm = transpose_44_perm_0, x = var_614_cast_fp16)[name = string("transpose_55")]; + tensor var_623_cast_fp16 = matmul(transpose_x = var_623_transpose_x_0, transpose_y = var_623_transpose_y_0, x = transpose_44, y = transpose_45)[name = string("op_623_cast_fp16")]; + fp16 var_624_to_fp16 = const()[name = string("op_624_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_13_cast_fp16 = mul(x = var_623_cast_fp16, y = var_624_to_fp16)[name = string("scores_13_cast_fp16")]; + tensor weights_37_cast_fp16 = softmax(axis = var_24, x = scores_13_cast_fp16)[name = string("weights_37_cast_fp16")]; + tensor weights_39_cast_fp16 = mul(x = weights_37_cast_fp16, y = var_313_cast_fp16)[name = string("weights_39_cast_fp16")]; + tensor var_635_axes_0 = const()[name = string("op_635_axes_0"), val = tensor([-1])]; + bool var_635_keep_dims_0 = const()[name = string("op_635_keep_dims_0"), val = bool(true)]; + tensor var_635_cast_fp16 = reduce_sum(axes = var_635_axes_0, keep_dims = var_635_keep_dims_0, x = weights_39_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_cast_fp16 = maximum(x = var_635_cast_fp16, y = var_11_to_fp16)[name = string("op_636_cast_fp16")]; + tensor weights_41_cast_fp16 = real_div(x = weights_39_cast_fp16, y = var_636_cast_fp16)[name = string("weights_41_cast_fp16")]; + bool context_25_transpose_x_0 = const()[name = string("context_25_transpose_x_0"), val = bool(false)]; + bool context_25_transpose_y_0 = const()[name = string("context_25_transpose_y_0"), val = bool(false)]; + tensor v_27_cast_fp16 = transpose(perm = v_27_perm_0, x = var_620_cast_fp16)[name = string("transpose_53")]; + tensor context_25_cast_fp16 = matmul(transpose_x = context_25_transpose_x_0, transpose_y = context_25_transpose_y_0, x = weights_41_cast_fp16, y = v_27_cast_fp16)[name = string("context_25_cast_fp16")]; + tensor var_639_perm_0 = const()[name = string("op_639_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_640 = const()[name = string("op_640"), val = tensor([1, 64, 640])]; + tensor var_639_cast_fp16 = transpose(perm = var_639_perm_0, x = context_25_cast_fp16)[name = string("transpose_52")]; + tensor context_27_cast_fp16 = reshape(shape = var_640, x = var_639_cast_fp16)[name = string("context_27_cast_fp16")]; + tensor module_layers_6_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21341184))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750848))))[name = string("module_layers_6_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21752192)))]; + tensor linear_25_cast_fp16 = linear(bias = module_layers_6_self_attn_out_proj_bias_to_fp16, weight = module_layers_6_self_attn_out_proj_weight_to_fp16_quantized, x = context_27_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = x_15_cast_fp16, y = linear_25_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor input_65_axes_0 = const()[name = string("input_65_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm1_weight_to_fp16 = const()[name = string("module_layers_6_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21753536)))]; + tensor module_layers_6_norm1_bias_to_fp16 = const()[name = string("module_layers_6_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21754880)))]; + tensor input_65_cast_fp16 = layer_norm(axes = input_65_axes_0, beta = module_layers_6_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm1_weight_to_fp16, x = input_63_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_layers_6_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21756224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22575488))))[name = string("module_layers_6_linear1_weight_to_fp16_quantized")]; + tensor module_layers_6_linear1_bias_to_fp16 = const()[name = string("module_layers_6_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22578112)))]; + tensor linear_26_cast_fp16 = linear(bias = module_layers_6_linear1_bias_to_fp16, weight = module_layers_6_linear1_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor input_69_cast_fp16 = relu(x = linear_26_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_layers_6_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22580736))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23400000))))[name = string("module_layers_6_linear2_weight_to_fp16_quantized")]; + tensor module_layers_6_linear2_bias_to_fp16 = const()[name = string("module_layers_6_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23401344)))]; + tensor linear_27_cast_fp16 = linear(bias = module_layers_6_linear2_bias_to_fp16, weight = module_layers_6_linear2_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = input_65_cast_fp16, y = linear_27_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor module_layers_6_norm2_weight_to_fp16 = const()[name = string("module_layers_6_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23402688)))]; + tensor module_layers_6_norm2_bias_to_fp16 = const()[name = string("module_layers_6_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23404032)))]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, beta = module_layers_6_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_6_norm2_weight_to_fp16, x = input_71_cast_fp16)[name = string("x_cast_fp16")]; + tensor module_layers_7_self_attn_in_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23405376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24634240))))[name = string("module_layers_7_self_attn_in_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_in_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_in_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24638144)))]; + tensor linear_28_cast_fp16 = linear(bias = module_layers_7_self_attn_in_proj_bias_to_fp16, weight = module_layers_7_self_attn_in_proj_weight_to_fp16_quantized, x = x_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_663_split_sizes_0 = const()[name = string("op_663_split_sizes_0"), val = tensor([640, 640, 640])]; + int32 var_663_axis_0 = const()[name = string("op_663_axis_0"), val = int32(-1)]; + tensor var_663_cast_fp16_0, tensor var_663_cast_fp16_1, tensor var_663_cast_fp16_2 = split(axis = var_663_axis_0, split_sizes = var_663_split_sizes_0, x = linear_28_cast_fp16)[name = string("op_663_cast_fp16")]; + tensor var_666 = const()[name = string("op_666"), val = tensor([1, 64, 4, 160])]; + tensor var_667_cast_fp16 = reshape(shape = var_666, x = var_663_cast_fp16_0)[name = string("op_667_cast_fp16")]; + tensor var_669 = const()[name = string("op_669"), val = tensor([1, 64, 4, 160])]; + tensor var_670_cast_fp16 = reshape(shape = var_669, x = var_663_cast_fp16_1)[name = string("op_670_cast_fp16")]; + tensor var_672 = const()[name = string("op_672"), val = tensor([1, 64, 4, 160])]; + tensor var_673_cast_fp16 = reshape(shape = var_672, x = var_663_cast_fp16_2)[name = string("op_673_cast_fp16")]; + tensor v_perm_0 = const()[name = string("v_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_676_transpose_x_0 = const()[name = string("op_676_transpose_x_0"), val = bool(false)]; + bool var_676_transpose_y_0 = const()[name = string("op_676_transpose_y_0"), val = bool(false)]; + tensor transpose_46_perm_0 = const()[name = string("transpose_46_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_47_perm_0 = const()[name = string("transpose_47_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_47 = transpose(perm = transpose_47_perm_0, x = var_670_cast_fp16)[name = string("transpose_50")]; + tensor transpose_46 = transpose(perm = transpose_46_perm_0, x = var_667_cast_fp16)[name = string("transpose_51")]; + tensor var_676_cast_fp16 = matmul(transpose_x = var_676_transpose_x_0, transpose_y = var_676_transpose_y_0, x = transpose_46, y = transpose_47)[name = string("op_676_cast_fp16")]; + fp16 var_677_to_fp16 = const()[name = string("op_677_to_fp16"), val = fp16(0x1.43cp-4)]; + tensor scores_cast_fp16 = mul(x = var_676_cast_fp16, y = var_677_to_fp16)[name = string("scores_cast_fp16")]; + tensor weights_43_cast_fp16 = softmax(axis = var_24, x = scores_cast_fp16)[name = string("weights_43_cast_fp16")]; + tensor weights_45_cast_fp16 = mul(x = weights_43_cast_fp16, y = var_313_cast_fp16)[name = string("weights_45_cast_fp16")]; + tensor var_688_axes_0 = const()[name = string("op_688_axes_0"), val = tensor([-1])]; + bool var_688_keep_dims_0 = const()[name = string("op_688_keep_dims_0"), val = bool(true)]; + tensor var_688_cast_fp16 = reduce_sum(axes = var_688_axes_0, keep_dims = var_688_keep_dims_0, x = weights_45_cast_fp16)[name = string("op_688_cast_fp16")]; + tensor var_689_cast_fp16 = maximum(x = var_688_cast_fp16, y = var_11_to_fp16)[name = string("op_689_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_45_cast_fp16, y = var_689_cast_fp16)[name = string("weights_cast_fp16")]; + bool context_29_transpose_x_0 = const()[name = string("context_29_transpose_x_0"), val = bool(false)]; + bool context_29_transpose_y_0 = const()[name = string("context_29_transpose_y_0"), val = bool(false)]; + tensor v_cast_fp16 = transpose(perm = v_perm_0, x = var_673_cast_fp16)[name = string("transpose_49")]; + tensor context_29_cast_fp16 = matmul(transpose_x = context_29_transpose_x_0, transpose_y = context_29_transpose_y_0, x = weights_cast_fp16, y = v_cast_fp16)[name = string("context_29_cast_fp16")]; + tensor var_692_perm_0 = const()[name = string("op_692_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_693 = const()[name = string("op_693"), val = tensor([1, 64, 640])]; + tensor var_692_cast_fp16 = transpose(perm = var_692_perm_0, x = context_29_cast_fp16)[name = string("transpose_48")]; + tensor context_cast_fp16 = reshape(shape = var_693, x = var_692_cast_fp16)[name = string("context_cast_fp16")]; + tensor module_layers_7_self_attn_out_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24642048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25051712))))[name = string("module_layers_7_self_attn_out_proj_weight_to_fp16_quantized")]; + tensor module_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = string("module_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25053056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_layers_7_self_attn_out_proj_bias_to_fp16, weight = module_layers_7_self_attn_out_proj_weight_to_fp16_quantized, x = context_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor input_73_cast_fp16 = add(x = x_cast_fp16, y = linear_29_cast_fp16)[name = string("input_73_cast_fp16")]; + tensor input_75_axes_0 = const()[name = string("input_75_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm1_weight_to_fp16 = const()[name = string("module_layers_7_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25054400)))]; + tensor module_layers_7_norm1_bias_to_fp16 = const()[name = string("module_layers_7_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25055744)))]; + tensor input_75_cast_fp16 = layer_norm(axes = input_75_axes_0, beta = module_layers_7_norm1_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm1_weight_to_fp16, x = input_73_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_layers_7_linear1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25057088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25876352))))[name = string("module_layers_7_linear1_weight_to_fp16_quantized")]; + tensor module_layers_7_linear1_bias_to_fp16 = const()[name = string("module_layers_7_linear1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25878976)))]; + tensor linear_30_cast_fp16 = linear(bias = module_layers_7_linear1_bias_to_fp16, weight = module_layers_7_linear1_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor input_79_cast_fp16 = relu(x = linear_30_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_layers_7_linear2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25881600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26700864))))[name = string("module_layers_7_linear2_weight_to_fp16_quantized")]; + tensor module_layers_7_linear2_bias_to_fp16 = const()[name = string("module_layers_7_linear2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26702208)))]; + tensor linear_31_cast_fp16 = linear(bias = module_layers_7_linear2_bias_to_fp16, weight = module_layers_7_linear2_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor input_81_cast_fp16 = add(x = input_75_cast_fp16, y = linear_31_cast_fp16)[name = string("input_81_cast_fp16")]; + tensor input_83_axes_0 = const()[name = string("input_83_axes_0"), val = tensor([-1])]; + tensor module_layers_7_norm2_weight_to_fp16 = const()[name = string("module_layers_7_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26703552)))]; + tensor module_layers_7_norm2_bias_to_fp16 = const()[name = string("module_layers_7_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26704896)))]; + tensor input_83_cast_fp16 = layer_norm(axes = input_83_axes_0, beta = module_layers_7_norm2_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_layers_7_norm2_weight_to_fp16, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([-1])]; + tensor module_encoder_norm_weight_to_fp16 = const()[name = string("module_encoder_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26706240)))]; + tensor module_encoder_norm_bias_to_fp16 = const()[name = string("module_encoder_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26707584)))]; + tensor input_cast_fp16 = layer_norm(axes = input_axes_0, beta = module_encoder_norm_bias_to_fp16, epsilon = var_9_to_fp16, gamma = module_encoder_norm_weight_to_fp16, x = input_83_cast_fp16)[name = string("input_cast_fp16")]; + tensor module_fc_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26708928))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770432))))[name = string("module_fc_out_weight_to_fp16_quantized")]; + tensor module_fc_out_bias_to_fp16 = const()[name = string("module_fc_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770688)))]; + tensor linear_32_cast_fp16 = linear(bias = module_fc_out_bias_to_fp16, weight = module_fc_out_weight_to_fp16_quantized, x = input_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor padded_axes_0 = const()[name = string("padded_axes_0"), val = tensor([-1])]; + tensor padded = expand_dims(axes = padded_axes_0, x = src_pad_mask)[name = string("padded")]; + tensor var_723_begin_0 = const()[name = string("op_723_begin_0"), val = tensor([0, 0, 0])]; + tensor var_723_end_0 = const()[name = string("op_723_end_0"), val = tensor([1, 64, 1])]; + tensor var_723_end_mask_0 = const()[name = string("op_723_end_mask_0"), val = tensor([true, true, false])]; + tensor var_723_cast_fp16 = slice_by_index(begin = var_723_begin_0, end = var_723_end_0, end_mask = var_723_end_mask_0, x = linear_32_cast_fp16)[name = string("op_723_cast_fp16")]; + tensor var_722_to_fp16 = const()[name = string("op_722_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26770944)))]; + tensor blank_logits_cast_fp16 = select(a = var_722_to_fp16, b = var_723_cast_fp16, cond = padded)[name = string("blank_logits_cast_fp16")]; + tensor var_725_begin_0 = const()[name = string("op_725_begin_0"), val = tensor([0, 0, 1])]; + tensor var_725_end_0 = const()[name = string("op_725_end_0"), val = tensor([1, 64, 96])]; + tensor var_725_end_mask_0 = const()[name = string("op_725_end_mask_0"), val = tensor([true, true, true])]; + tensor var_725_cast_fp16 = slice_by_index(begin = var_725_begin_0, end = var_725_end_0, end_mask = var_725_end_mask_0, x = linear_32_cast_fp16)[name = string("op_725_cast_fp16")]; + fp16 var_726_value_0_to_fp16 = const()[name = string("op_726_value_0_to_fp16"), val = fp16(-0x1.388p+13)]; + tensor var_726_cast_fp16 = fill_like(ref_tensor = var_725_cast_fp16, value = var_726_value_0_to_fp16)[name = string("op_726_cast_fp16")]; + tensor padded_after_broadcast_reps_0 = const()[name = string("padded_after_broadcast_reps_0"), val = tensor([1, 1, 95])]; + tensor padded_after_broadcast = tile(reps = padded_after_broadcast_reps_0, x = padded)[name = string("padded_after_broadcast")]; + tensor nonblank_logits_cast_fp16 = select(a = var_726_cast_fp16, b = var_725_cast_fp16, cond = padded_after_broadcast)[name = string("nonblank_logits_cast_fp16")]; + bool var_730_interleave_0 = const()[name = string("op_730_interleave_0"), val = bool(false)]; + tensor var_730_cast_fp16 = concat(axis = var_24, interleave = var_730_interleave_0, values = (blank_logits_cast_fp16, nonblank_logits_cast_fp16))[name = string("op_730_cast_fp16")]; + string var_730_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_730_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor logits = cast(dtype = var_730_cast_fp16_to_fp32_dtype_0, x = var_730_cast_fp16)[name = string("cast_1")]; + } -> (logits); +} \ No newline at end of file diff --git a/coreml/coreml/g2p.mlmodelc/weights/weight.bin b/coreml/coreml/g2p.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..dd5c633871d0edfb7a0f555b71498a0a2b9ac49b --- /dev/null +++ b/coreml/coreml/g2p.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:045da2991ed2a42ba027aaa6b83813054f410b6eedfdce82f2ab287ec4ca74e2 +size 27346752 diff --git a/coreml/coreml/pitch_context_encoder.mlmodelc/analytics/coremldata.bin b/coreml/coreml/pitch_context_encoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..7c2f1ebde89c66ba3baed9c6254b70fb9d3b8be5 --- /dev/null +++ b/coreml/coreml/pitch_context_encoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2250c452f034a8c339b121dc184ff3447dd105db0368e668a3c167612b979bd2 +size 243 diff --git a/coreml/coreml/pitch_context_encoder.mlmodelc/coremldata.bin b/coreml/coreml/pitch_context_encoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..306013aac4579bc64d4c093d58345721e6b12215 --- /dev/null +++ b/coreml/coreml/pitch_context_encoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f8c2d1d9908c394fdf3afe5729f29bba8f53f8c76cbf4c428467ec0cc2023739 +size 476 diff --git a/coreml/coreml/pitch_context_encoder.mlmodelc/model.mil b/coreml/coreml/pitch_context_encoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..1a31b7795da79221a08d4a71f1a375380b21873a --- /dev/null +++ b/coreml/coreml/pitch_context_encoder.mlmodelc/model.mil @@ -0,0 +1,267 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor span_context_mask, tensor span_context_phone_ids, tensor span_context_segment_ids) { + int32 var_15 = const()[name = string("op_15"), val = int32(0)]; + tensor span_context_mask_1 = not_equal(x = span_context_mask, y = var_15)[name = string("span_context_mask")]; + int32 var_30 = const()[name = string("op_30"), val = int32(-1)]; + int32 var_35 = const()[name = string("op_35"), val = int32(2)]; + int32 var_36 = const()[name = string("op_36"), val = int32(86)]; + int32 var_38 = const()[name = string("op_38"), val = int32(1)]; + int32 var_40 = const()[name = string("op_40"), val = int32(0)]; + tensor var_55 = maximum(x = span_context_phone_ids, y = var_40)[name = string("op_55")]; + tensor input_1 = minimum(x = var_55, y = var_36)[name = string("input_1")]; + tensor var_59 = maximum(x = span_context_segment_ids, y = var_40)[name = string("op_59")]; + tensor input_3 = minimum(x = var_59, y = var_35)[name = string("input_3")]; + string var_64_dtype_0 = const()[name = string("op_64_dtype_0"), val = string("int32")]; + bool var_65_exclusive_0 = const()[name = string("op_65_exclusive_0"), val = bool(false)]; + bool var_65_reverse_0 = const()[name = string("op_65_reverse_0"), val = bool(false)]; + tensor var_64 = cast(dtype = var_64_dtype_0, x = span_context_mask_1)[name = string("cast_13")]; + tensor var_65 = cumsum(axis = var_38, exclusive = var_65_exclusive_0, reverse = var_65_reverse_0, x = var_64)[name = string("op_65")]; + int32 var_66 = const()[name = string("op_66"), val = int32(1)]; + tensor var_67 = sub(x = var_65, y = var_66)[name = string("op_67")]; + tensor input_5 = maximum(x = var_67, y = var_40)[name = string("input_5")]; + int32 var_70_batch_dims_0 = const()[name = string("op_70_batch_dims_0"), val = int32(0)]; + bool var_70_validate_indices_0 = const()[name = string("op_70_validate_indices_0"), val = bool(false)]; + tensor module_flow_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33536))))[name = string("module_flow_phone_embed_weight_to_fp16_quantized")]; + string input_1_to_int16_dtype_0 = const()[name = string("input_1_to_int16_dtype_0"), val = string("int16")]; + string cast_24_dtype_0 = const()[name = string("cast_24_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor input_1_to_int16 = cast(dtype = input_1_to_int16_dtype_0, x = input_1)[name = string("cast_12")]; + tensor cast_24 = cast(dtype = cast_24_dtype_0, x = input_1_to_int16)[name = string("cast_11")]; + tensor greater_equal_0 = greater_equal(x = cast_24, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(87)]; + tensor add_0 = add(x = cast_24, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_24, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_10")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_9")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(87)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_70_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_70_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_70_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_70_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_70_batch_dims_0, indices = select_0_1, validate_indices = var_70_validate_indices_0, x = module_flow_phone_embed_weight_to_fp16_quantized)[name = string("op_70_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_72_axis_0 = const()[name = string("op_72_axis_0"), val = int32(0)]; + int32 var_72_batch_dims_0 = const()[name = string("op_72_batch_dims_0"), val = int32(0)]; + bool var_72_validate_indices_0 = const()[name = string("op_72_validate_indices_0"), val = bool(false)]; + tensor module_flow_span_segment_embed_weight_to_fp16 = const()[name = string("module_flow_span_segment_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33792)))]; + string input_3_to_uint16_dtype_0 = const()[name = string("input_3_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_3_to_uint16 = cast(dtype = input_3_to_uint16_dtype_0, x = input_3)[name = string("cast_8")]; + tensor var_72_cast_fp16_cast_uint16 = gather(axis = var_72_axis_0, batch_dims = var_72_batch_dims_0, indices = input_3_to_uint16, validate_indices = var_72_validate_indices_0, x = module_flow_span_segment_embed_weight_to_fp16)[name = string("op_72_cast_fp16_cast_uint16")]; + tensor var_73_cast_fp16 = add(x = op_70_cast_fp16_cast_uint16_cast_uint16, y = var_72_cast_fp16_cast_uint16)[name = string("op_73_cast_fp16")]; + int32 var_75_axis_0 = const()[name = string("op_75_axis_0"), val = int32(0)]; + int32 var_75_batch_dims_0 = const()[name = string("op_75_batch_dims_0"), val = int32(0)]; + bool var_75_validate_indices_0 = const()[name = string("op_75_validate_indices_0"), val = bool(false)]; + tensor module_flow_span_position_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331136))))[name = string("module_flow_span_position_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_7")]; + tensor var_75_cast_fp16_cast_uint16 = gather(axis = var_75_axis_0, batch_dims = var_75_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_75_validate_indices_0, x = module_flow_span_position_embed_weight_to_fp16_quantized)[name = string("op_75_cast_fp16_cast_uint16")]; + tensor hidden_1_cast_fp16 = add(x = var_73_cast_fp16, y = var_75_cast_fp16_cast_uint16)[name = string("hidden_1_cast_fp16")]; + tensor var_82_axes_0 = const()[name = string("op_82_axes_0"), val = tensor([-1])]; + tensor var_82 = expand_dims(axes = var_82_axes_0, x = span_context_mask_1)[name = string("op_82")]; + tensor var_83 = logical_not(x = var_82)[name = string("op_83")]; + fp16 var_18_to_fp16 = const()[name = string("op_18_to_fp16"), val = fp16(0x0p+0)]; + tensor hidden_3_cast_fp16 = select(a = var_18_to_fp16, b = hidden_1_cast_fp16, cond = var_83)[name = string("hidden_3_cast_fp16")]; + tensor input_7_axes_0 = const()[name = string("input_7_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332736)))]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(333568)))]; + fp16 var_28_to_fp16 = const()[name = string("op_28_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_7_cast_fp16 = layer_norm(axes = input_7_axes_0, beta = module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16, x = hidden_3_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(334400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(776832))))[name = string("module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(779200)))]; + tensor linear_0_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_100 = const()[name = string("op_100"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_100, x = linear_0_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_102_split_sizes_0 = const()[name = string("op_102_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_102_axis_0 = const()[name = string("op_102_axis_0"), val = int32(2)]; + tensor var_102_cast_fp16_0, tensor var_102_cast_fp16_1, tensor var_102_cast_fp16_2 = split(axis = var_102_axis_0, split_sizes = var_102_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_102_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_102_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_102_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_102_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_110_transpose_x_0 = const()[name = string("op_110_transpose_x_0"), val = bool(false)]; + bool var_110_transpose_y_0 = const()[name = string("op_110_transpose_y_0"), val = bool(false)]; + tensor transpose_8_perm_0 = const()[name = string("transpose_8_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_9_perm_0 = const()[name = string("transpose_9_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_9 = transpose(perm = transpose_9_perm_0, x = squeeze_1_cast_fp16)[name = string("transpose_18")]; + tensor transpose_8 = transpose(perm = transpose_8_perm_0, x = squeeze_0_cast_fp16)[name = string("transpose_19")]; + tensor var_110_cast_fp16 = matmul(transpose_x = var_110_transpose_x_0, transpose_y = var_110_transpose_y_0, x = transpose_8, y = transpose_9)[name = string("op_110_cast_fp16")]; + fp16 var_111_to_fp16 = const()[name = string("op_111_to_fp16"), val = fp16(0x1p-3)]; + tensor scores_1_cast_fp16 = mul(x = var_110_cast_fp16, y = var_111_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor var_114_axes_0 = const()[name = string("op_114_axes_0"), val = tensor([1])]; + tensor var_114 = expand_dims(axes = var_114_axes_0, x = span_context_mask_1)[name = string("op_114")]; + tensor var_115_axes_0 = const()[name = string("op_115_axes_0"), val = tensor([2])]; + tensor var_115 = expand_dims(axes = var_115_axes_0, x = var_114)[name = string("op_115")]; + tensor var_117 = logical_not(x = var_115)[name = string("op_117")]; + fp16 var_22_to_fp16 = const()[name = string("op_22_to_fp16"), val = fp16(-inf)]; + tensor scores_3_cast_fp16 = select(a = var_22_to_fp16, b = scores_1_cast_fp16, cond = var_117)[name = string("scores_3_cast_fp16")]; + tensor var_119_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = string("op_119_cast_fp16")]; + string var_120_to_fp16_dtype_0 = const()[name = string("op_120_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_115_to_fp16 = cast(dtype = var_120_to_fp16_dtype_0, x = var_115)[name = string("cast_6")]; + tensor weights_1_cast_fp16 = mul(x = var_119_cast_fp16, y = var_115_to_fp16)[name = string("weights_1_cast_fp16")]; + tensor var_123_axes_0 = const()[name = string("op_123_axes_0"), val = tensor([-1])]; + bool var_123_keep_dims_0 = const()[name = string("op_123_keep_dims_0"), val = bool(true)]; + tensor var_123_cast_fp16 = reduce_sum(axes = var_123_axes_0, keep_dims = var_123_keep_dims_0, x = weights_1_cast_fp16)[name = string("op_123_cast_fp16")]; + fp16 var_21_to_fp16 = const()[name = string("op_21_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_124_cast_fp16 = maximum(x = var_123_cast_fp16, y = var_21_to_fp16)[name = string("op_124_cast_fp16")]; + tensor weights_3_cast_fp16 = real_div(x = weights_1_cast_fp16, y = var_124_cast_fp16)[name = string("weights_3_cast_fp16")]; + bool var_126_transpose_x_0 = const()[name = string("op_126_transpose_x_0"), val = bool(false)]; + bool var_126_transpose_y_0 = const()[name = string("op_126_transpose_y_0"), val = bool(false)]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_17")]; + tensor var_126_cast_fp16 = matmul(transpose_x = var_126_transpose_x_0, transpose_y = var_126_transpose_y_0, x = weights_3_cast_fp16, y = value_3_cast_fp16)[name = string("op_126_cast_fp16")]; + tensor var_127_perm_0 = const()[name = string("op_127_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_128 = const()[name = string("op_128"), val = tensor([1, 512, 384])]; + tensor var_127_cast_fp16 = transpose(perm = var_127_perm_0, x = var_126_cast_fp16)[name = string("transpose_16")]; + tensor input_9_cast_fp16 = reshape(shape = var_128, x = var_127_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(781568))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(929088))))[name = string("module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(929920)))]; + tensor linear_1_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = hidden_3_cast_fp16, y = linear_1_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(930752)))]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(931584)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(932416))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1522304))))[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1525440)))]; + tensor linear_2_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + string input_19_mode_0 = const()[name = string("input_19_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_19_cast_fp16 = gelu(mode = input_19_mode_0, x = linear_2_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1528576))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2118464))))[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2119296)))]; + tensor linear_3_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor hidden_5_cast_fp16 = add(x = input_13_cast_fp16, y = linear_3_cast_fp16)[name = string("hidden_5_cast_fp16")]; + tensor hidden_7_cast_fp16 = select(a = var_18_to_fp16, b = hidden_5_cast_fp16, cond = var_83)[name = string("hidden_7_cast_fp16")]; + tensor input_23_axes_0 = const()[name = string("input_23_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2120128)))]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2120960)))]; + tensor input_23_cast_fp16 = layer_norm(axes = input_23_axes_0, beta = module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16, x = hidden_7_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2121792))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564224))))[name = string("module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566592)))]; + tensor linear_4_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_168 = const()[name = string("op_168"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_168, x = linear_4_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_170_split_sizes_0 = const()[name = string("op_170_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_170_axis_0 = const()[name = string("op_170_axis_0"), val = int32(2)]; + tensor var_170_cast_fp16_0, tensor var_170_cast_fp16_1, tensor var_170_cast_fp16_2 = split(axis = var_170_axis_0, split_sizes = var_170_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_170_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_170_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_170_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_170_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_178_transpose_x_0 = const()[name = string("op_178_transpose_x_0"), val = bool(false)]; + bool var_178_transpose_y_0 = const()[name = string("op_178_transpose_y_0"), val = bool(false)]; + tensor transpose_10_perm_0 = const()[name = string("transpose_10_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_11_perm_0 = const()[name = string("transpose_11_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_11 = transpose(perm = transpose_11_perm_0, x = squeeze_4_cast_fp16)[name = string("transpose_14")]; + tensor transpose_10 = transpose(perm = transpose_10_perm_0, x = squeeze_3_cast_fp16)[name = string("transpose_15")]; + tensor var_178_cast_fp16 = matmul(transpose_x = var_178_transpose_x_0, transpose_y = var_178_transpose_y_0, x = transpose_10, y = transpose_11)[name = string("op_178_cast_fp16")]; + fp16 var_179_to_fp16 = const()[name = string("op_179_to_fp16"), val = fp16(0x1p-3)]; + tensor scores_5_cast_fp16 = mul(x = var_178_cast_fp16, y = var_179_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_22_to_fp16, b = scores_5_cast_fp16, cond = var_117)[name = string("scores_cast_fp16")]; + tensor var_187_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = string("op_187_cast_fp16")]; + tensor weights_5_cast_fp16 = mul(x = var_187_cast_fp16, y = var_115_to_fp16)[name = string("weights_5_cast_fp16")]; + tensor var_191_axes_0 = const()[name = string("op_191_axes_0"), val = tensor([-1])]; + bool var_191_keep_dims_0 = const()[name = string("op_191_keep_dims_0"), val = bool(true)]; + tensor var_191_cast_fp16 = reduce_sum(axes = var_191_axes_0, keep_dims = var_191_keep_dims_0, x = weights_5_cast_fp16)[name = string("op_191_cast_fp16")]; + tensor var_192_cast_fp16 = maximum(x = var_191_cast_fp16, y = var_21_to_fp16)[name = string("op_192_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_5_cast_fp16, y = var_192_cast_fp16)[name = string("weights_cast_fp16")]; + bool var_194_transpose_x_0 = const()[name = string("op_194_transpose_x_0"), val = bool(false)]; + bool var_194_transpose_y_0 = const()[name = string("op_194_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_13")]; + tensor var_194_cast_fp16 = matmul(transpose_x = var_194_transpose_x_0, transpose_y = var_194_transpose_y_0, x = weights_cast_fp16, y = value_cast_fp16)[name = string("op_194_cast_fp16")]; + tensor var_195_perm_0 = const()[name = string("op_195_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_196 = const()[name = string("op_196"), val = tensor([1, 512, 384])]; + tensor var_195_cast_fp16 = transpose(perm = var_195_perm_0, x = var_194_cast_fp16)[name = string("transpose_12")]; + tensor input_25_cast_fp16 = reshape(shape = var_196, x = var_195_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2568960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2716480))))[name = string("module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2717312)))]; + tensor linear_5_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_29_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_5_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor input_31_axes_0 = const()[name = string("input_31_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2718144)))]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2718976)))]; + tensor input_31_cast_fp16 = layer_norm(axes = input_31_axes_0, beta = module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2719808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3309696))))[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3312832)))]; + tensor linear_6_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_6_cast_fp16")]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_35_cast_fp16 = gelu(mode = input_35_mode_0, x = linear_6_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3315968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3905856))))[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3906688)))]; + tensor linear_7_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor hidden_9_cast_fp16 = add(x = input_29_cast_fp16, y = linear_7_cast_fp16)[name = string("hidden_9_cast_fp16")]; + tensor hidden_cast_fp16 = select(a = var_18_to_fp16, b = hidden_9_cast_fp16, cond = var_83)[name = string("hidden_cast_fp16")]; + tensor var_224 = equal(x = input_3, y = var_40)[name = string("op_224")]; + tensor var_225 = logical_and(x = span_context_mask_1, y = var_224)[name = string("op_225")]; + tensor var_228_axes_0 = const()[name = string("op_228_axes_0"), val = tensor([1])]; + bool var_228_keep_dims_0 = const()[name = string("op_228_keep_dims_0"), val = bool(true)]; + string weight_15_to_fp16_dtype_0 = const()[name = string("weight_15_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_225_to_fp16 = cast(dtype = weight_15_to_fp16_dtype_0, x = var_225)[name = string("cast_5")]; + tensor var_228_cast_fp16 = reduce_sum(axes = var_228_axes_0, keep_dims = var_228_keep_dims_0, x = var_225_to_fp16)[name = string("op_228_cast_fp16")]; + fp16 var_17_to_fp16 = const()[name = string("op_17_to_fp16"), val = fp16(0x1p+0)]; + tensor var_229_cast_fp16 = maximum(x = var_228_cast_fp16, y = var_17_to_fp16)[name = string("op_229_cast_fp16")]; + tensor weight_17_cast_fp16 = real_div(x = var_225_to_fp16, y = var_229_cast_fp16)[name = string("weight_17_cast_fp16")]; + tensor var_231_axes_0 = const()[name = string("op_231_axes_0"), val = tensor([-1])]; + tensor var_231_cast_fp16 = expand_dims(axes = var_231_axes_0, x = weight_17_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_cast_fp16 = mul(x = hidden_cast_fp16, y = var_231_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_234_axes_0 = const()[name = string("op_234_axes_0"), val = tensor([1])]; + bool var_234_keep_dims_0 = const()[name = string("op_234_keep_dims_0"), val = bool(false)]; + tensor var_234_cast_fp16 = reduce_sum(axes = var_234_axes_0, keep_dims = var_234_keep_dims_0, x = var_232_cast_fp16)[name = string("op_234_cast_fp16")]; + tensor var_235 = equal(x = input_3, y = var_38)[name = string("op_235")]; + tensor var_236 = logical_and(x = span_context_mask_1, y = var_235)[name = string("op_236")]; + tensor var_239_axes_0 = const()[name = string("op_239_axes_0"), val = tensor([1])]; + bool var_239_keep_dims_0 = const()[name = string("op_239_keep_dims_0"), val = bool(true)]; + string weight_19_to_fp16_dtype_0 = const()[name = string("weight_19_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_236_to_fp16 = cast(dtype = weight_19_to_fp16_dtype_0, x = var_236)[name = string("cast_4")]; + tensor var_239_cast_fp16 = reduce_sum(axes = var_239_axes_0, keep_dims = var_239_keep_dims_0, x = var_236_to_fp16)[name = string("op_239_cast_fp16")]; + tensor var_240_cast_fp16 = maximum(x = var_239_cast_fp16, y = var_17_to_fp16)[name = string("op_240_cast_fp16")]; + tensor weight_21_cast_fp16 = real_div(x = var_236_to_fp16, y = var_240_cast_fp16)[name = string("weight_21_cast_fp16")]; + tensor var_242_axes_0 = const()[name = string("op_242_axes_0"), val = tensor([-1])]; + tensor var_242_cast_fp16 = expand_dims(axes = var_242_axes_0, x = weight_21_cast_fp16)[name = string("op_242_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = hidden_cast_fp16, y = var_242_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor var_245_axes_0 = const()[name = string("op_245_axes_0"), val = tensor([1])]; + bool var_245_keep_dims_0 = const()[name = string("op_245_keep_dims_0"), val = bool(false)]; + tensor var_245_cast_fp16 = reduce_sum(axes = var_245_axes_0, keep_dims = var_245_keep_dims_0, x = var_243_cast_fp16)[name = string("op_245_cast_fp16")]; + tensor var_246 = equal(x = input_3, y = var_35)[name = string("op_246")]; + tensor var_247 = logical_and(x = span_context_mask_1, y = var_246)[name = string("op_247")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([1])]; + bool var_250_keep_dims_0 = const()[name = string("op_250_keep_dims_0"), val = bool(true)]; + string weight_23_to_fp16_dtype_0 = const()[name = string("weight_23_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_247_to_fp16 = cast(dtype = weight_23_to_fp16_dtype_0, x = var_247)[name = string("cast_3")]; + tensor var_250_cast_fp16 = reduce_sum(axes = var_250_axes_0, keep_dims = var_250_keep_dims_0, x = var_247_to_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = maximum(x = var_250_cast_fp16, y = var_17_to_fp16)[name = string("op_251_cast_fp16")]; + tensor weight_25_cast_fp16 = real_div(x = var_247_to_fp16, y = var_251_cast_fp16)[name = string("weight_25_cast_fp16")]; + tensor var_253_axes_0 = const()[name = string("op_253_axes_0"), val = tensor([-1])]; + tensor var_253_cast_fp16 = expand_dims(axes = var_253_axes_0, x = weight_25_cast_fp16)[name = string("op_253_cast_fp16")]; + tensor var_254_cast_fp16 = mul(x = hidden_cast_fp16, y = var_253_cast_fp16)[name = string("op_254_cast_fp16")]; + tensor var_256_axes_0 = const()[name = string("op_256_axes_0"), val = tensor([1])]; + bool var_256_keep_dims_0 = const()[name = string("op_256_keep_dims_0"), val = bool(false)]; + tensor var_256_cast_fp16 = reduce_sum(axes = var_256_axes_0, keep_dims = var_256_keep_dims_0, x = var_254_cast_fp16)[name = string("op_256_cast_fp16")]; + bool input_39_interleave_0 = const()[name = string("input_39_interleave_0"), val = bool(false)]; + tensor input_39_cast_fp16 = concat(axis = var_30, interleave = input_39_interleave_0, values = (var_234_cast_fp16, var_245_cast_fp16, var_256_cast_fp16))[name = string("input_39_cast_fp16")]; + tensor module_flow_span_segment_pool_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3907520))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4349952))))[name = string("module_flow_span_segment_pool_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_segment_pool_proj_bias_to_fp16 = const()[name = string("module_flow_span_segment_pool_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4350784)))]; + tensor linear_8_cast_fp16 = linear(bias = module_flow_span_segment_pool_proj_bias_to_fp16, weight = module_flow_span_segment_pool_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor pooled_axes_0 = const()[name = string("pooled_axes_0"), val = tensor([-1])]; + tensor module_flow_span_context_norm_weight_to_fp16 = const()[name = string("module_flow_span_context_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4351616)))]; + tensor module_flow_span_context_norm_bias_to_fp16 = const()[name = string("module_flow_span_context_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4352448)))]; + tensor pooled_cast_fp16 = layer_norm(axes = pooled_axes_0, beta = module_flow_span_context_norm_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_context_norm_weight_to_fp16, x = linear_8_cast_fp16)[name = string("pooled_cast_fp16")]; + tensor var_38_list = const()[name = string("op_38_list"), val = tensor([1])]; + bool reduce_max_0_keep_dims_0 = const()[name = string("reduce_max_0_keep_dims_0"), val = bool(true)]; + tensor reduce_max_0 = reduce_max(axes = var_38_list, keep_dims = reduce_max_0_keep_dims_0, x = var_64)[name = string("reduce_max_0")]; + string var_266_dtype_0 = const()[name = string("op_266_dtype_0"), val = string("bool")]; + tensor var_266 = cast(dtype = var_266_dtype_0, x = reduce_max_0)[name = string("cast_2")]; + tensor var_267 = logical_not(x = var_266)[name = string("op_267")]; + tensor var_268_cast_fp16 = select(a = var_18_to_fp16, b = pooled_cast_fp16, cond = var_267)[name = string("op_268_cast_fp16")]; + string var_268_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_268_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor span_context_hidden = cast(dtype = var_268_cast_fp16_to_fp32_dtype_0, x = var_268_cast_fp16)[name = string("cast_1")]; + } -> (span_context_hidden); +} \ No newline at end of file diff --git a/coreml/coreml/pitch_context_encoder.mlmodelc/weights/weight.bin b/coreml/coreml/pitch_context_encoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..f1435f3e126dbf95e347dda9f26d0a186f24fa21 --- /dev/null +++ b/coreml/coreml/pitch_context_encoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:278d961222a35d3ff57c5e4485e21f467d7b7ac7b69caf567af69fa92854308c +size 4353280 diff --git a/coreml/coreml/pitch_estimator.mlmodelc/analytics/coremldata.bin b/coreml/coreml/pitch_estimator.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..93701a9429c428adfddefa992b6255fd4665b994 --- /dev/null +++ b/coreml/coreml/pitch_estimator.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6c525ec64c58e2d0852b432163653726cd12a0d2ee9fab7fd606afdc6d3b8d26 +size 243 diff --git a/coreml/coreml/pitch_estimator.mlmodelc/coremldata.bin b/coreml/coreml/pitch_estimator.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..4a4dc2a29886272ee3428285cc155e6836c5070d --- /dev/null +++ b/coreml/coreml/pitch_estimator.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:afeb64f3c064a15518f25027ab0ebd28b09f8768ac8a0f70df65f542f0bd8bdf +size 3292 diff --git a/coreml/coreml/pitch_estimator.mlmodelc/model.mil b/coreml/coreml/pitch_estimator.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..c31069bfdb12c0b30607ef979a43b82e71a94aed --- /dev/null +++ b/coreml/coreml/pitch_estimator.mlmodelc/model.mil @@ -0,0 +1,5758 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func pitch_estimator(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23532544))), scale = tensor([[[0x1.3ap-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 1024])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925824)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 1024, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 1024, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 1024, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 1024, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 1024, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 1024, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 1024, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 1024, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 1024, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.258p-6, 0x1.7ccp-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func pitch_estimator_128(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(171392))), scale = tensor([[[0x1.168p-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 128])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254592)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 128, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 128, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 128, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 128, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 128, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 128, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 128, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 128, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 128, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.258p-6, 0x1.7ccp-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func pitch_estimator_256(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22790912))), scale = tensor([[[0x1.36p-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 256])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22889280)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 256, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 256, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 256, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 256, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 256, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 256, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 256, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 256, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 256, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.258p-6, 0x1.7ccp-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func pitch_estimator_384(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22889856))), scale = tensor([[[0x1.36p-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 384])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23037376)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 384, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 384, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 384, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 384, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 384, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 384, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 384, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 384, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 384, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.258p-6, 0x1.7ccp-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func pitch_estimator_512(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23038208))), scale = tensor([[[0x1.3ap-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 512])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23234880)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 512, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 512, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 512, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 512, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 512, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 512, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 512, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 512, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 512, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.258p-6, 0x1.7ccp-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func pitch_estimator_768(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_31 = const()[name = string("op_31"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_31)[name = string("present")]; + int32 var_33 = const()[name = string("op_33"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_33)[name = string("latent_mask")]; + int32 var_54 = const()[name = string("op_54"), val = int32(-1)]; + int32 var_78 = const()[name = string("op_78"), val = int32(7)]; + tensor var_79 = mul(x = voice_id, y = var_78)[name = string("op_79")]; + tensor index = add(x = var_79, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_36_dtype_0 = const()[name = string("cast_36_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_19")]; + tensor cast_36 = cast(dtype = cast_36_dtype_0, x = index_to_int16)[name = string("cast_18")]; + tensor greater_equal_0 = greater_equal(x = cast_36, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_36, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_36, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_17")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_16")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_15")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16 = const()[name = string("module_model_latent_in_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952)))]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71552)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_14")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_105")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_133_axis_0 = const()[name = string("op_133_axis_0"), val = int32(0)]; + int32 var_133_batch_dims_0 = const()[name = string("op_133_batch_dims_0"), val = int32(0)]; + bool var_133_validate_indices_0 = const()[name = string("op_133_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(72384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(105856))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_13")]; + tensor var_133_cast_fp16_cast_uint16 = gather(axis = var_133_axis_0, batch_dims = var_133_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_133_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_133_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_133_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_139_axis_0 = const()[name = string("op_139_axis_0"), val = int32(0)]; + int32 var_139_batch_dims_0 = const()[name = string("op_139_batch_dims_0"), val = int32(0)]; + bool var_139_validate_indices_0 = const()[name = string("op_139_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(106112))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(139968))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_12")]; + tensor var_139_cast_fp16_cast_uint16 = gather(axis = var_139_axis_0, batch_dims = var_139_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_139_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_139_cast_fp16_cast_uint16")]; + int32 var_144_axis_0 = const()[name = string("op_144_axis_0"), val = int32(0)]; + int32 var_144_batch_dims_0 = const()[name = string("op_144_batch_dims_0"), val = int32(0)]; + bool var_144_validate_indices_0 = const()[name = string("op_144_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(140224))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164864))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_11")]; + tensor var_144_cast_fp16_cast_uint16 = gather(axis = var_144_axis_0, batch_dims = var_144_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_144_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_144_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_139_cast_fp16_cast_uint16, y = var_144_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_9")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_10")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(165056)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(166656)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_155_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_159_axis_0 = const()[name = string("op_159_axis_0"), val = int32(0)]; + int32 var_159_batch_dims_0 = const()[name = string("op_159_batch_dims_0"), val = int32(0)]; + bool var_159_validate_indices_0 = const()[name = string("op_159_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(167488)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_8")]; + tensor var_159_cast_fp16_cast_uint16 = gather(axis = var_159_axis_0, batch_dims = var_159_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_159_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_159_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_159_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + tensor op_163_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23235968))), scale = tensor([[[0x1.3ap-5]]]))[name = string("op_163_to_fp16_quantized")]; + tensor x_1_cast_fp16 = add(x = hidden_13_cast_fp16, y = op_163_to_fp16_quantized)[name = string("x_1_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(220608))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226048))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_7")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_6")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_4")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_5")]; + tensor var_175_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_175_cast_fp16")]; + bool input_15_interleave_0 = const()[name = string("input_15_interleave_0"), val = bool(false)]; + tensor input_15_cast_fp16 = concat(axis = var_54, interleave = input_15_interleave_0, values = (var_175_cast_fp16, present_to_fp16))[name = string("input_15_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16 = const()[name = string("module_model_delivery_conditioner_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(231616)))]; + tensor linear_2_bias_0_to_fp16 = const()[name = string("linear_2_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239360)))]; + tensor linear_2_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_193_axes_0 = const()[name = string("op_193_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_3")]; + tensor var_193_cast_fp16 = expand_dims(axes = var_193_axes_0, x = time_to_fp16)[name = string("op_193_cast_fp16")]; + tensor var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240192)))]; + tensor phases_1_cast_fp16 = mul(x = var_193_cast_fp16, y = var_194_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_196_to_fp16 = const()[name = string("op_196_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_196_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_198_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_198_cast_fp16")]; + tensor var_199_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_199_cast_fp16")]; + bool input_21_interleave_0 = const()[name = string("input_21_interleave_0"), val = bool(false)]; + tensor input_21_cast_fp16 = concat(axis = var_54, interleave = input_21_interleave_0, values = (var_198_cast_fp16, var_199_cast_fp16))[name = string("input_21_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(240640))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388160))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(388992)))]; + tensor linear_3_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("linear_3_cast_fp16")]; + string input_25_mode_0 = const()[name = string("input_25_mode_0"), val = string("EXACT")]; + tensor input_25_cast_fp16 = gelu(mode = input_25_mode_0, x = linear_3_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(389824))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(537344))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(538176)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_209_cast_fp16 = add(x = linear_4_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_209_cast_fp16")]; + tensor var_210_cast_fp16 = add(x = var_209_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_210_cast_fp16")]; + tensor var_211_cast_fp16 = add(x = var_210_cast_fp16, y = linear_2_cast_fp16)[name = string("op_211_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539008)))]; + tensor cond_1_cast_fp16 = add(x = var_211_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_57_to_fp16 = const()[name = string("op_57_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_58_to_fp16 = const()[name = string("op_58_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_57_to_fp16, beta = var_58_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_225_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_225_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(539840))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552192))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553024)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_225_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_231_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_axes_0 = const()[name = string("op_232_axes_0"), val = tensor([-1])]; + tensor var_232_cast_fp16 = expand_dims(axes = var_232_axes_0, x = clip_1_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_233_cast_fp16 = mul(x = var_231_cast_fp16, y = var_232_cast_fp16)[name = string("op_233_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553856))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(566208))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_233_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = linear_5_cast_fp16, y = linear_6_cast_fp16)[name = string("op_239_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_239_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_cast_fp16")]; + tensor var_249_axes_0 = const()[name = string("op_249_axes_0"), val = tensor([-1])]; + tensor var_249_cast_fp16 = expand_dims(axes = var_249_axes_0, x = mask_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor input_31_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_249_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(567040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(763712))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_7_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(764544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912064))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_2")]; + tensor linear_8_cast_fp16 = linear(bias = linear_2_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_8_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = cond_cast_fp16, y = linear_8_cast_fp16)[name = string("input_33_cast_fp16")]; + fp16 var_260_to_fp16 = const()[name = string("op_260_to_fp16"), val = fp16(0x1p-3)]; + tensor input_37_cast_fp16 = mul(x = x_1_cast_fp16, y = var_260_to_fp16)[name = string("input_37_cast_fp16")]; + tensor input_35_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(912896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797696))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1802368)))]; + tensor linear_9_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_286_split_sizes_0 = const()[name = string("op_286_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_286_axis_0 = const()[name = string("op_286_axis_0"), val = int32(-1)]; + tensor var_286_cast_fp16_0, tensor var_286_cast_fp16_1, tensor var_286_cast_fp16_2, tensor var_286_cast_fp16_3, tensor var_286_cast_fp16_4, tensor var_286_cast_fp16_5 = split(axis = var_286_axis_0, split_sizes = var_286_split_sizes_0, x = linear_9_cast_fp16)[name = string("op_286_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = var_286_cast_fp16_2)[name = string("op_292_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_63_to_fp16, x = input_37_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor([1])]; + tensor var_295_cast_fp16 = expand_dims(axes = var_295_axes_0, x = var_286_cast_fp16_1)[name = string("op_295_cast_fp16")]; + fp16 var_296_to_fp16 = const()[name = string("op_296_to_fp16"), val = fp16(0x1p+0)]; + tensor var_297_cast_fp16 = add(x = var_295_cast_fp16, y = var_296_to_fp16)[name = string("op_297_cast_fp16")]; + tensor var_298_cast_fp16 = mul(x = x_3_cast_fp16, y = var_297_cast_fp16)[name = string("op_298_cast_fp16")]; + tensor var_299_axes_0 = const()[name = string("op_299_axes_0"), val = tensor([1])]; + tensor var_299_cast_fp16 = expand_dims(axes = var_299_axes_0, x = var_286_cast_fp16_0)[name = string("op_299_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_298_cast_fp16, y = var_299_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1807040))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2249472))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2251840)))]; + tensor linear_10_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_311 = const()[name = string("op_311"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_311, x = linear_10_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_313_split_sizes_0 = const()[name = string("op_313_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_313_axis_0 = const()[name = string("op_313_axis_0"), val = int32(2)]; + tensor var_313_cast_fp16_0, tensor var_313_cast_fp16_1, tensor var_313_cast_fp16_2 = split(axis = var_313_axis_0, split_sizes = var_313_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_313_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_313_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_313_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_313_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_318_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_318_cast_fp16")]; + tensor var_320_axes_0 = const()[name = string("op_320_axes_0"), val = tensor([-1])]; + bool var_320_keep_dims_0 = const()[name = string("op_320_keep_dims_0"), val = bool(true)]; + tensor var_320_cast_fp16 = reduce_mean(axes = var_320_axes_0, keep_dims = var_320_keep_dims_0, x = var_318_cast_fp16)[name = string("op_320_cast_fp16")]; + fp16 var_321_to_fp16 = const()[name = string("op_321_to_fp16"), val = fp16(0x1p-23)]; + tensor var_322_cast_fp16 = add(x = var_320_cast_fp16, y = var_321_to_fp16)[name = string("op_322_cast_fp16")]; + fp32 var_323_epsilon_0 = const()[name = string("op_323_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_323_cast_fp16 = rsqrt(epsilon = var_323_epsilon_0, x = var_322_cast_fp16)[name = string("op_323_cast_fp16")]; + tensor var_324_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_323_cast_fp16)[name = string("op_324_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254208)))]; + tensor var_325_cast_fp16 = mul(x = var_324_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_325_cast_fp16")]; + tensor var_328_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_328_cast_fp16")]; + tensor var_330_axes_0 = const()[name = string("op_330_axes_0"), val = tensor([-1])]; + bool var_330_keep_dims_0 = const()[name = string("op_330_keep_dims_0"), val = bool(true)]; + tensor var_330_cast_fp16 = reduce_mean(axes = var_330_axes_0, keep_dims = var_330_keep_dims_0, x = var_328_cast_fp16)[name = string("op_330_cast_fp16")]; + fp16 var_331_to_fp16 = const()[name = string("op_331_to_fp16"), val = fp16(0x1p-23)]; + tensor var_332_cast_fp16 = add(x = var_330_cast_fp16, y = var_331_to_fp16)[name = string("op_332_cast_fp16")]; + fp32 var_333_epsilon_0 = const()[name = string("op_333_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_333_cast_fp16 = rsqrt(epsilon = var_333_epsilon_0, x = var_332_cast_fp16)[name = string("op_333_cast_fp16")]; + tensor var_334_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_333_cast_fp16)[name = string("op_334_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254400)))]; + tensor var_335_cast_fp16 = mul(x = var_334_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_335_cast_fp16")]; + tensor var_340 = const()[name = string("op_340"), val = tensor([1, 1, 1, 768])]; + tensor var_341 = reshape(shape = var_340, x = latent_mask_1)[name = string("op_341")]; + tensor var_343 = logical_not(x = var_341)[name = string("op_343")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(-inf)]; + tensor var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23530944)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_62_to_fp16, b = var_339_to_fp16, cond = var_343)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_48_perm_0 = const()[name = string("transpose_48_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_49_perm_0 = const()[name = string("transpose_49_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50_perm_0 = const()[name = string("transpose_50_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_50 = transpose(perm = transpose_50_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_102")]; + tensor transpose_49 = transpose(perm = transpose_49_perm_0, x = var_335_cast_fp16)[name = string("transpose_103")]; + tensor transpose_48 = transpose(perm = transpose_48_perm_0, x = var_325_cast_fp16)[name = string("transpose_104")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_49, query = transpose_48, value = transpose_50)[name = string("out_1_cast_fp16")]; + tensor var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_347 = const()[name = string("op_347"), val = tensor([1, 768, 384])]; + tensor var_346_cast_fp16 = transpose(perm = var_346_perm_0, x = out_1_cast_fp16)[name = string("transpose_101")]; + tensor input_39_cast_fp16 = reshape(shape = var_347, x = var_346_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2254912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2402432))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2403264)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_353_cast_fp16 = mul(x = var_292_cast_fp16, y = linear_11_cast_fp16)[name = string("op_353_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_37_cast_fp16, y = var_353_cast_fp16)[name = string("input_43_cast_fp16")]; + tensor var_355_axes_0 = const()[name = string("op_355_axes_0"), val = tensor([1])]; + tensor var_355_cast_fp16 = expand_dims(axes = var_355_axes_0, x = var_286_cast_fp16_5)[name = string("op_355_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_63_to_fp16, x = input_43_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_358_axes_0 = const()[name = string("op_358_axes_0"), val = tensor([1])]; + tensor var_358_cast_fp16 = expand_dims(axes = var_358_axes_0, x = var_286_cast_fp16_4)[name = string("op_358_cast_fp16")]; + fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1p+0)]; + tensor var_360_cast_fp16 = add(x = var_358_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = x_7_cast_fp16, y = var_360_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = var_286_cast_fp16_3)[name = string("op_362_cast_fp16")]; + tensor input_45_cast_fp16 = add(x = var_361_cast_fp16, y = var_362_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2404096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2993984))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2997120)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_12_cast_fp16")]; + string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")]; + tensor input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = linear_12_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3000256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590144))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3590976)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_375_cast_fp16 = mul(x = var_355_cast_fp16, y = linear_13_cast_fp16)[name = string("op_375_cast_fp16")]; + tensor input_57_cast_fp16 = add(x = input_43_cast_fp16, y = var_375_cast_fp16)[name = string("input_57_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3591808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4476608))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4481280)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_14_cast_fp16")]; + tensor var_384_split_sizes_0 = const()[name = string("op_384_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_384_axis_0 = const()[name = string("op_384_axis_0"), val = int32(-1)]; + tensor var_384_cast_fp16_0, tensor var_384_cast_fp16_1, tensor var_384_cast_fp16_2, tensor var_384_cast_fp16_3, tensor var_384_cast_fp16_4, tensor var_384_cast_fp16_5 = split(axis = var_384_axis_0, split_sizes = var_384_split_sizes_0, x = linear_14_cast_fp16)[name = string("op_384_cast_fp16")]; + tensor var_390_axes_0 = const()[name = string("op_390_axes_0"), val = tensor([1])]; + tensor var_390_cast_fp16 = expand_dims(axes = var_390_axes_0, x = var_384_cast_fp16_2)[name = string("op_390_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_63_to_fp16, x = input_57_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_393_axes_0 = const()[name = string("op_393_axes_0"), val = tensor([1])]; + tensor var_393_cast_fp16 = expand_dims(axes = var_393_axes_0, x = var_384_cast_fp16_1)[name = string("op_393_cast_fp16")]; + fp16 var_394_to_fp16 = const()[name = string("op_394_to_fp16"), val = fp16(0x1p+0)]; + tensor var_395_cast_fp16 = add(x = var_393_cast_fp16, y = var_394_to_fp16)[name = string("op_395_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = x_9_cast_fp16, y = var_395_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor var_397_axes_0 = const()[name = string("op_397_axes_0"), val = tensor([1])]; + tensor var_397_cast_fp16 = expand_dims(axes = var_397_axes_0, x = var_384_cast_fp16_0)[name = string("op_397_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_396_cast_fp16, y = var_397_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4485952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4928384))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4930752)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_409 = const()[name = string("op_409"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_409, x = linear_15_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_411_split_sizes_0 = const()[name = string("op_411_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_411_axis_0 = const()[name = string("op_411_axis_0"), val = int32(2)]; + tensor var_411_cast_fp16_0, tensor var_411_cast_fp16_1, tensor var_411_cast_fp16_2 = split(axis = var_411_axis_0, split_sizes = var_411_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_411_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_411_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_411_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_411_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_416_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_416_cast_fp16")]; + tensor var_418_axes_0 = const()[name = string("op_418_axes_0"), val = tensor([-1])]; + bool var_418_keep_dims_0 = const()[name = string("op_418_keep_dims_0"), val = bool(true)]; + tensor var_418_cast_fp16 = reduce_mean(axes = var_418_axes_0, keep_dims = var_418_keep_dims_0, x = var_416_cast_fp16)[name = string("op_418_cast_fp16")]; + fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1p-23)]; + tensor var_420_cast_fp16 = add(x = var_418_cast_fp16, y = var_419_to_fp16)[name = string("op_420_cast_fp16")]; + fp32 var_421_epsilon_0 = const()[name = string("op_421_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_421_cast_fp16 = rsqrt(epsilon = var_421_epsilon_0, x = var_420_cast_fp16)[name = string("op_421_cast_fp16")]; + tensor var_422_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933120)))]; + tensor var_423_cast_fp16 = mul(x = var_422_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_423_cast_fp16")]; + tensor var_426_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_426_cast_fp16")]; + tensor var_428_axes_0 = const()[name = string("op_428_axes_0"), val = tensor([-1])]; + bool var_428_keep_dims_0 = const()[name = string("op_428_keep_dims_0"), val = bool(true)]; + tensor var_428_cast_fp16 = reduce_mean(axes = var_428_axes_0, keep_dims = var_428_keep_dims_0, x = var_426_cast_fp16)[name = string("op_428_cast_fp16")]; + fp16 var_429_to_fp16 = const()[name = string("op_429_to_fp16"), val = fp16(0x1p-23)]; + tensor var_430_cast_fp16 = add(x = var_428_cast_fp16, y = var_429_to_fp16)[name = string("op_430_cast_fp16")]; + fp32 var_431_epsilon_0 = const()[name = string("op_431_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_431_cast_fp16 = rsqrt(epsilon = var_431_epsilon_0, x = var_430_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor var_432_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_431_cast_fp16)[name = string("op_432_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933312)))]; + tensor var_433_cast_fp16 = mul(x = var_432_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_433_cast_fp16")]; + tensor transpose_51_perm_0 = const()[name = string("transpose_51_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_52_perm_0 = const()[name = string("transpose_52_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53_perm_0 = const()[name = string("transpose_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_53 = transpose(perm = transpose_53_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_98")]; + tensor transpose_52 = transpose(perm = transpose_52_perm_0, x = var_433_cast_fp16)[name = string("transpose_99")]; + tensor transpose_51 = transpose(perm = transpose_51_perm_0, x = var_423_cast_fp16)[name = string("transpose_100")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_52, query = transpose_51, value = transpose_53)[name = string("out_3_cast_fp16")]; + tensor var_444_perm_0 = const()[name = string("op_444_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_445 = const()[name = string("op_445"), val = tensor([1, 768, 384])]; + tensor var_444_cast_fp16 = transpose(perm = var_444_perm_0, x = out_3_cast_fp16)[name = string("transpose_97")]; + tensor input_59_cast_fp16 = reshape(shape = var_445, x = var_444_cast_fp16)[name = string("input_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4933504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081024))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5081856)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_59_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_451_cast_fp16 = mul(x = var_390_cast_fp16, y = linear_16_cast_fp16)[name = string("op_451_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_57_cast_fp16, y = var_451_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor var_453_axes_0 = const()[name = string("op_453_axes_0"), val = tensor([1])]; + tensor var_453_cast_fp16 = expand_dims(axes = var_453_axes_0, x = var_384_cast_fp16_5)[name = string("op_453_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_63_to_fp16, x = input_63_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_456_axes_0 = const()[name = string("op_456_axes_0"), val = tensor([1])]; + tensor var_456_cast_fp16 = expand_dims(axes = var_456_axes_0, x = var_384_cast_fp16_4)[name = string("op_456_cast_fp16")]; + fp16 var_457_to_fp16 = const()[name = string("op_457_to_fp16"), val = fp16(0x1p+0)]; + tensor var_458_cast_fp16 = add(x = var_456_cast_fp16, y = var_457_to_fp16)[name = string("op_458_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = x_13_cast_fp16, y = var_458_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_460_axes_0 = const()[name = string("op_460_axes_0"), val = tensor([1])]; + tensor var_460_cast_fp16 = expand_dims(axes = var_460_axes_0, x = var_384_cast_fp16_3)[name = string("op_460_cast_fp16")]; + tensor input_65_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5082688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5672576))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5675712)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_17_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = linear_17_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5678848))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6268736))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6269568)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_69_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_473_cast_fp16 = mul(x = var_453_cast_fp16, y = linear_18_cast_fp16)[name = string("op_473_cast_fp16")]; + tensor input_77_cast_fp16 = add(x = input_63_cast_fp16, y = var_473_cast_fp16)[name = string("input_77_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6270400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7155200))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7159872)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_19_cast_fp16")]; + tensor var_482_split_sizes_0 = const()[name = string("op_482_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_482_axis_0 = const()[name = string("op_482_axis_0"), val = int32(-1)]; + tensor var_482_cast_fp16_0, tensor var_482_cast_fp16_1, tensor var_482_cast_fp16_2, tensor var_482_cast_fp16_3, tensor var_482_cast_fp16_4, tensor var_482_cast_fp16_5 = split(axis = var_482_axis_0, split_sizes = var_482_split_sizes_0, x = linear_19_cast_fp16)[name = string("op_482_cast_fp16")]; + tensor var_488_axes_0 = const()[name = string("op_488_axes_0"), val = tensor([1])]; + tensor var_488_cast_fp16 = expand_dims(axes = var_488_axes_0, x = var_482_cast_fp16_2)[name = string("op_488_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_63_to_fp16, x = input_77_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_491_axes_0 = const()[name = string("op_491_axes_0"), val = tensor([1])]; + tensor var_491_cast_fp16 = expand_dims(axes = var_491_axes_0, x = var_482_cast_fp16_1)[name = string("op_491_cast_fp16")]; + fp16 var_492_to_fp16 = const()[name = string("op_492_to_fp16"), val = fp16(0x1p+0)]; + tensor var_493_cast_fp16 = add(x = var_491_cast_fp16, y = var_492_to_fp16)[name = string("op_493_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = x_15_cast_fp16, y = var_493_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor var_495_axes_0 = const()[name = string("op_495_axes_0"), val = tensor([1])]; + tensor var_495_cast_fp16 = expand_dims(axes = var_495_axes_0, x = var_482_cast_fp16_0)[name = string("op_495_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164544))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7606976))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7609344)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_507 = const()[name = string("op_507"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_507, x = linear_20_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_509_split_sizes_0 = const()[name = string("op_509_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_509_axis_0 = const()[name = string("op_509_axis_0"), val = int32(2)]; + tensor var_509_cast_fp16_0, tensor var_509_cast_fp16_1, tensor var_509_cast_fp16_2 = split(axis = var_509_axis_0, split_sizes = var_509_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_509_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_509_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_509_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_509_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_514_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_514_cast_fp16")]; + tensor var_516_axes_0 = const()[name = string("op_516_axes_0"), val = tensor([-1])]; + bool var_516_keep_dims_0 = const()[name = string("op_516_keep_dims_0"), val = bool(true)]; + tensor var_516_cast_fp16 = reduce_mean(axes = var_516_axes_0, keep_dims = var_516_keep_dims_0, x = var_514_cast_fp16)[name = string("op_516_cast_fp16")]; + fp16 var_517_to_fp16 = const()[name = string("op_517_to_fp16"), val = fp16(0x1p-23)]; + tensor var_518_cast_fp16 = add(x = var_516_cast_fp16, y = var_517_to_fp16)[name = string("op_518_cast_fp16")]; + fp32 var_519_epsilon_0 = const()[name = string("op_519_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_519_cast_fp16 = rsqrt(epsilon = var_519_epsilon_0, x = var_518_cast_fp16)[name = string("op_519_cast_fp16")]; + tensor var_520_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_519_cast_fp16)[name = string("op_520_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611712)))]; + tensor var_521_cast_fp16 = mul(x = var_520_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_521_cast_fp16")]; + tensor var_524_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_524_cast_fp16")]; + tensor var_526_axes_0 = const()[name = string("op_526_axes_0"), val = tensor([-1])]; + bool var_526_keep_dims_0 = const()[name = string("op_526_keep_dims_0"), val = bool(true)]; + tensor var_526_cast_fp16 = reduce_mean(axes = var_526_axes_0, keep_dims = var_526_keep_dims_0, x = var_524_cast_fp16)[name = string("op_526_cast_fp16")]; + fp16 var_527_to_fp16 = const()[name = string("op_527_to_fp16"), val = fp16(0x1p-23)]; + tensor var_528_cast_fp16 = add(x = var_526_cast_fp16, y = var_527_to_fp16)[name = string("op_528_cast_fp16")]; + fp32 var_529_epsilon_0 = const()[name = string("op_529_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_529_cast_fp16 = rsqrt(epsilon = var_529_epsilon_0, x = var_528_cast_fp16)[name = string("op_529_cast_fp16")]; + tensor var_530_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_529_cast_fp16)[name = string("op_530_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7611904)))]; + tensor var_531_cast_fp16 = mul(x = var_530_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_531_cast_fp16")]; + tensor transpose_54_perm_0 = const()[name = string("transpose_54_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_55_perm_0 = const()[name = string("transpose_55_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56_perm_0 = const()[name = string("transpose_56_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_56 = transpose(perm = transpose_56_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_94")]; + tensor transpose_55 = transpose(perm = transpose_55_perm_0, x = var_531_cast_fp16)[name = string("transpose_95")]; + tensor transpose_54 = transpose(perm = transpose_54_perm_0, x = var_521_cast_fp16)[name = string("transpose_96")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_55, query = transpose_54, value = transpose_56)[name = string("out_5_cast_fp16")]; + tensor var_542_perm_0 = const()[name = string("op_542_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_543 = const()[name = string("op_543"), val = tensor([1, 768, 384])]; + tensor var_542_cast_fp16 = transpose(perm = var_542_perm_0, x = out_5_cast_fp16)[name = string("transpose_93")]; + tensor input_79_cast_fp16 = reshape(shape = var_543, x = var_542_cast_fp16)[name = string("input_79_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7612096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7759616))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7760448)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_79_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_549_cast_fp16 = mul(x = var_488_cast_fp16, y = linear_21_cast_fp16)[name = string("op_549_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_77_cast_fp16, y = var_549_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor var_551_axes_0 = const()[name = string("op_551_axes_0"), val = tensor([1])]; + tensor var_551_cast_fp16 = expand_dims(axes = var_551_axes_0, x = var_482_cast_fp16_5)[name = string("op_551_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_63_to_fp16, x = input_83_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_554_axes_0 = const()[name = string("op_554_axes_0"), val = tensor([1])]; + tensor var_554_cast_fp16 = expand_dims(axes = var_554_axes_0, x = var_482_cast_fp16_4)[name = string("op_554_cast_fp16")]; + fp16 var_555_to_fp16 = const()[name = string("op_555_to_fp16"), val = fp16(0x1p+0)]; + tensor var_556_cast_fp16 = add(x = var_554_cast_fp16, y = var_555_to_fp16)[name = string("op_556_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = x_19_cast_fp16, y = var_556_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_558_axes_0 = const()[name = string("op_558_axes_0"), val = tensor([1])]; + tensor var_558_cast_fp16 = expand_dims(axes = var_558_axes_0, x = var_482_cast_fp16_3)[name = string("op_558_cast_fp16")]; + tensor input_85_cast_fp16 = add(x = var_557_cast_fp16, y = var_558_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7761280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8351168))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8354304)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_22_cast_fp16")]; + string input_89_mode_0 = const()[name = string("input_89_mode_0"), val = string("EXACT")]; + tensor input_89_cast_fp16 = gelu(mode = input_89_mode_0, x = linear_22_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8357440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8947328))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948160)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_89_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_571_cast_fp16 = mul(x = var_551_cast_fp16, y = linear_23_cast_fp16)[name = string("op_571_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_83_cast_fp16, y = var_571_cast_fp16)[name = string("input_97_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8948992))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833792))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9838464)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_24_cast_fp16")]; + tensor var_580_split_sizes_0 = const()[name = string("op_580_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_580_axis_0 = const()[name = string("op_580_axis_0"), val = int32(-1)]; + tensor var_580_cast_fp16_0, tensor var_580_cast_fp16_1, tensor var_580_cast_fp16_2, tensor var_580_cast_fp16_3, tensor var_580_cast_fp16_4, tensor var_580_cast_fp16_5 = split(axis = var_580_axis_0, split_sizes = var_580_split_sizes_0, x = linear_24_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor var_586_axes_0 = const()[name = string("op_586_axes_0"), val = tensor([1])]; + tensor var_586_cast_fp16 = expand_dims(axes = var_586_axes_0, x = var_580_cast_fp16_2)[name = string("op_586_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_63_to_fp16, x = input_97_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_589_axes_0 = const()[name = string("op_589_axes_0"), val = tensor([1])]; + tensor var_589_cast_fp16 = expand_dims(axes = var_589_axes_0, x = var_580_cast_fp16_1)[name = string("op_589_cast_fp16")]; + fp16 var_590_to_fp16 = const()[name = string("op_590_to_fp16"), val = fp16(0x1p+0)]; + tensor var_591_cast_fp16 = add(x = var_589_cast_fp16, y = var_590_to_fp16)[name = string("op_591_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = x_21_cast_fp16, y = var_591_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor var_593_axes_0 = const()[name = string("op_593_axes_0"), val = tensor([1])]; + tensor var_593_cast_fp16 = expand_dims(axes = var_593_axes_0, x = var_580_cast_fp16_0)[name = string("op_593_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_592_cast_fp16, y = var_593_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9843136))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10285568))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10287936)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_605 = const()[name = string("op_605"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_605, x = linear_25_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_607_split_sizes_0 = const()[name = string("op_607_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_607_axis_0 = const()[name = string("op_607_axis_0"), val = int32(2)]; + tensor var_607_cast_fp16_0, tensor var_607_cast_fp16_1, tensor var_607_cast_fp16_2 = split(axis = var_607_axis_0, split_sizes = var_607_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_607_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_607_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_607_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_607_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_612_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_612_cast_fp16")]; + tensor var_614_axes_0 = const()[name = string("op_614_axes_0"), val = tensor([-1])]; + bool var_614_keep_dims_0 = const()[name = string("op_614_keep_dims_0"), val = bool(true)]; + tensor var_614_cast_fp16 = reduce_mean(axes = var_614_axes_0, keep_dims = var_614_keep_dims_0, x = var_612_cast_fp16)[name = string("op_614_cast_fp16")]; + fp16 var_615_to_fp16 = const()[name = string("op_615_to_fp16"), val = fp16(0x1p-23)]; + tensor var_616_cast_fp16 = add(x = var_614_cast_fp16, y = var_615_to_fp16)[name = string("op_616_cast_fp16")]; + fp32 var_617_epsilon_0 = const()[name = string("op_617_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_617_cast_fp16 = rsqrt(epsilon = var_617_epsilon_0, x = var_616_cast_fp16)[name = string("op_617_cast_fp16")]; + tensor var_618_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_617_cast_fp16)[name = string("op_618_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290304)))]; + tensor var_619_cast_fp16 = mul(x = var_618_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_619_cast_fp16")]; + tensor var_622_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_622_cast_fp16")]; + tensor var_624_axes_0 = const()[name = string("op_624_axes_0"), val = tensor([-1])]; + bool var_624_keep_dims_0 = const()[name = string("op_624_keep_dims_0"), val = bool(true)]; + tensor var_624_cast_fp16 = reduce_mean(axes = var_624_axes_0, keep_dims = var_624_keep_dims_0, x = var_622_cast_fp16)[name = string("op_624_cast_fp16")]; + fp16 var_625_to_fp16 = const()[name = string("op_625_to_fp16"), val = fp16(0x1p-23)]; + tensor var_626_cast_fp16 = add(x = var_624_cast_fp16, y = var_625_to_fp16)[name = string("op_626_cast_fp16")]; + fp32 var_627_epsilon_0 = const()[name = string("op_627_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_627_cast_fp16 = rsqrt(epsilon = var_627_epsilon_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")]; + tensor var_628_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_627_cast_fp16)[name = string("op_628_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290496)))]; + tensor var_629_cast_fp16 = mul(x = var_628_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_629_cast_fp16")]; + tensor transpose_57_perm_0 = const()[name = string("transpose_57_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_58_perm_0 = const()[name = string("transpose_58_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59_perm_0 = const()[name = string("transpose_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_59 = transpose(perm = transpose_59_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_90")]; + tensor transpose_58 = transpose(perm = transpose_58_perm_0, x = var_629_cast_fp16)[name = string("transpose_91")]; + tensor transpose_57 = transpose(perm = transpose_57_perm_0, x = var_619_cast_fp16)[name = string("transpose_92")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_58, query = transpose_57, value = transpose_59)[name = string("out_7_cast_fp16")]; + tensor var_640_perm_0 = const()[name = string("op_640_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_641 = const()[name = string("op_641"), val = tensor([1, 768, 384])]; + tensor var_640_cast_fp16 = transpose(perm = var_640_perm_0, x = out_7_cast_fp16)[name = string("transpose_89")]; + tensor input_99_cast_fp16 = reshape(shape = var_641, x = var_640_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10290688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10438208))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439040)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_99_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_647_cast_fp16 = mul(x = var_586_cast_fp16, y = linear_26_cast_fp16)[name = string("op_647_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_97_cast_fp16, y = var_647_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor var_649_axes_0 = const()[name = string("op_649_axes_0"), val = tensor([1])]; + tensor var_649_cast_fp16 = expand_dims(axes = var_649_axes_0, x = var_580_cast_fp16_5)[name = string("op_649_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_63_to_fp16, x = input_103_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_652_axes_0 = const()[name = string("op_652_axes_0"), val = tensor([1])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = var_580_cast_fp16_4)[name = string("op_652_cast_fp16")]; + fp16 var_653_to_fp16 = const()[name = string("op_653_to_fp16"), val = fp16(0x1p+0)]; + tensor var_654_cast_fp16 = add(x = var_652_cast_fp16, y = var_653_to_fp16)[name = string("op_654_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = x_25_cast_fp16, y = var_654_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_656_axes_0 = const()[name = string("op_656_axes_0"), val = tensor([1])]; + tensor var_656_cast_fp16 = expand_dims(axes = var_656_axes_0, x = var_580_cast_fp16_3)[name = string("op_656_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = var_655_cast_fp16, y = var_656_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10439872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11029760))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11032896)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_27_cast_fp16")]; + string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("EXACT")]; + tensor input_109_cast_fp16 = gelu(mode = input_109_mode_0, x = linear_27_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11036032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11625920))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11626752)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_669_cast_fp16 = mul(x = var_649_cast_fp16, y = linear_28_cast_fp16)[name = string("op_669_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = input_103_cast_fp16, y = var_669_cast_fp16)[name = string("input_117_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11627584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12512384))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12517056)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_29_cast_fp16")]; + tensor var_678_split_sizes_0 = const()[name = string("op_678_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_678_axis_0 = const()[name = string("op_678_axis_0"), val = int32(-1)]; + tensor var_678_cast_fp16_0, tensor var_678_cast_fp16_1, tensor var_678_cast_fp16_2, tensor var_678_cast_fp16_3, tensor var_678_cast_fp16_4, tensor var_678_cast_fp16_5 = split(axis = var_678_axis_0, split_sizes = var_678_split_sizes_0, x = linear_29_cast_fp16)[name = string("op_678_cast_fp16")]; + tensor var_684_axes_0 = const()[name = string("op_684_axes_0"), val = tensor([1])]; + tensor var_684_cast_fp16 = expand_dims(axes = var_684_axes_0, x = var_678_cast_fp16_2)[name = string("op_684_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_63_to_fp16, x = input_117_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_687_axes_0 = const()[name = string("op_687_axes_0"), val = tensor([1])]; + tensor var_687_cast_fp16 = expand_dims(axes = var_687_axes_0, x = var_678_cast_fp16_1)[name = string("op_687_cast_fp16")]; + fp16 var_688_to_fp16 = const()[name = string("op_688_to_fp16"), val = fp16(0x1p+0)]; + tensor var_689_cast_fp16 = add(x = var_687_cast_fp16, y = var_688_to_fp16)[name = string("op_689_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = x_27_cast_fp16, y = var_689_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor var_691_axes_0 = const()[name = string("op_691_axes_0"), val = tensor([1])]; + tensor var_691_cast_fp16 = expand_dims(axes = var_691_axes_0, x = var_678_cast_fp16_0)[name = string("op_691_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_690_cast_fp16, y = var_691_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12521728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12964160))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12966528)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_703 = const()[name = string("op_703"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_703, x = linear_30_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_705_split_sizes_0 = const()[name = string("op_705_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_705_axis_0 = const()[name = string("op_705_axis_0"), val = int32(2)]; + tensor var_705_cast_fp16_0, tensor var_705_cast_fp16_1, tensor var_705_cast_fp16_2 = split(axis = var_705_axis_0, split_sizes = var_705_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_705_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_705_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_705_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_705_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_710_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_710_cast_fp16")]; + tensor var_712_axes_0 = const()[name = string("op_712_axes_0"), val = tensor([-1])]; + bool var_712_keep_dims_0 = const()[name = string("op_712_keep_dims_0"), val = bool(true)]; + tensor var_712_cast_fp16 = reduce_mean(axes = var_712_axes_0, keep_dims = var_712_keep_dims_0, x = var_710_cast_fp16)[name = string("op_712_cast_fp16")]; + fp16 var_713_to_fp16 = const()[name = string("op_713_to_fp16"), val = fp16(0x1p-23)]; + tensor var_714_cast_fp16 = add(x = var_712_cast_fp16, y = var_713_to_fp16)[name = string("op_714_cast_fp16")]; + fp32 var_715_epsilon_0 = const()[name = string("op_715_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_715_cast_fp16 = rsqrt(epsilon = var_715_epsilon_0, x = var_714_cast_fp16)[name = string("op_715_cast_fp16")]; + tensor var_716_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_715_cast_fp16)[name = string("op_716_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12968896)))]; + tensor var_717_cast_fp16 = mul(x = var_716_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_717_cast_fp16")]; + tensor var_720_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_720_cast_fp16")]; + tensor var_722_axes_0 = const()[name = string("op_722_axes_0"), val = tensor([-1])]; + bool var_722_keep_dims_0 = const()[name = string("op_722_keep_dims_0"), val = bool(true)]; + tensor var_722_cast_fp16 = reduce_mean(axes = var_722_axes_0, keep_dims = var_722_keep_dims_0, x = var_720_cast_fp16)[name = string("op_722_cast_fp16")]; + fp16 var_723_to_fp16 = const()[name = string("op_723_to_fp16"), val = fp16(0x1p-23)]; + tensor var_724_cast_fp16 = add(x = var_722_cast_fp16, y = var_723_to_fp16)[name = string("op_724_cast_fp16")]; + fp32 var_725_epsilon_0 = const()[name = string("op_725_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_725_cast_fp16 = rsqrt(epsilon = var_725_epsilon_0, x = var_724_cast_fp16)[name = string("op_725_cast_fp16")]; + tensor var_726_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_725_cast_fp16)[name = string("op_726_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969088)))]; + tensor var_727_cast_fp16 = mul(x = var_726_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_727_cast_fp16")]; + tensor transpose_60_perm_0 = const()[name = string("transpose_60_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_61_perm_0 = const()[name = string("transpose_61_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62_perm_0 = const()[name = string("transpose_62_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_62 = transpose(perm = transpose_62_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_86")]; + tensor transpose_61 = transpose(perm = transpose_61_perm_0, x = var_727_cast_fp16)[name = string("transpose_87")]; + tensor transpose_60 = transpose(perm = transpose_60_perm_0, x = var_717_cast_fp16)[name = string("transpose_88")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_61, query = transpose_60, value = transpose_62)[name = string("out_9_cast_fp16")]; + tensor var_738_perm_0 = const()[name = string("op_738_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_739 = const()[name = string("op_739"), val = tensor([1, 768, 384])]; + tensor var_738_cast_fp16 = transpose(perm = var_738_perm_0, x = out_9_cast_fp16)[name = string("transpose_85")]; + tensor input_119_cast_fp16 = reshape(shape = var_739, x = var_738_cast_fp16)[name = string("input_119_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12969280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116800))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13117632)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_119_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_745_cast_fp16 = mul(x = var_684_cast_fp16, y = linear_31_cast_fp16)[name = string("op_745_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_117_cast_fp16, y = var_745_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor var_747_axes_0 = const()[name = string("op_747_axes_0"), val = tensor([1])]; + tensor var_747_cast_fp16 = expand_dims(axes = var_747_axes_0, x = var_678_cast_fp16_5)[name = string("op_747_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_63_to_fp16, x = input_123_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_750_axes_0 = const()[name = string("op_750_axes_0"), val = tensor([1])]; + tensor var_750_cast_fp16 = expand_dims(axes = var_750_axes_0, x = var_678_cast_fp16_4)[name = string("op_750_cast_fp16")]; + fp16 var_751_to_fp16 = const()[name = string("op_751_to_fp16"), val = fp16(0x1p+0)]; + tensor var_752_cast_fp16 = add(x = var_750_cast_fp16, y = var_751_to_fp16)[name = string("op_752_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = x_31_cast_fp16, y = var_752_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_754_axes_0 = const()[name = string("op_754_axes_0"), val = tensor([1])]; + tensor var_754_cast_fp16 = expand_dims(axes = var_754_axes_0, x = var_678_cast_fp16_3)[name = string("op_754_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = var_753_cast_fp16, y = var_754_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13118464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13708352))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13711488)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_32_cast_fp16")]; + string input_129_mode_0 = const()[name = string("input_129_mode_0"), val = string("EXACT")]; + tensor input_129_cast_fp16 = gelu(mode = input_129_mode_0, x = linear_32_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13714624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14304512))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14305344)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_129_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_767_cast_fp16 = mul(x = var_747_cast_fp16, y = linear_33_cast_fp16)[name = string("op_767_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_123_cast_fp16, y = var_767_cast_fp16)[name = string("input_137_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14306176))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15190976))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15195648)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_34_cast_fp16")]; + tensor var_776_split_sizes_0 = const()[name = string("op_776_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_776_axis_0 = const()[name = string("op_776_axis_0"), val = int32(-1)]; + tensor var_776_cast_fp16_0, tensor var_776_cast_fp16_1, tensor var_776_cast_fp16_2, tensor var_776_cast_fp16_3, tensor var_776_cast_fp16_4, tensor var_776_cast_fp16_5 = split(axis = var_776_axis_0, split_sizes = var_776_split_sizes_0, x = linear_34_cast_fp16)[name = string("op_776_cast_fp16")]; + tensor var_782_axes_0 = const()[name = string("op_782_axes_0"), val = tensor([1])]; + tensor var_782_cast_fp16 = expand_dims(axes = var_782_axes_0, x = var_776_cast_fp16_2)[name = string("op_782_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_63_to_fp16, x = input_137_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_785_axes_0 = const()[name = string("op_785_axes_0"), val = tensor([1])]; + tensor var_785_cast_fp16 = expand_dims(axes = var_785_axes_0, x = var_776_cast_fp16_1)[name = string("op_785_cast_fp16")]; + fp16 var_786_to_fp16 = const()[name = string("op_786_to_fp16"), val = fp16(0x1p+0)]; + tensor var_787_cast_fp16 = add(x = var_785_cast_fp16, y = var_786_to_fp16)[name = string("op_787_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = x_33_cast_fp16, y = var_787_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor var_789_axes_0 = const()[name = string("op_789_axes_0"), val = tensor([1])]; + tensor var_789_cast_fp16 = expand_dims(axes = var_789_axes_0, x = var_776_cast_fp16_0)[name = string("op_789_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_788_cast_fp16, y = var_789_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15200320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15642752))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15645120)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_801 = const()[name = string("op_801"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_801, x = linear_35_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_803_split_sizes_0 = const()[name = string("op_803_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_803_axis_0 = const()[name = string("op_803_axis_0"), val = int32(2)]; + tensor var_803_cast_fp16_0, tensor var_803_cast_fp16_1, tensor var_803_cast_fp16_2 = split(axis = var_803_axis_0, split_sizes = var_803_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_803_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_803_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_803_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_803_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_808_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_808_cast_fp16")]; + tensor var_810_axes_0 = const()[name = string("op_810_axes_0"), val = tensor([-1])]; + bool var_810_keep_dims_0 = const()[name = string("op_810_keep_dims_0"), val = bool(true)]; + tensor var_810_cast_fp16 = reduce_mean(axes = var_810_axes_0, keep_dims = var_810_keep_dims_0, x = var_808_cast_fp16)[name = string("op_810_cast_fp16")]; + fp16 var_811_to_fp16 = const()[name = string("op_811_to_fp16"), val = fp16(0x1p-23)]; + tensor var_812_cast_fp16 = add(x = var_810_cast_fp16, y = var_811_to_fp16)[name = string("op_812_cast_fp16")]; + fp32 var_813_epsilon_0 = const()[name = string("op_813_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_813_cast_fp16 = rsqrt(epsilon = var_813_epsilon_0, x = var_812_cast_fp16)[name = string("op_813_cast_fp16")]; + tensor var_814_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_813_cast_fp16)[name = string("op_814_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647488)))]; + tensor var_815_cast_fp16 = mul(x = var_814_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_815_cast_fp16")]; + tensor var_818_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_818_cast_fp16")]; + tensor var_820_axes_0 = const()[name = string("op_820_axes_0"), val = tensor([-1])]; + bool var_820_keep_dims_0 = const()[name = string("op_820_keep_dims_0"), val = bool(true)]; + tensor var_820_cast_fp16 = reduce_mean(axes = var_820_axes_0, keep_dims = var_820_keep_dims_0, x = var_818_cast_fp16)[name = string("op_820_cast_fp16")]; + fp16 var_821_to_fp16 = const()[name = string("op_821_to_fp16"), val = fp16(0x1p-23)]; + tensor var_822_cast_fp16 = add(x = var_820_cast_fp16, y = var_821_to_fp16)[name = string("op_822_cast_fp16")]; + fp32 var_823_epsilon_0 = const()[name = string("op_823_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_823_cast_fp16 = rsqrt(epsilon = var_823_epsilon_0, x = var_822_cast_fp16)[name = string("op_823_cast_fp16")]; + tensor var_824_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_823_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647680)))]; + tensor var_825_cast_fp16 = mul(x = var_824_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_825_cast_fp16")]; + tensor transpose_63_perm_0 = const()[name = string("transpose_63_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_64_perm_0 = const()[name = string("transpose_64_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65_perm_0 = const()[name = string("transpose_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_65 = transpose(perm = transpose_65_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_82")]; + tensor transpose_64 = transpose(perm = transpose_64_perm_0, x = var_825_cast_fp16)[name = string("transpose_83")]; + tensor transpose_63 = transpose(perm = transpose_63_perm_0, x = var_815_cast_fp16)[name = string("transpose_84")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_64, query = transpose_63, value = transpose_65)[name = string("out_11_cast_fp16")]; + tensor var_836_perm_0 = const()[name = string("op_836_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_837 = const()[name = string("op_837"), val = tensor([1, 768, 384])]; + tensor var_836_cast_fp16 = transpose(perm = var_836_perm_0, x = out_11_cast_fp16)[name = string("transpose_81")]; + tensor input_139_cast_fp16 = reshape(shape = var_837, x = var_836_cast_fp16)[name = string("input_139_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15647872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15795392))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15796224)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_139_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_843_cast_fp16 = mul(x = var_782_cast_fp16, y = linear_36_cast_fp16)[name = string("op_843_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = var_843_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor var_845_axes_0 = const()[name = string("op_845_axes_0"), val = tensor([1])]; + tensor var_845_cast_fp16 = expand_dims(axes = var_845_axes_0, x = var_776_cast_fp16_5)[name = string("op_845_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_63_to_fp16, x = input_143_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_848_axes_0 = const()[name = string("op_848_axes_0"), val = tensor([1])]; + tensor var_848_cast_fp16 = expand_dims(axes = var_848_axes_0, x = var_776_cast_fp16_4)[name = string("op_848_cast_fp16")]; + fp16 var_849_to_fp16 = const()[name = string("op_849_to_fp16"), val = fp16(0x1p+0)]; + tensor var_850_cast_fp16 = add(x = var_848_cast_fp16, y = var_849_to_fp16)[name = string("op_850_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = x_37_cast_fp16, y = var_850_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_852_axes_0 = const()[name = string("op_852_axes_0"), val = tensor([1])]; + tensor var_852_cast_fp16 = expand_dims(axes = var_852_axes_0, x = var_776_cast_fp16_3)[name = string("op_852_cast_fp16")]; + tensor input_145_cast_fp16 = add(x = var_851_cast_fp16, y = var_852_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15797056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16386944))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16390080)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_37_cast_fp16")]; + string input_149_mode_0 = const()[name = string("input_149_mode_0"), val = string("EXACT")]; + tensor input_149_cast_fp16 = gelu(mode = input_149_mode_0, x = linear_37_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16393216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983104))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16983936)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_149_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_865_cast_fp16 = mul(x = var_845_cast_fp16, y = linear_38_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor input_157_cast_fp16 = add(x = input_143_cast_fp16, y = var_865_cast_fp16)[name = string("input_157_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16984768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17869568))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17874240)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_39_cast_fp16")]; + tensor var_874_split_sizes_0 = const()[name = string("op_874_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_874_axis_0 = const()[name = string("op_874_axis_0"), val = int32(-1)]; + tensor var_874_cast_fp16_0, tensor var_874_cast_fp16_1, tensor var_874_cast_fp16_2, tensor var_874_cast_fp16_3, tensor var_874_cast_fp16_4, tensor var_874_cast_fp16_5 = split(axis = var_874_axis_0, split_sizes = var_874_split_sizes_0, x = linear_39_cast_fp16)[name = string("op_874_cast_fp16")]; + tensor var_880_axes_0 = const()[name = string("op_880_axes_0"), val = tensor([1])]; + tensor var_880_cast_fp16 = expand_dims(axes = var_880_axes_0, x = var_874_cast_fp16_2)[name = string("op_880_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_63_to_fp16, x = input_157_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_883_axes_0 = const()[name = string("op_883_axes_0"), val = tensor([1])]; + tensor var_883_cast_fp16 = expand_dims(axes = var_883_axes_0, x = var_874_cast_fp16_1)[name = string("op_883_cast_fp16")]; + fp16 var_884_to_fp16 = const()[name = string("op_884_to_fp16"), val = fp16(0x1p+0)]; + tensor var_885_cast_fp16 = add(x = var_883_cast_fp16, y = var_884_to_fp16)[name = string("op_885_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = x_39_cast_fp16, y = var_885_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor var_887_axes_0 = const()[name = string("op_887_axes_0"), val = tensor([1])]; + tensor var_887_cast_fp16 = expand_dims(axes = var_887_axes_0, x = var_874_cast_fp16_0)[name = string("op_887_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_886_cast_fp16, y = var_887_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17878912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18321344))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18323712)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_899 = const()[name = string("op_899"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_899, x = linear_40_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_901_split_sizes_0 = const()[name = string("op_901_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_901_axis_0 = const()[name = string("op_901_axis_0"), val = int32(2)]; + tensor var_901_cast_fp16_0, tensor var_901_cast_fp16_1, tensor var_901_cast_fp16_2 = split(axis = var_901_axis_0, split_sizes = var_901_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_901_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_901_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_901_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_901_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_906_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor var_908_axes_0 = const()[name = string("op_908_axes_0"), val = tensor([-1])]; + bool var_908_keep_dims_0 = const()[name = string("op_908_keep_dims_0"), val = bool(true)]; + tensor var_908_cast_fp16 = reduce_mean(axes = var_908_axes_0, keep_dims = var_908_keep_dims_0, x = var_906_cast_fp16)[name = string("op_908_cast_fp16")]; + fp16 var_909_to_fp16 = const()[name = string("op_909_to_fp16"), val = fp16(0x1p-23)]; + tensor var_910_cast_fp16 = add(x = var_908_cast_fp16, y = var_909_to_fp16)[name = string("op_910_cast_fp16")]; + fp32 var_911_epsilon_0 = const()[name = string("op_911_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_911_cast_fp16 = rsqrt(epsilon = var_911_epsilon_0, x = var_910_cast_fp16)[name = string("op_911_cast_fp16")]; + tensor var_912_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_911_cast_fp16)[name = string("op_912_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326080)))]; + tensor var_913_cast_fp16 = mul(x = var_912_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_913_cast_fp16")]; + tensor var_916_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_916_cast_fp16")]; + tensor var_918_axes_0 = const()[name = string("op_918_axes_0"), val = tensor([-1])]; + bool var_918_keep_dims_0 = const()[name = string("op_918_keep_dims_0"), val = bool(true)]; + tensor var_918_cast_fp16 = reduce_mean(axes = var_918_axes_0, keep_dims = var_918_keep_dims_0, x = var_916_cast_fp16)[name = string("op_918_cast_fp16")]; + fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1p-23)]; + tensor var_920_cast_fp16 = add(x = var_918_cast_fp16, y = var_919_to_fp16)[name = string("op_920_cast_fp16")]; + fp32 var_921_epsilon_0 = const()[name = string("op_921_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_921_cast_fp16 = rsqrt(epsilon = var_921_epsilon_0, x = var_920_cast_fp16)[name = string("op_921_cast_fp16")]; + tensor var_922_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_921_cast_fp16)[name = string("op_922_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326272)))]; + tensor var_923_cast_fp16 = mul(x = var_922_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_923_cast_fp16")]; + tensor transpose_66_perm_0 = const()[name = string("transpose_66_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_67_perm_0 = const()[name = string("transpose_67_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68_perm_0 = const()[name = string("transpose_68_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_68 = transpose(perm = transpose_68_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_78")]; + tensor transpose_67 = transpose(perm = transpose_67_perm_0, x = var_923_cast_fp16)[name = string("transpose_79")]; + tensor transpose_66 = transpose(perm = transpose_66_perm_0, x = var_913_cast_fp16)[name = string("transpose_80")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_67, query = transpose_66, value = transpose_68)[name = string("out_13_cast_fp16")]; + tensor var_934_perm_0 = const()[name = string("op_934_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_935 = const()[name = string("op_935"), val = tensor([1, 768, 384])]; + tensor var_934_cast_fp16 = transpose(perm = var_934_perm_0, x = out_13_cast_fp16)[name = string("transpose_77")]; + tensor input_159_cast_fp16 = reshape(shape = var_935, x = var_934_cast_fp16)[name = string("input_159_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18326464))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18473984))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18474816)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_159_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_941_cast_fp16 = mul(x = var_880_cast_fp16, y = linear_41_cast_fp16)[name = string("op_941_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_157_cast_fp16, y = var_941_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_943_axes_0 = const()[name = string("op_943_axes_0"), val = tensor([1])]; + tensor var_943_cast_fp16 = expand_dims(axes = var_943_axes_0, x = var_874_cast_fp16_5)[name = string("op_943_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_63_to_fp16, x = input_163_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_946_axes_0 = const()[name = string("op_946_axes_0"), val = tensor([1])]; + tensor var_946_cast_fp16 = expand_dims(axes = var_946_axes_0, x = var_874_cast_fp16_4)[name = string("op_946_cast_fp16")]; + fp16 var_947_to_fp16 = const()[name = string("op_947_to_fp16"), val = fp16(0x1p+0)]; + tensor var_948_cast_fp16 = add(x = var_946_cast_fp16, y = var_947_to_fp16)[name = string("op_948_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = x_43_cast_fp16, y = var_948_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_950_axes_0 = const()[name = string("op_950_axes_0"), val = tensor([1])]; + tensor var_950_cast_fp16 = expand_dims(axes = var_950_axes_0, x = var_874_cast_fp16_3)[name = string("op_950_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = var_949_cast_fp16, y = var_950_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18475648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19065536))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19068672)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_42_cast_fp16")]; + string input_169_mode_0 = const()[name = string("input_169_mode_0"), val = string("EXACT")]; + tensor input_169_cast_fp16 = gelu(mode = input_169_mode_0, x = linear_42_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19071808))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19661696))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19662528)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_169_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_963_cast_fp16 = mul(x = var_943_cast_fp16, y = linear_43_cast_fp16)[name = string("op_963_cast_fp16")]; + tensor input_177_cast_fp16 = add(x = input_163_cast_fp16, y = var_963_cast_fp16)[name = string("input_177_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19663360))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20548160))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20552832)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_44_cast_fp16")]; + tensor var_972_split_sizes_0 = const()[name = string("op_972_split_sizes_0"), val = tensor([384, 384, 384, 384, 384, 384])]; + int32 var_972_axis_0 = const()[name = string("op_972_axis_0"), val = int32(-1)]; + tensor var_972_cast_fp16_0, tensor var_972_cast_fp16_1, tensor var_972_cast_fp16_2, tensor var_972_cast_fp16_3, tensor var_972_cast_fp16_4, tensor var_972_cast_fp16_5 = split(axis = var_972_axis_0, split_sizes = var_972_split_sizes_0, x = linear_44_cast_fp16)[name = string("op_972_cast_fp16")]; + tensor var_978_axes_0 = const()[name = string("op_978_axes_0"), val = tensor([1])]; + tensor var_978_cast_fp16 = expand_dims(axes = var_978_axes_0, x = var_972_cast_fp16_2)[name = string("op_978_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_63_to_fp16, x = input_177_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_981_axes_0 = const()[name = string("op_981_axes_0"), val = tensor([1])]; + tensor var_981_cast_fp16 = expand_dims(axes = var_981_axes_0, x = var_972_cast_fp16_1)[name = string("op_981_cast_fp16")]; + fp16 var_982_to_fp16 = const()[name = string("op_982_to_fp16"), val = fp16(0x1p+0)]; + tensor var_983_cast_fp16 = add(x = var_981_cast_fp16, y = var_982_to_fp16)[name = string("op_983_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = x_45_cast_fp16, y = var_983_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_985_axes_0 = const()[name = string("op_985_axes_0"), val = tensor([1])]; + tensor var_985_cast_fp16 = expand_dims(axes = var_985_axes_0, x = var_972_cast_fp16_0)[name = string("op_985_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_984_cast_fp16, y = var_985_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20557504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20999936))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21002304)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_997 = const()[name = string("op_997"), val = tensor([1, 768, 3, 6, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_997, x = linear_45_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(2)]; + tensor var_999_cast_fp16_0, tensor var_999_cast_fp16_1, tensor var_999_cast_fp16_2 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_999_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_999_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_999_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_999_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1004_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor var_1006_axes_0 = const()[name = string("op_1006_axes_0"), val = tensor([-1])]; + bool var_1006_keep_dims_0 = const()[name = string("op_1006_keep_dims_0"), val = bool(true)]; + tensor var_1006_cast_fp16 = reduce_mean(axes = var_1006_axes_0, keep_dims = var_1006_keep_dims_0, x = var_1004_cast_fp16)[name = string("op_1006_cast_fp16")]; + fp16 var_1007_to_fp16 = const()[name = string("op_1007_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1008_cast_fp16 = add(x = var_1006_cast_fp16, y = var_1007_to_fp16)[name = string("op_1008_cast_fp16")]; + fp32 var_1009_epsilon_0 = const()[name = string("op_1009_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1009_cast_fp16 = rsqrt(epsilon = var_1009_epsilon_0, x = var_1008_cast_fp16)[name = string("op_1009_cast_fp16")]; + tensor var_1010_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1009_cast_fp16)[name = string("op_1010_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004672)))]; + tensor var_1011_cast_fp16 = mul(x = var_1010_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1011_cast_fp16")]; + tensor var_1014_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1016_axes_0 = const()[name = string("op_1016_axes_0"), val = tensor([-1])]; + bool var_1016_keep_dims_0 = const()[name = string("op_1016_keep_dims_0"), val = bool(true)]; + tensor var_1016_cast_fp16 = reduce_mean(axes = var_1016_axes_0, keep_dims = var_1016_keep_dims_0, x = var_1014_cast_fp16)[name = string("op_1016_cast_fp16")]; + fp16 var_1017_to_fp16 = const()[name = string("op_1017_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1018_cast_fp16 = add(x = var_1016_cast_fp16, y = var_1017_to_fp16)[name = string("op_1018_cast_fp16")]; + fp32 var_1019_epsilon_0 = const()[name = string("op_1019_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1019_cast_fp16 = rsqrt(epsilon = var_1019_epsilon_0, x = var_1018_cast_fp16)[name = string("op_1019_cast_fp16")]; + tensor var_1020_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1019_cast_fp16)[name = string("op_1020_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21004864)))]; + tensor var_1021_cast_fp16 = mul(x = var_1020_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1021_cast_fp16")]; + tensor transpose_69_perm_0 = const()[name = string("transpose_69_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_70_perm_0 = const()[name = string("transpose_70_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71_perm_0 = const()[name = string("transpose_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_71 = transpose(perm = transpose_71_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_74")]; + tensor transpose_70 = transpose(perm = transpose_70_perm_0, x = var_1021_cast_fp16)[name = string("transpose_75")]; + tensor transpose_69 = transpose(perm = transpose_69_perm_0, x = var_1011_cast_fp16)[name = string("transpose_76")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_70, query = transpose_69, value = transpose_71)[name = string("out_15_cast_fp16")]; + tensor var_1032_perm_0 = const()[name = string("op_1032_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1033 = const()[name = string("op_1033"), val = tensor([1, 768, 384])]; + tensor var_1032_cast_fp16 = transpose(perm = var_1032_perm_0, x = out_15_cast_fp16)[name = string("transpose_73")]; + tensor input_179_cast_fp16 = reshape(shape = var_1033, x = var_1032_cast_fp16)[name = string("input_179_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21005056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21152576))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21153408)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_179_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1039_cast_fp16 = mul(x = var_978_cast_fp16, y = linear_46_cast_fp16)[name = string("op_1039_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_177_cast_fp16, y = var_1039_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor var_1041_axes_0 = const()[name = string("op_1041_axes_0"), val = tensor([1])]; + tensor var_1041_cast_fp16 = expand_dims(axes = var_1041_axes_0, x = var_972_cast_fp16_5)[name = string("op_1041_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_63_to_fp16, x = input_183_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1044_axes_0 = const()[name = string("op_1044_axes_0"), val = tensor([1])]; + tensor var_1044_cast_fp16 = expand_dims(axes = var_1044_axes_0, x = var_972_cast_fp16_4)[name = string("op_1044_cast_fp16")]; + fp16 var_1045_to_fp16 = const()[name = string("op_1045_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1046_cast_fp16 = add(x = var_1044_cast_fp16, y = var_1045_to_fp16)[name = string("op_1046_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1046_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1048_axes_0 = const()[name = string("op_1048_axes_0"), val = tensor([1])]; + tensor var_1048_cast_fp16 = expand_dims(axes = var_1048_axes_0, x = var_972_cast_fp16_3)[name = string("op_1048_cast_fp16")]; + tensor input_185_cast_fp16 = add(x = var_1047_cast_fp16, y = var_1048_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21154240))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21744128))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21747264)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_47_cast_fp16")]; + string input_189_mode_0 = const()[name = string("input_189_mode_0"), val = string("EXACT")]; + tensor input_189_cast_fp16 = gelu(mode = input_189_mode_0, x = linear_47_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21750400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22340288))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341120)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_189_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1061_cast_fp16 = mul(x = var_1041_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1061_cast_fp16")]; + tensor input_197_cast_fp16 = add(x = input_183_cast_fp16, y = var_1061_cast_fp16)[name = string("input_197_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22341952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22636928))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22638528)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_49_cast_fp16")]; + tensor var_1067_split_sizes_0 = const()[name = string("op_1067_split_sizes_0"), val = tensor([384, 384])]; + int32 var_1067_axis_0 = const()[name = string("op_1067_axis_0"), val = int32(-1)]; + tensor var_1067_cast_fp16_0, tensor var_1067_cast_fp16_1 = split(axis = var_1067_axis_0, split_sizes = var_1067_split_sizes_0, x = linear_49_cast_fp16)[name = string("op_1067_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_63_to_fp16, x = input_197_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1071_axes_0 = const()[name = string("op_1071_axes_0"), val = tensor([1])]; + tensor var_1071_cast_fp16 = expand_dims(axes = var_1071_axes_0, x = var_1067_cast_fp16_1)[name = string("op_1071_cast_fp16")]; + fp16 var_1072_to_fp16 = const()[name = string("op_1072_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1073_cast_fp16 = add(x = var_1071_cast_fp16, y = var_1072_to_fp16)[name = string("op_1073_cast_fp16")]; + tensor var_1074_cast_fp16 = mul(x = x_cast_fp16, y = var_1073_cast_fp16)[name = string("op_1074_cast_fp16")]; + tensor var_1075_axes_0 = const()[name = string("op_1075_axes_0"), val = tensor([1])]; + tensor var_1075_cast_fp16 = expand_dims(axes = var_1075_axes_0, x = var_1067_cast_fp16_0)[name = string("op_1075_cast_fp16")]; + tensor input_199_cast_fp16 = add(x = var_1074_cast_fp16, y = var_1075_cast_fp16)[name = string("input_199_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22640128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22787648))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22788480)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_199_cast_fp16)[name = string("linear_50_cast_fp16")]; + string input_203_mode_0 = const()[name = string("input_203_mode_0"), val = string("EXACT")]; + tensor input_203_cast_fp16 = gelu(mode = input_203_mode_0, x = linear_50_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16 = const()[name = string("module_model_out_3_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22789312)))]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor([0x1.258p-6, 0x1.7ccp-5])]; + tensor linear_51_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16, x = input_203_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1087_perm_0 = const()[name = string("op_1087_perm_0"), val = tensor([0, 2, 1])]; + string var_1087_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1087_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1087_cast_fp16 = transpose(perm = var_1087_perm_0, x = linear_51_cast_fp16)[name = string("transpose_72")]; + tensor velocity = cast(dtype = var_1087_cast_fp16_to_fp32_dtype_0, x = var_1087_cast_fp16)[name = string("cast_1")]; + } -> (velocity); +} \ No newline at end of file diff --git a/coreml/coreml/pitch_estimator.mlmodelc/weights/weight.bin b/coreml/coreml/pitch_estimator.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..43e4ec81f15bbe49ecffd5fe714ef1fc5bbd7d6b --- /dev/null +++ b/coreml/coreml/pitch_estimator.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d293c541b5c719f325597ad8e513f21d0470b44121dc7d797d426c5d3f2b84e2 +size 23927936 diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/analytics/coremldata.bin b/coreml/coreml/vector_context_encoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..9ed23fd8ebfc63df01d3b04daca7619cbefff638 --- /dev/null +++ b/coreml/coreml/vector_context_encoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:570664aec08bb343039555b5655729c28dbf82239fbcd40d8eac65c03431b3c5 +size 243 diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/coremldata.bin b/coreml/coreml/vector_context_encoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..246333210158f47f5c16f4f925249ca043cd5ee5 --- /dev/null +++ b/coreml/coreml/vector_context_encoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfc44cb0b6477bb9540b51e1bc9d70b6317e894d5f044d863deae35f55d321e1 +size 476 diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/model.mil b/coreml/coreml/vector_context_encoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..ad2811e8901c10ff15b8f7c7ff21f24fde90f076 --- /dev/null +++ b/coreml/coreml/vector_context_encoder.mlmodelc/model.mil @@ -0,0 +1,267 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func main(tensor span_context_mask, tensor span_context_phone_ids, tensor span_context_segment_ids) { + int32 var_15 = const()[name = string("op_15"), val = int32(0)]; + tensor span_context_mask_1 = not_equal(x = span_context_mask, y = var_15)[name = string("span_context_mask")]; + int32 var_30 = const()[name = string("op_30"), val = int32(-1)]; + int32 var_35 = const()[name = string("op_35"), val = int32(2)]; + int32 var_36 = const()[name = string("op_36"), val = int32(86)]; + int32 var_38 = const()[name = string("op_38"), val = int32(1)]; + int32 var_40 = const()[name = string("op_40"), val = int32(0)]; + tensor var_55 = maximum(x = span_context_phone_ids, y = var_40)[name = string("op_55")]; + tensor input_1 = minimum(x = var_55, y = var_36)[name = string("input_1")]; + tensor var_59 = maximum(x = span_context_segment_ids, y = var_40)[name = string("op_59")]; + tensor input_3 = minimum(x = var_59, y = var_35)[name = string("input_3")]; + string var_64_dtype_0 = const()[name = string("op_64_dtype_0"), val = string("int32")]; + bool var_65_exclusive_0 = const()[name = string("op_65_exclusive_0"), val = bool(false)]; + bool var_65_reverse_0 = const()[name = string("op_65_reverse_0"), val = bool(false)]; + tensor var_64 = cast(dtype = var_64_dtype_0, x = span_context_mask_1)[name = string("cast_13")]; + tensor var_65 = cumsum(axis = var_38, exclusive = var_65_exclusive_0, reverse = var_65_reverse_0, x = var_64)[name = string("op_65")]; + int32 var_66 = const()[name = string("op_66"), val = int32(1)]; + tensor var_67 = sub(x = var_65, y = var_66)[name = string("op_67")]; + tensor input_5 = maximum(x = var_67, y = var_40)[name = string("input_5")]; + int32 var_70_batch_dims_0 = const()[name = string("op_70_batch_dims_0"), val = int32(0)]; + bool var_70_validate_indices_0 = const()[name = string("op_70_validate_indices_0"), val = bool(false)]; + tensor module_flow_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44672))))[name = string("module_flow_phone_embed_weight_to_fp16_quantized")]; + string input_1_to_int16_dtype_0 = const()[name = string("input_1_to_int16_dtype_0"), val = string("int16")]; + string cast_24_dtype_0 = const()[name = string("cast_24_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor input_1_to_int16 = cast(dtype = input_1_to_int16_dtype_0, x = input_1)[name = string("cast_12")]; + tensor cast_24 = cast(dtype = cast_24_dtype_0, x = input_1_to_int16)[name = string("cast_11")]; + tensor greater_equal_0 = greater_equal(x = cast_24, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(87)]; + tensor add_0 = add(x = cast_24, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_24, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_10")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_9")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(87)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_70_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_70_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_70_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_70_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_70_batch_dims_0, indices = select_0_1, validate_indices = var_70_validate_indices_0, x = module_flow_phone_embed_weight_to_fp16_quantized)[name = string("op_70_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_72_axis_0 = const()[name = string("op_72_axis_0"), val = int32(0)]; + int32 var_72_batch_dims_0 = const()[name = string("op_72_batch_dims_0"), val = int32(0)]; + bool var_72_validate_indices_0 = const()[name = string("op_72_validate_indices_0"), val = bool(false)]; + tensor module_flow_span_segment_embed_weight_to_fp16 = const()[name = string("module_flow_span_segment_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44928)))]; + string input_3_to_uint16_dtype_0 = const()[name = string("input_3_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_3_to_uint16 = cast(dtype = input_3_to_uint16_dtype_0, x = input_3)[name = string("cast_8")]; + tensor var_72_cast_fp16_cast_uint16 = gather(axis = var_72_axis_0, batch_dims = var_72_batch_dims_0, indices = input_3_to_uint16, validate_indices = var_72_validate_indices_0, x = module_flow_span_segment_embed_weight_to_fp16)[name = string("op_72_cast_fp16_cast_uint16")]; + tensor var_73_cast_fp16 = add(x = op_70_cast_fp16_cast_uint16_cast_uint16, y = var_72_cast_fp16_cast_uint16)[name = string("op_73_cast_fp16")]; + int32 var_75_axis_0 = const()[name = string("op_75_axis_0"), val = int32(0)]; + int32 var_75_batch_dims_0 = const()[name = string("op_75_batch_dims_0"), val = int32(0)]; + bool var_75_validate_indices_0 = const()[name = string("op_75_validate_indices_0"), val = bool(false)]; + tensor module_flow_span_position_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441344))))[name = string("module_flow_span_position_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_7")]; + tensor var_75_cast_fp16_cast_uint16 = gather(axis = var_75_axis_0, batch_dims = var_75_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_75_validate_indices_0, x = module_flow_span_position_embed_weight_to_fp16_quantized)[name = string("op_75_cast_fp16_cast_uint16")]; + tensor hidden_1_cast_fp16 = add(x = var_73_cast_fp16, y = var_75_cast_fp16_cast_uint16)[name = string("hidden_1_cast_fp16")]; + tensor var_82_axes_0 = const()[name = string("op_82_axes_0"), val = tensor([-1])]; + tensor var_82 = expand_dims(axes = var_82_axes_0, x = span_context_mask_1)[name = string("op_82")]; + tensor var_83 = logical_not(x = var_82)[name = string("op_83")]; + fp16 var_18_to_fp16 = const()[name = string("op_18_to_fp16"), val = fp16(0x0p+0)]; + tensor hidden_3_cast_fp16 = select(a = var_18_to_fp16, b = hidden_1_cast_fp16, cond = var_83)[name = string("hidden_3_cast_fp16")]; + tensor input_7_axes_0 = const()[name = string("input_7_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(442944)))]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(444032)))]; + fp16 var_28_to_fp16 = const()[name = string("op_28_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_7_cast_fp16 = layer_norm(axes = input_7_axes_0, beta = module_flow_span_ordered_encoder_blocks_0_norm_attention_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_0_norm_attention_weight_to_fp16, x = hidden_3_cast_fp16)[name = string("input_7_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445120))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1231616))))[name = string("module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1234752)))]; + tensor linear_0_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_qkv_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_qkv_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_100 = const()[name = string("op_100"), val = tensor([1, 512, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_100, x = linear_0_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_102_split_sizes_0 = const()[name = string("op_102_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_102_axis_0 = const()[name = string("op_102_axis_0"), val = int32(2)]; + tensor var_102_cast_fp16_0, tensor var_102_cast_fp16_1, tensor var_102_cast_fp16_2 = split(axis = var_102_axis_0, split_sizes = var_102_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_102_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_102_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_102_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_102_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor value_3_perm_0 = const()[name = string("value_3_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_110_transpose_x_0 = const()[name = string("op_110_transpose_x_0"), val = bool(false)]; + bool var_110_transpose_y_0 = const()[name = string("op_110_transpose_y_0"), val = bool(false)]; + tensor transpose_8_perm_0 = const()[name = string("transpose_8_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_9_perm_0 = const()[name = string("transpose_9_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_9 = transpose(perm = transpose_9_perm_0, x = squeeze_1_cast_fp16)[name = string("transpose_18")]; + tensor transpose_8 = transpose(perm = transpose_8_perm_0, x = squeeze_0_cast_fp16)[name = string("transpose_19")]; + tensor var_110_cast_fp16 = matmul(transpose_x = var_110_transpose_x_0, transpose_y = var_110_transpose_y_0, x = transpose_8, y = transpose_9)[name = string("op_110_cast_fp16")]; + fp16 var_111_to_fp16 = const()[name = string("op_111_to_fp16"), val = fp16(0x1p-3)]; + tensor scores_1_cast_fp16 = mul(x = var_110_cast_fp16, y = var_111_to_fp16)[name = string("scores_1_cast_fp16")]; + tensor var_114_axes_0 = const()[name = string("op_114_axes_0"), val = tensor([1])]; + tensor var_114 = expand_dims(axes = var_114_axes_0, x = span_context_mask_1)[name = string("op_114")]; + tensor var_115_axes_0 = const()[name = string("op_115_axes_0"), val = tensor([2])]; + tensor var_115 = expand_dims(axes = var_115_axes_0, x = var_114)[name = string("op_115")]; + tensor var_117 = logical_not(x = var_115)[name = string("op_117")]; + fp16 var_23_to_fp16 = const()[name = string("op_23_to_fp16"), val = fp16(-inf)]; + tensor scores_3_cast_fp16 = select(a = var_23_to_fp16, b = scores_1_cast_fp16, cond = var_117)[name = string("scores_3_cast_fp16")]; + tensor var_119_cast_fp16 = softmax(axis = var_30, x = scores_3_cast_fp16)[name = string("op_119_cast_fp16")]; + string var_120_to_fp16_dtype_0 = const()[name = string("op_120_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_115_to_fp16 = cast(dtype = var_120_to_fp16_dtype_0, x = var_115)[name = string("cast_6")]; + tensor weights_1_cast_fp16 = mul(x = var_119_cast_fp16, y = var_115_to_fp16)[name = string("weights_1_cast_fp16")]; + tensor var_123_axes_0 = const()[name = string("op_123_axes_0"), val = tensor([-1])]; + bool var_123_keep_dims_0 = const()[name = string("op_123_keep_dims_0"), val = bool(true)]; + tensor var_123_cast_fp16 = reduce_sum(axes = var_123_axes_0, keep_dims = var_123_keep_dims_0, x = weights_1_cast_fp16)[name = string("op_123_cast_fp16")]; + fp16 var_21_to_fp16 = const()[name = string("op_21_to_fp16"), val = fp16(0x1.1p-20)]; + tensor var_124_cast_fp16 = maximum(x = var_123_cast_fp16, y = var_21_to_fp16)[name = string("op_124_cast_fp16")]; + tensor weights_3_cast_fp16 = real_div(x = weights_1_cast_fp16, y = var_124_cast_fp16)[name = string("weights_3_cast_fp16")]; + bool var_126_transpose_x_0 = const()[name = string("op_126_transpose_x_0"), val = bool(false)]; + bool var_126_transpose_y_0 = const()[name = string("op_126_transpose_y_0"), val = bool(false)]; + tensor value_3_cast_fp16 = transpose(perm = value_3_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_17")]; + tensor var_126_cast_fp16 = matmul(transpose_x = var_126_transpose_x_0, transpose_y = var_126_transpose_y_0, x = weights_3_cast_fp16, y = value_3_cast_fp16)[name = string("op_126_cast_fp16")]; + tensor var_127_perm_0 = const()[name = string("op_127_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_128 = const()[name = string("op_128"), val = tensor([1, 512, 512])]; + tensor var_127_cast_fp16 = transpose(perm = var_127_perm_0, x = var_126_cast_fp16)[name = string("transpose_16")]; + tensor input_9_cast_fp16 = reshape(shape = var_128, x = var_127_cast_fp16)[name = string("input_9_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1237888))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1500096))))[name = string("module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1501184)))]; + tensor linear_1_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_attention_proj_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_attention_proj_weight_to_fp16_quantized, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = hidden_3_cast_fp16, y = linear_1_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_axes_0 = const()[name = string("input_15_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1502272)))]; + tensor module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1503360)))]; + tensor input_15_cast_fp16 = layer_norm(axes = input_15_axes_0, beta = module_flow_span_ordered_encoder_blocks_0_norm_feedforward_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_0_norm_feedforward_weight_to_fp16, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1504448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2553088))))[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2557248)))]; + tensor linear_2_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_feedforward_0_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_feedforward_0_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("linear_2_cast_fp16")]; + string input_19_mode_0 = const()[name = string("input_19_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_19_cast_fp16 = gelu(mode = input_19_mode_0, x = linear_2_cast_fp16)[name = string("input_19_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2561408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3610048))))[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3611136)))]; + tensor linear_3_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_0_feedforward_2_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_0_feedforward_2_weight_to_fp16_quantized, x = input_19_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor hidden_5_cast_fp16 = add(x = input_13_cast_fp16, y = linear_3_cast_fp16)[name = string("hidden_5_cast_fp16")]; + tensor hidden_7_cast_fp16 = select(a = var_18_to_fp16, b = hidden_5_cast_fp16, cond = var_83)[name = string("hidden_7_cast_fp16")]; + tensor input_23_axes_0 = const()[name = string("input_23_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3612224)))]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3613312)))]; + tensor input_23_cast_fp16 = layer_norm(axes = input_23_axes_0, beta = module_flow_span_ordered_encoder_blocks_1_norm_attention_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_1_norm_attention_weight_to_fp16, x = hidden_7_cast_fp16)[name = string("input_23_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3614400))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4400896))))[name = string("module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4404032)))]; + tensor linear_4_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_qkv_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_qkv_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_168 = const()[name = string("op_168"), val = tensor([1, 512, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_168, x = linear_4_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_170_split_sizes_0 = const()[name = string("op_170_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_170_axis_0 = const()[name = string("op_170_axis_0"), val = int32(2)]; + tensor var_170_cast_fp16_0, tensor var_170_cast_fp16_1, tensor var_170_cast_fp16_2 = split(axis = var_170_axis_0, split_sizes = var_170_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_170_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_170_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_170_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_170_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor value_perm_0 = const()[name = string("value_perm_0"), val = tensor([0, 2, -3, -1])]; + bool var_178_transpose_x_0 = const()[name = string("op_178_transpose_x_0"), val = bool(false)]; + bool var_178_transpose_y_0 = const()[name = string("op_178_transpose_y_0"), val = bool(false)]; + tensor transpose_10_perm_0 = const()[name = string("transpose_10_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_11_perm_0 = const()[name = string("transpose_11_perm_0"), val = tensor([0, 2, -1, -3])]; + tensor transpose_11 = transpose(perm = transpose_11_perm_0, x = squeeze_4_cast_fp16)[name = string("transpose_14")]; + tensor transpose_10 = transpose(perm = transpose_10_perm_0, x = squeeze_3_cast_fp16)[name = string("transpose_15")]; + tensor var_178_cast_fp16 = matmul(transpose_x = var_178_transpose_x_0, transpose_y = var_178_transpose_y_0, x = transpose_10, y = transpose_11)[name = string("op_178_cast_fp16")]; + fp16 var_179_to_fp16 = const()[name = string("op_179_to_fp16"), val = fp16(0x1p-3)]; + tensor scores_5_cast_fp16 = mul(x = var_178_cast_fp16, y = var_179_to_fp16)[name = string("scores_5_cast_fp16")]; + tensor scores_cast_fp16 = select(a = var_23_to_fp16, b = scores_5_cast_fp16, cond = var_117)[name = string("scores_cast_fp16")]; + tensor var_187_cast_fp16 = softmax(axis = var_30, x = scores_cast_fp16)[name = string("op_187_cast_fp16")]; + tensor weights_5_cast_fp16 = mul(x = var_187_cast_fp16, y = var_115_to_fp16)[name = string("weights_5_cast_fp16")]; + tensor var_191_axes_0 = const()[name = string("op_191_axes_0"), val = tensor([-1])]; + bool var_191_keep_dims_0 = const()[name = string("op_191_keep_dims_0"), val = bool(true)]; + tensor var_191_cast_fp16 = reduce_sum(axes = var_191_axes_0, keep_dims = var_191_keep_dims_0, x = weights_5_cast_fp16)[name = string("op_191_cast_fp16")]; + tensor var_192_cast_fp16 = maximum(x = var_191_cast_fp16, y = var_21_to_fp16)[name = string("op_192_cast_fp16")]; + tensor weights_cast_fp16 = real_div(x = weights_5_cast_fp16, y = var_192_cast_fp16)[name = string("weights_cast_fp16")]; + bool var_194_transpose_x_0 = const()[name = string("op_194_transpose_x_0"), val = bool(false)]; + bool var_194_transpose_y_0 = const()[name = string("op_194_transpose_y_0"), val = bool(false)]; + tensor value_cast_fp16 = transpose(perm = value_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_13")]; + tensor var_194_cast_fp16 = matmul(transpose_x = var_194_transpose_x_0, transpose_y = var_194_transpose_y_0, x = weights_cast_fp16, y = value_cast_fp16)[name = string("op_194_cast_fp16")]; + tensor var_195_perm_0 = const()[name = string("op_195_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_196 = const()[name = string("op_196"), val = tensor([1, 512, 512])]; + tensor var_195_cast_fp16 = transpose(perm = var_195_perm_0, x = var_194_cast_fp16)[name = string("transpose_12")]; + tensor input_25_cast_fp16 = reshape(shape = var_196, x = var_195_cast_fp16)[name = string("input_25_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4407168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4669376))))[name = string("module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4670464)))]; + tensor linear_5_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_attention_proj_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_attention_proj_weight_to_fp16_quantized, x = input_25_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor input_29_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_5_cast_fp16)[name = string("input_29_cast_fp16")]; + tensor input_31_axes_0 = const()[name = string("input_31_axes_0"), val = tensor([-1])]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4671552)))]; + tensor module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4672640)))]; + tensor input_31_cast_fp16 = layer_norm(axes = input_31_axes_0, beta = module_flow_span_ordered_encoder_blocks_1_norm_feedforward_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_ordered_encoder_blocks_1_norm_feedforward_weight_to_fp16, x = input_29_cast_fp16)[name = string("input_31_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4673728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5722368))))[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5726528)))]; + tensor linear_6_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_feedforward_0_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_feedforward_0_weight_to_fp16_quantized, x = input_31_cast_fp16)[name = string("linear_6_cast_fp16")]; + string input_35_mode_0 = const()[name = string("input_35_mode_0"), val = string("TANH_APPROXIMATION")]; + tensor input_35_cast_fp16 = gelu(mode = input_35_mode_0, x = linear_6_cast_fp16)[name = string("input_35_cast_fp16")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5730688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6779328))))[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized")]; + tensor module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16 = const()[name = string("module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6780416)))]; + tensor linear_7_cast_fp16 = linear(bias = module_flow_span_ordered_encoder_blocks_1_feedforward_2_bias_to_fp16, weight = module_flow_span_ordered_encoder_blocks_1_feedforward_2_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor hidden_9_cast_fp16 = add(x = input_29_cast_fp16, y = linear_7_cast_fp16)[name = string("hidden_9_cast_fp16")]; + tensor hidden_cast_fp16 = select(a = var_18_to_fp16, b = hidden_9_cast_fp16, cond = var_83)[name = string("hidden_cast_fp16")]; + tensor var_224 = equal(x = input_3, y = var_40)[name = string("op_224")]; + tensor var_225 = logical_and(x = span_context_mask_1, y = var_224)[name = string("op_225")]; + tensor var_228_axes_0 = const()[name = string("op_228_axes_0"), val = tensor([1])]; + bool var_228_keep_dims_0 = const()[name = string("op_228_keep_dims_0"), val = bool(true)]; + string weight_15_to_fp16_dtype_0 = const()[name = string("weight_15_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_225_to_fp16 = cast(dtype = weight_15_to_fp16_dtype_0, x = var_225)[name = string("cast_5")]; + tensor var_228_cast_fp16 = reduce_sum(axes = var_228_axes_0, keep_dims = var_228_keep_dims_0, x = var_225_to_fp16)[name = string("op_228_cast_fp16")]; + fp16 var_17_to_fp16 = const()[name = string("op_17_to_fp16"), val = fp16(0x1p+0)]; + tensor var_229_cast_fp16 = maximum(x = var_228_cast_fp16, y = var_17_to_fp16)[name = string("op_229_cast_fp16")]; + tensor weight_17_cast_fp16 = real_div(x = var_225_to_fp16, y = var_229_cast_fp16)[name = string("weight_17_cast_fp16")]; + tensor var_231_axes_0 = const()[name = string("op_231_axes_0"), val = tensor([-1])]; + tensor var_231_cast_fp16 = expand_dims(axes = var_231_axes_0, x = weight_17_cast_fp16)[name = string("op_231_cast_fp16")]; + tensor var_232_cast_fp16 = mul(x = hidden_cast_fp16, y = var_231_cast_fp16)[name = string("op_232_cast_fp16")]; + tensor var_234_axes_0 = const()[name = string("op_234_axes_0"), val = tensor([1])]; + bool var_234_keep_dims_0 = const()[name = string("op_234_keep_dims_0"), val = bool(false)]; + tensor var_234_cast_fp16 = reduce_sum(axes = var_234_axes_0, keep_dims = var_234_keep_dims_0, x = var_232_cast_fp16)[name = string("op_234_cast_fp16")]; + tensor var_235 = equal(x = input_3, y = var_38)[name = string("op_235")]; + tensor var_236 = logical_and(x = span_context_mask_1, y = var_235)[name = string("op_236")]; + tensor var_239_axes_0 = const()[name = string("op_239_axes_0"), val = tensor([1])]; + bool var_239_keep_dims_0 = const()[name = string("op_239_keep_dims_0"), val = bool(true)]; + string weight_19_to_fp16_dtype_0 = const()[name = string("weight_19_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_236_to_fp16 = cast(dtype = weight_19_to_fp16_dtype_0, x = var_236)[name = string("cast_4")]; + tensor var_239_cast_fp16 = reduce_sum(axes = var_239_axes_0, keep_dims = var_239_keep_dims_0, x = var_236_to_fp16)[name = string("op_239_cast_fp16")]; + tensor var_240_cast_fp16 = maximum(x = var_239_cast_fp16, y = var_17_to_fp16)[name = string("op_240_cast_fp16")]; + tensor weight_21_cast_fp16 = real_div(x = var_236_to_fp16, y = var_240_cast_fp16)[name = string("weight_21_cast_fp16")]; + tensor var_242_axes_0 = const()[name = string("op_242_axes_0"), val = tensor([-1])]; + tensor var_242_cast_fp16 = expand_dims(axes = var_242_axes_0, x = weight_21_cast_fp16)[name = string("op_242_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = hidden_cast_fp16, y = var_242_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor var_245_axes_0 = const()[name = string("op_245_axes_0"), val = tensor([1])]; + bool var_245_keep_dims_0 = const()[name = string("op_245_keep_dims_0"), val = bool(false)]; + tensor var_245_cast_fp16 = reduce_sum(axes = var_245_axes_0, keep_dims = var_245_keep_dims_0, x = var_243_cast_fp16)[name = string("op_245_cast_fp16")]; + tensor var_246 = equal(x = input_3, y = var_35)[name = string("op_246")]; + tensor var_247 = logical_and(x = span_context_mask_1, y = var_246)[name = string("op_247")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([1])]; + bool var_250_keep_dims_0 = const()[name = string("op_250_keep_dims_0"), val = bool(true)]; + string weight_23_to_fp16_dtype_0 = const()[name = string("weight_23_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_247_to_fp16 = cast(dtype = weight_23_to_fp16_dtype_0, x = var_247)[name = string("cast_3")]; + tensor var_250_cast_fp16 = reduce_sum(axes = var_250_axes_0, keep_dims = var_250_keep_dims_0, x = var_247_to_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = maximum(x = var_250_cast_fp16, y = var_17_to_fp16)[name = string("op_251_cast_fp16")]; + tensor weight_25_cast_fp16 = real_div(x = var_247_to_fp16, y = var_251_cast_fp16)[name = string("weight_25_cast_fp16")]; + tensor var_253_axes_0 = const()[name = string("op_253_axes_0"), val = tensor([-1])]; + tensor var_253_cast_fp16 = expand_dims(axes = var_253_axes_0, x = weight_25_cast_fp16)[name = string("op_253_cast_fp16")]; + tensor var_254_cast_fp16 = mul(x = hidden_cast_fp16, y = var_253_cast_fp16)[name = string("op_254_cast_fp16")]; + tensor var_256_axes_0 = const()[name = string("op_256_axes_0"), val = tensor([1])]; + bool var_256_keep_dims_0 = const()[name = string("op_256_keep_dims_0"), val = bool(false)]; + tensor var_256_cast_fp16 = reduce_sum(axes = var_256_axes_0, keep_dims = var_256_keep_dims_0, x = var_254_cast_fp16)[name = string("op_256_cast_fp16")]; + bool input_39_interleave_0 = const()[name = string("input_39_interleave_0"), val = bool(false)]; + tensor input_39_cast_fp16 = concat(axis = var_30, interleave = input_39_interleave_0, values = (var_234_cast_fp16, var_245_cast_fp16, var_256_cast_fp16))[name = string("input_39_cast_fp16")]; + tensor module_flow_span_segment_pool_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6781504))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7568000))))[name = string("module_flow_span_segment_pool_proj_weight_to_fp16_quantized")]; + tensor module_flow_span_segment_pool_proj_bias_to_fp16 = const()[name = string("module_flow_span_segment_pool_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7569088)))]; + tensor linear_8_cast_fp16 = linear(bias = module_flow_span_segment_pool_proj_bias_to_fp16, weight = module_flow_span_segment_pool_proj_weight_to_fp16_quantized, x = input_39_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor pooled_axes_0 = const()[name = string("pooled_axes_0"), val = tensor([-1])]; + tensor module_flow_span_context_norm_weight_to_fp16 = const()[name = string("module_flow_span_context_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7570176)))]; + tensor module_flow_span_context_norm_bias_to_fp16 = const()[name = string("module_flow_span_context_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7571264)))]; + tensor pooled_cast_fp16 = layer_norm(axes = pooled_axes_0, beta = module_flow_span_context_norm_bias_to_fp16, epsilon = var_28_to_fp16, gamma = module_flow_span_context_norm_weight_to_fp16, x = linear_8_cast_fp16)[name = string("pooled_cast_fp16")]; + tensor var_38_list = const()[name = string("op_38_list"), val = tensor([1])]; + bool reduce_max_0_keep_dims_0 = const()[name = string("reduce_max_0_keep_dims_0"), val = bool(true)]; + tensor reduce_max_0 = reduce_max(axes = var_38_list, keep_dims = reduce_max_0_keep_dims_0, x = var_64)[name = string("reduce_max_0")]; + string var_266_dtype_0 = const()[name = string("op_266_dtype_0"), val = string("bool")]; + tensor var_266 = cast(dtype = var_266_dtype_0, x = reduce_max_0)[name = string("cast_2")]; + tensor var_267 = logical_not(x = var_266)[name = string("op_267")]; + tensor var_268_cast_fp16 = select(a = var_18_to_fp16, b = pooled_cast_fp16, cond = var_267)[name = string("op_268_cast_fp16")]; + string var_268_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_268_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor span_context_hidden = cast(dtype = var_268_cast_fp16_to_fp32_dtype_0, x = var_268_cast_fp16)[name = string("cast_1")]; + } -> (span_context_hidden); +} \ No newline at end of file diff --git a/coreml/coreml/vector_context_encoder.mlmodelc/weights/weight.bin b/coreml/coreml/vector_context_encoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..ca19050731a69bcfffbe19c724d7608bfb4ac8a3 --- /dev/null +++ b/coreml/coreml/vector_context_encoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f686199febbe315c2102abd521d33fb629813d2293afe9daa08c94828d14d66e +size 7572352 diff --git a/coreml/coreml/vector_estimator.mlmodelc/analytics/coremldata.bin b/coreml/coreml/vector_estimator.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0baf243b4edbe3da3f2b5de1f0f3014f5c0feeff --- /dev/null +++ b/coreml/coreml/vector_estimator.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b7a8c719108fdbc99b2a35e71f88e663d83fb6677ae0ac892fc162357b6a2a1a +size 243 diff --git a/coreml/coreml/vector_estimator.mlmodelc/coremldata.bin b/coreml/coreml/vector_estimator.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b00101a508bac72cba29b77727afc2ae9470b549 --- /dev/null +++ b/coreml/coreml/vector_estimator.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e9b0d967ca6ab8cace2814a61e7c1561d9a45c7b65b2373825b6d53e10b9b47f +size 3845 diff --git a/coreml/coreml/vector_estimator.mlmodelc/model.mil b/coreml/coreml/vector_estimator.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..cb347e8d6154e62d88083bc7b73719f556df5a8c --- /dev/null +++ b/coreml/coreml/vector_estimator.mlmodelc/model.mil @@ -0,0 +1,8116 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func vector_estimator(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60252416))), scale = tensor([[[0x1.1fp-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 1120])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60776768)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 1120, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 1120, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 1120, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 1120, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 1120, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 1120, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 1120, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 1120, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 1120, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 1120, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 1120, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 1120, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 1120, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 1120, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func vector_estimator_128(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219264))), scale = tensor([[[0x1.1c4p-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 224])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828800)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 224, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 224, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 224, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 224, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 224, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 224, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 224, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 224, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 224, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 224, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 224, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 224, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 224, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 224, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func vector_estimator_256(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264256))), scale = tensor([[[0x1.1c4p-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 352])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59395392)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 352, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 352, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 352, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 352, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 352, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 352, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 352, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 352, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 352, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 352, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 352, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 352, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 352, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 352, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func vector_estimator_384(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59396160))), scale = tensor([[[0x1.1c4p-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 480])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59592832)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 480, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 480, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 480, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 480, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 480, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 480, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 480, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 480, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 480, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 480, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 480, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 480, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 480, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 480, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func vector_estimator_512(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59593856))), scale = tensor([[[0x1.1fp-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 608])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59856064)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 608, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 608, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 608, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 608, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 608, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 608, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 608, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 608, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 608, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 608, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 608, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 608, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 608, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 608, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); + func vector_estimator_768(tensor delivery_present, tensor delivery_values, tensor expanded_boundary_event_ids, tensor expanded_melody, tensor expanded_modifier_event_ids, tensor expanded_phone_ids, tensor expanded_phone_log_duration, tensor expanded_phone_phase, tensor expanded_sentence_type_ids, tensor language_id, tensor latent_mask, tensor noise, tensor prefix_latents, tensor prefix_mask, tensor span_context_hidden, tensor time, tensor voice_id) { + int32 var_34 = const()[name = string("op_34"), val = int32(0)]; + tensor present = not_equal(x = delivery_present, y = var_34)[name = string("present")]; + int32 var_36 = const()[name = string("op_36"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_36)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + tensor prefix_mask_1 = not_equal(x = prefix_mask, y = var_38)[name = string("prefix_mask_1")]; + int32 var_59 = const()[name = string("op_59"), val = int32(-1)]; + int32 var_74 = const()[name = string("op_74"), val = int32(1)]; + int32 var_88 = const()[name = string("op_88"), val = int32(7)]; + tensor var_89 = mul(x = voice_id, y = var_88)[name = string("op_89")]; + tensor index = add(x = var_89, y = language_id)[name = string("index")]; + int32 value_13_batch_dims_0 = const()[name = string("value_13_batch_dims_0"), val = int32(0)]; + bool value_13_validate_indices_0 = const()[name = string("value_13_validate_indices_0"), val = bool(false)]; + tensor module_conditions_identity_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50304))))[name = string("module_conditions_identity_to_fp16_quantized")]; + string index_to_int16_dtype_0 = const()[name = string("index_to_int16_dtype_0"), val = string("int16")]; + string cast_50_dtype_0 = const()[name = string("cast_50_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor index_to_int16 = cast(dtype = index_to_int16_dtype_0, x = index)[name = string("cast_21")]; + tensor cast_50 = cast(dtype = cast_50_dtype_0, x = index_to_int16)[name = string("cast_20")]; + tensor greater_equal_0 = greater_equal(x = cast_50, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(98)]; + tensor add_0 = add(x = cast_50, y = slice_by_index_0)[name = string("add_0")]; + tensor select_0 = select(a = cast_50, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_19")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_18")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(98)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0_1)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 value_13_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("value_13_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor value_13_cast_fp16_cast_uint16_cast_uint16 = gather(axis = value_13_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = value_13_batch_dims_0, indices = select_0_1, validate_indices = value_13_validate_indices_0, x = module_conditions_identity_to_fp16_quantized)[name = string("value_13_cast_fp16_cast_uint16_cast_uint16")]; + int32 value_1_axis_0 = const()[name = string("value_1_axis_0"), val = int32(0)]; + int32 value_1_batch_dims_0 = const()[name = string("value_1_batch_dims_0"), val = int32(0)]; + bool value_1_validate_indices_0 = const()[name = string("value_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_baseline_to_fp16 = const()[name = string("module_conditions_prosody_baseline_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50624)))]; + string index_to_uint16_dtype_0 = const()[name = string("index_to_uint16_dtype_0"), val = string("uint16")]; + tensor index_to_uint16 = cast(dtype = index_to_uint16_dtype_0, x = index)[name = string("cast_17")]; + tensor value_1_cast_fp16_cast_uint16 = gather(axis = value_1_axis_0, batch_dims = value_1_batch_dims_0, indices = index_to_uint16, validate_indices = value_1_validate_indices_0, x = module_conditions_prosody_baseline_to_fp16)[name = string("value_1_cast_fp16_cast_uint16")]; + int32 value_5_axis_0 = const()[name = string("value_5_axis_0"), val = int32(0)]; + int32 value_5_batch_dims_0 = const()[name = string("value_5_batch_dims_0"), val = int32(0)]; + bool value_5_validate_indices_0 = const()[name = string("value_5_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_delta_to_fp16 = const()[name = string("module_conditions_prosody_delta_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56960)))]; + tensor value_5_cast_fp16_cast_uint16 = gather(axis = value_5_axis_0, batch_dims = value_5_batch_dims_0, indices = index_to_uint16, validate_indices = value_5_validate_indices_0, x = module_conditions_prosody_delta_to_fp16)[name = string("value_5_cast_fp16_cast_uint16")]; + int32 value_9_axis_0 = const()[name = string("value_9_axis_0"), val = int32(0)]; + int32 value_9_batch_dims_0 = const()[name = string("value_9_batch_dims_0"), val = int32(0)]; + bool value_9_validate_indices_0 = const()[name = string("value_9_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_feature_mask_to_fp16 = const()[name = string("module_conditions_prosody_feature_mask_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63296)))]; + tensor value_9_cast_fp16_cast_uint16 = gather(axis = value_9_axis_0, batch_dims = value_9_batch_dims_0, indices = index_to_uint16, validate_indices = value_9_validate_indices_0, x = module_conditions_prosody_feature_mask_to_fp16)[name = string("value_9_cast_fp16_cast_uint16")]; + int32 prosody_confidence_1_axis_0 = const()[name = string("prosody_confidence_1_axis_0"), val = int32(0)]; + int32 prosody_confidence_1_batch_dims_0 = const()[name = string("prosody_confidence_1_batch_dims_0"), val = int32(0)]; + bool prosody_confidence_1_validate_indices_0 = const()[name = string("prosody_confidence_1_validate_indices_0"), val = bool(false)]; + tensor module_conditions_prosody_confidence_to_fp16 = const()[name = string("module_conditions_prosody_confidence_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69632)))]; + tensor prosody_confidence_1_cast_fp16_cast_uint16 = gather(axis = prosody_confidence_1_axis_0, batch_dims = prosody_confidence_1_batch_dims_0, indices = index_to_uint16, validate_indices = prosody_confidence_1_validate_indices_0, x = module_conditions_prosody_confidence_to_fp16)[name = string("prosody_confidence_1_cast_fp16_cast_uint16")]; + fp16 fill_like_1_value_0_to_fp16 = const()[name = string("fill_like_1_value_0_to_fp16"), val = fp16(0x1p+0)]; + tensor fill_like_1_cast_fp16 = fill_like(ref_tensor = voice_id, value = fill_like_1_value_0_to_fp16)[name = string("fill_like_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = string("input_1_perm_0"), val = tensor([0, 2, 1])]; + string noise_to_fp16_dtype_0 = const()[name = string("noise_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(69952))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(82304))))[name = string("module_model_latent_in_weight_to_fp16_quantized")]; + tensor module_model_latent_in_bias_to_fp16 = const()[name = string("module_model_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(83392)))]; + tensor noise_to_fp16 = cast(dtype = noise_to_fp16_dtype_0, x = noise)[name = string("cast_16")]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = noise_to_fp16)[name = string("transpose_158")]; + tensor linear_0_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_1_cast_fp16)[name = string("linear_0_cast_fp16")]; + int32 var_146_axis_0 = const()[name = string("op_146_axis_0"), val = int32(0)]; + int32 var_146_batch_dims_0 = const()[name = string("op_146_batch_dims_0"), val = int32(0)]; + bool var_146_validate_indices_0 = const()[name = string("op_146_validate_indices_0"), val = bool(false)]; + tensor module_model_phone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129088))))[name = string("module_model_phone_embed_weight_to_fp16_quantized")]; + string expanded_phone_ids_to_uint16_dtype_0 = const()[name = string("expanded_phone_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_phone_ids_to_uint16 = cast(dtype = expanded_phone_ids_to_uint16_dtype_0, x = expanded_phone_ids)[name = string("cast_15")]; + tensor var_146_cast_fp16_cast_uint16 = gather(axis = var_146_axis_0, batch_dims = var_146_batch_dims_0, indices = expanded_phone_ids_to_uint16, validate_indices = var_146_validate_indices_0, x = module_model_phone_embed_weight_to_fp16_quantized)[name = string("op_146_cast_fp16_cast_uint16")]; + tensor hidden_9_cast_fp16 = add(x = linear_0_cast_fp16, y = var_146_cast_fp16_cast_uint16)[name = string("hidden_9_cast_fp16")]; + int32 var_152_axis_0 = const()[name = string("op_152_axis_0"), val = int32(0)]; + int32 var_152_batch_dims_0 = const()[name = string("op_152_batch_dims_0"), val = int32(0)]; + bool var_152_validate_indices_0 = const()[name = string("op_152_validate_indices_0"), val = bool(false)]; + tensor module_model_boundary_event_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(129344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174464))))[name = string("module_model_boundary_event_embed_weight_to_fp16_quantized")]; + string expanded_boundary_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_boundary_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_boundary_event_ids_to_uint16 = cast(dtype = expanded_boundary_event_ids_to_uint16_dtype_0, x = expanded_boundary_event_ids)[name = string("cast_14")]; + tensor var_152_cast_fp16_cast_uint16 = gather(axis = var_152_axis_0, batch_dims = var_152_batch_dims_0, indices = expanded_boundary_event_ids_to_uint16, validate_indices = var_152_validate_indices_0, x = module_model_boundary_event_embed_weight_to_fp16_quantized)[name = string("op_152_cast_fp16_cast_uint16")]; + int32 var_157_axis_0 = const()[name = string("op_157_axis_0"), val = int32(0)]; + int32 var_157_batch_dims_0 = const()[name = string("op_157_batch_dims_0"), val = int32(0)]; + bool var_157_validate_indices_0 = const()[name = string("op_157_validate_indices_0"), val = bool(false)]; + tensor module_model_modifier_event_table_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207552))))[name = string("module_model_modifier_event_table_weight_to_fp16_quantized")]; + string expanded_modifier_event_ids_to_uint16_dtype_0 = const()[name = string("expanded_modifier_event_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_modifier_event_ids_to_uint16 = cast(dtype = expanded_modifier_event_ids_to_uint16_dtype_0, x = expanded_modifier_event_ids)[name = string("cast_13")]; + tensor var_157_cast_fp16_cast_uint16 = gather(axis = var_157_axis_0, batch_dims = var_157_batch_dims_0, indices = expanded_modifier_event_ids_to_uint16, validate_indices = var_157_validate_indices_0, x = module_model_modifier_event_table_weight_to_fp16_quantized)[name = string("op_157_cast_fp16_cast_uint16")]; + tensor hidden_7_cast_fp16 = add(x = var_152_cast_fp16_cast_uint16, y = var_157_cast_fp16_cast_uint16)[name = string("hidden_7_cast_fp16")]; + int32 input_9_axis_0 = const()[name = string("input_9_axis_0"), val = int32(-1)]; + string expanded_phone_phase_to_fp16_dtype_0 = const()[name = string("expanded_phone_phase_to_fp16_dtype_0"), val = string("fp16")]; + string expanded_phone_log_duration_to_fp16_dtype_0 = const()[name = string("expanded_phone_log_duration_to_fp16_dtype_0"), val = string("fp16")]; + tensor expanded_phone_phase_to_fp16 = cast(dtype = expanded_phone_phase_to_fp16_dtype_0, x = expanded_phone_phase)[name = string("cast_11")]; + tensor expanded_phone_log_duration_to_fp16 = cast(dtype = expanded_phone_log_duration_to_fp16_dtype_0, x = expanded_phone_log_duration)[name = string("cast_12")]; + tensor input_9_cast_fp16 = stack(axis = input_9_axis_0, values = (expanded_phone_phase_to_fp16, expanded_phone_log_duration_to_fp16))[name = string("input_9_cast_fp16")]; + tensor module_model_local_timing_proj_weight_to_fp16 = const()[name = string("module_model_local_timing_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(207744)))]; + tensor module_model_local_timing_proj_bias_to_fp16 = const()[name = string("module_model_local_timing_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209856)))]; + tensor linear_1_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_local_timing_proj_weight_to_fp16, x = input_9_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_168_cast_fp16 = add(x = hidden_7_cast_fp16, y = linear_1_cast_fp16)[name = string("op_168_cast_fp16")]; + tensor hidden_11_cast_fp16 = add(x = hidden_9_cast_fp16, y = var_168_cast_fp16)[name = string("hidden_11_cast_fp16")]; + int32 var_172_axis_0 = const()[name = string("op_172_axis_0"), val = int32(0)]; + int32 var_172_batch_dims_0 = const()[name = string("op_172_batch_dims_0"), val = int32(0)]; + bool var_172_validate_indices_0 = const()[name = string("op_172_validate_indices_0"), val = bool(false)]; + tensor module_model_sentence_type_embed_weight_to_fp16 = const()[name = string("module_model_sentence_type_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(210944)))]; + string expanded_sentence_type_ids_to_uint16_dtype_0 = const()[name = string("expanded_sentence_type_ids_to_uint16_dtype_0"), val = string("uint16")]; + tensor expanded_sentence_type_ids_to_uint16 = cast(dtype = expanded_sentence_type_ids_to_uint16_dtype_0, x = expanded_sentence_type_ids)[name = string("cast_10")]; + tensor var_172_cast_fp16_cast_uint16 = gather(axis = var_172_axis_0, batch_dims = var_172_batch_dims_0, indices = expanded_sentence_type_ids_to_uint16, validate_indices = var_172_validate_indices_0, x = module_model_sentence_type_embed_weight_to_fp16)[name = string("op_172_cast_fp16_cast_uint16")]; + tensor hidden_13_cast_fp16 = add(x = hidden_11_cast_fp16, y = var_172_cast_fp16_cast_uint16)[name = string("hidden_13_cast_fp16")]; + string expanded_melody_to_fp16_dtype_0 = const()[name = string("expanded_melody_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_melody_proj_weight_to_fp16 = const()[name = string("module_model_melody_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(216128)))]; + tensor expanded_melody_to_fp16 = cast(dtype = expanded_melody_to_fp16_dtype_0, x = expanded_melody)[name = string("cast_9")]; + tensor linear_2_cast_fp16 = linear(bias = module_model_local_timing_proj_bias_to_fp16, weight = module_model_melody_proj_weight_to_fp16, x = expanded_melody_to_fp16)[name = string("linear_2_cast_fp16")]; + tensor hidden_15_cast_fp16 = add(x = hidden_13_cast_fp16, y = linear_2_cast_fp16)[name = string("hidden_15_cast_fp16")]; + tensor op_181_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59857344))), scale = tensor([[[0x1.1fp-5]]]))[name = string("op_181_to_fp16_quantized")]; + tensor hidden_19_cast_fp16 = add(x = hidden_15_cast_fp16, y = op_181_to_fp16_quantized)[name = string("hidden_19_cast_fp16")]; + int32 voice_hidden_axis_0 = const()[name = string("voice_hidden_axis_0"), val = int32(0)]; + int32 voice_hidden_batch_dims_0 = const()[name = string("voice_hidden_batch_dims_0"), val = int32(0)]; + bool voice_hidden_validate_indices_0 = const()[name = string("voice_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(284864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292096))))[name = string("module_model_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_uint16_dtype_0 = const()[name = string("voice_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor voice_id_to_uint16 = cast(dtype = voice_id_to_uint16_dtype_0, x = voice_id)[name = string("cast_8")]; + tensor voice_hidden_cast_fp16_cast_uint16 = gather(axis = voice_hidden_axis_0, batch_dims = voice_hidden_batch_dims_0, indices = voice_id_to_uint16, validate_indices = voice_hidden_validate_indices_0, x = module_model_voice_embed_weight_to_fp16_quantized)[name = string("voice_hidden_cast_fp16_cast_uint16")]; + int32 language_hidden_axis_0 = const()[name = string("language_hidden_axis_0"), val = int32(0)]; + int32 language_hidden_batch_dims_0 = const()[name = string("language_hidden_batch_dims_0"), val = int32(0)]; + bool language_hidden_validate_indices_0 = const()[name = string("language_hidden_validate_indices_0"), val = bool(false)]; + tensor module_model_language_embed_weight_to_fp16 = const()[name = string("module_model_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(292224)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_7")]; + tensor language_hidden_cast_fp16_cast_uint16 = gather(axis = language_hidden_axis_0, batch_dims = language_hidden_batch_dims_0, indices = language_id_to_uint16, validate_indices = language_hidden_validate_indices_0, x = module_model_language_embed_weight_to_fp16)[name = string("language_hidden_cast_fp16_cast_uint16")]; + string delivery_values_to_fp16_dtype_0 = const()[name = string("delivery_values_to_fp16_dtype_0"), val = string("fp16")]; + string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")]; + tensor delivery_values_to_fp16 = cast(dtype = delivery_values_to_fp16_dtype_0, x = delivery_values)[name = string("cast_5")]; + tensor present_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = present)[name = string("cast_6")]; + tensor var_193_cast_fp16 = mul(x = delivery_values_to_fp16, y = present_to_fp16)[name = string("op_193_cast_fp16")]; + bool input_17_interleave_0 = const()[name = string("input_17_interleave_0"), val = bool(false)]; + tensor input_17_cast_fp16 = concat(axis = var_59, interleave = input_17_interleave_0, values = (var_193_cast_fp16, present_to_fp16))[name = string("input_17_cast_fp16")]; + tensor module_model_delivery_conditioner_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(299456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304640))))[name = string("module_model_delivery_conditioner_proj_weight_to_fp16_quantized")]; + tensor linear_3_bias_0_to_fp16 = const()[name = string("linear_3_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(305728)))]; + tensor linear_3_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_delivery_conditioner_proj_weight_to_fp16_quantized, x = input_17_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor([-1])]; + string time_to_fp16_dtype_0 = const()[name = string("time_to_fp16_dtype_0"), val = string("fp16")]; + tensor time_to_fp16 = cast(dtype = time_to_fp16_dtype_0, x = time)[name = string("cast_4")]; + tensor var_211_cast_fp16 = expand_dims(axes = var_211_axes_0, x = time_to_fp16)[name = string("op_211_cast_fp16")]; + tensor var_212_to_fp16 = const()[name = string("op_212_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(306816)))]; + tensor phases_1_cast_fp16 = mul(x = var_211_cast_fp16, y = var_212_to_fp16)[name = string("phases_1_cast_fp16")]; + fp16 var_214_to_fp16 = const()[name = string("op_214_to_fp16"), val = fp16(0x1.92p+2)]; + tensor phases_cast_fp16 = mul(x = phases_1_cast_fp16, y = var_214_to_fp16)[name = string("phases_cast_fp16")]; + tensor var_216_cast_fp16 = sin(x = phases_cast_fp16)[name = string("op_216_cast_fp16")]; + tensor var_217_cast_fp16 = cos(x = phases_cast_fp16)[name = string("op_217_cast_fp16")]; + bool input_23_interleave_0 = const()[name = string("input_23_interleave_0"), val = bool(false)]; + tensor input_23_cast_fp16 = concat(axis = var_59, interleave = input_23_interleave_0, values = (var_216_cast_fp16, var_217_cast_fp16))[name = string("input_23_cast_fp16")]; + tensor module_model_time_embed_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307392))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(569600))))[name = string("module_model_time_embed_1_weight_to_fp16_quantized")]; + tensor module_model_time_embed_1_bias_to_fp16 = const()[name = string("module_model_time_embed_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570688)))]; + tensor linear_4_cast_fp16 = linear(bias = module_model_time_embed_1_bias_to_fp16, weight = module_model_time_embed_1_weight_to_fp16_quantized, x = input_23_cast_fp16)[name = string("linear_4_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = linear_4_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor module_model_time_embed_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(571776))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(833984))))[name = string("module_model_time_embed_3_weight_to_fp16_quantized")]; + tensor module_model_time_embed_3_bias_to_fp16 = const()[name = string("module_model_time_embed_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(835072)))]; + tensor linear_5_cast_fp16 = linear(bias = module_model_time_embed_3_bias_to_fp16, weight = module_model_time_embed_3_weight_to_fp16_quantized, x = input_27_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_227_cast_fp16 = add(x = linear_5_cast_fp16, y = voice_hidden_cast_fp16_cast_uint16)[name = string("op_227_cast_fp16")]; + tensor var_228_cast_fp16 = add(x = var_227_cast_fp16, y = language_hidden_cast_fp16_cast_uint16)[name = string("op_228_cast_fp16")]; + tensor var_229_cast_fp16 = add(x = var_228_cast_fp16, y = linear_3_cast_fp16)[name = string("op_229_cast_fp16")]; + tensor boundary_hidden_to_fp16 = const()[name = string("boundary_hidden_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(836160)))]; + tensor cond_1_cast_fp16 = add(x = var_229_cast_fp16, y = boundary_hidden_to_fp16)[name = string("cond_1_cast_fp16")]; + fp16 var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = fp16(0x0p+0)]; + fp16 var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_0_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = value_9_cast_fp16_cast_uint16)[name = string("clip_0_cast_fp16")]; + tensor clip_1_cast_fp16 = clip(alpha = var_62_to_fp16, beta = var_63_to_fp16, x = prosody_confidence_1_cast_fp16_cast_uint16)[name = string("clip_1_cast_fp16")]; + tensor var_243_cast_fp16 = mul(x = value_1_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_243_cast_fp16")]; + tensor module_model_prosody_baseline_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(837248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(853696))))[name = string("module_model_prosody_baseline_proj_weight_to_fp16_quantized")]; + tensor module_model_prosody_baseline_proj_bias_to_fp16 = const()[name = string("module_model_prosody_baseline_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(854784)))]; + tensor linear_6_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_baseline_proj_weight_to_fp16_quantized, x = var_243_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_249_cast_fp16 = mul(x = value_5_cast_fp16_cast_uint16, y = clip_0_cast_fp16)[name = string("op_249_cast_fp16")]; + tensor var_250_axes_0 = const()[name = string("op_250_axes_0"), val = tensor([-1])]; + tensor var_250_cast_fp16 = expand_dims(axes = var_250_axes_0, x = clip_1_cast_fp16)[name = string("op_250_cast_fp16")]; + tensor var_251_cast_fp16 = mul(x = var_249_cast_fp16, y = var_250_cast_fp16)[name = string("op_251_cast_fp16")]; + tensor module_model_prosody_delta_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(872320))))[name = string("module_model_prosody_delta_proj_weight_to_fp16_quantized")]; + tensor linear_7_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_prosody_delta_proj_weight_to_fp16_quantized, x = var_251_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_257_cast_fp16 = add(x = linear_6_cast_fp16, y = linear_7_cast_fp16)[name = string("op_257_cast_fp16")]; + tensor cond_3_cast_fp16 = add(x = cond_1_cast_fp16, y = var_257_cast_fp16)[name = string("cond_3_cast_fp16")]; + tensor mask_7_cast_fp16 = mul(x = fill_like_1_cast_fp16, y = fill_like_1_cast_fp16)[name = string("mask_7_cast_fp16")]; + tensor var_267_axes_0 = const()[name = string("op_267_axes_0"), val = tensor([-1])]; + tensor var_267_cast_fp16 = expand_dims(axes = var_267_axes_0, x = mask_7_cast_fp16)[name = string("op_267_cast_fp16")]; + tensor input_33_cast_fp16 = mul(x = value_13_cast_fp16_cast_uint16_cast_uint16, y = var_267_cast_fp16)[name = string("input_33_cast_fp16")]; + tensor module_model_identity_reference_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(873408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1135616))))[name = string("module_model_identity_reference_proj_weight_to_fp16_quantized")]; + tensor linear_8_cast_fp16 = linear(bias = module_model_prosody_baseline_proj_bias_to_fp16, weight = module_model_identity_reference_proj_weight_to_fp16_quantized, x = input_33_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor cond_cast_fp16 = add(x = cond_3_cast_fp16, y = linear_8_cast_fp16)[name = string("cond_cast_fp16")]; + string span_context_hidden_to_fp16_dtype_0 = const()[name = string("span_context_hidden_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_model_span_context_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1398912))))[name = string("module_model_span_context_proj_weight_to_fp16_quantized")]; + tensor span_context_hidden_to_fp16 = cast(dtype = span_context_hidden_to_fp16_dtype_0, x = span_context_hidden)[name = string("cast_3")]; + tensor linear_9_cast_fp16 = linear(bias = linear_3_bias_0_to_fp16, weight = module_model_span_context_proj_weight_to_fp16_quantized, x = span_context_hidden_to_fp16)[name = string("linear_9_cast_fp16")]; + tensor input_39_cast_fp16 = add(x = cond_cast_fp16, y = linear_9_cast_fp16)[name = string("input_39_cast_fp16")]; + tensor input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor([0, 2, 1])]; + string prefix_latents_to_fp16_dtype_0 = const()[name = string("prefix_latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor prefix_latents_to_fp16 = cast(dtype = prefix_latents_to_fp16_dtype_0, x = prefix_latents)[name = string("cast_2")]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = prefix_latents_to_fp16)[name = string("transpose_157")]; + tensor linear_10_cast_fp16 = linear(bias = module_model_latent_in_bias_to_fp16, weight = module_model_latent_in_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor op_284_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1400000))), scale = tensor([[[0x1.0ep-5]]]))[name = string("op_284_to_fp16_quantized")]; + tensor context_3_cast_fp16 = add(x = linear_10_cast_fp16, y = op_284_to_fp16_quantized)[name = string("context_3_cast_fp16")]; + tensor var_287_to_fp16 = const()[name = string("op_287_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1449216)))]; + tensor context_cast_fp16 = add(x = context_3_cast_fp16, y = var_287_to_fp16)[name = string("context_cast_fp16")]; + bool x_1_interleave_0 = const()[name = string("x_1_interleave_0"), val = bool(false)]; + tensor x_1_cast_fp16 = concat(axis = var_74, interleave = x_1_interleave_0, values = (context_cast_fp16, hidden_19_cast_fp16))[name = string("x_1_cast_fp16")]; + bool valid_mask_interleave_0 = const()[name = string("valid_mask_interleave_0"), val = bool(false)]; + tensor valid_mask = concat(axis = var_74, interleave = valid_mask_interleave_0, values = (prefix_mask_1, latent_mask_1))[name = string("valid_mask")]; + fp16 var_295_to_fp16 = const()[name = string("op_295_to_fp16"), val = fp16(0x1p-3)]; + tensor input_43_cast_fp16 = mul(x = x_1_cast_fp16, y = var_295_to_fp16)[name = string("input_43_cast_fp16")]; + tensor input_41_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1450304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3023232))))[name = string("module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3029440)))]; + tensor linear_11_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_329_split_sizes_0 = const()[name = string("op_329_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_329_axis_0 = const()[name = string("op_329_axis_0"), val = int32(-1)]; + tensor var_329_cast_fp16_0, tensor var_329_cast_fp16_1, tensor var_329_cast_fp16_2, tensor var_329_cast_fp16_3, tensor var_329_cast_fp16_4, tensor var_329_cast_fp16_5 = split(axis = var_329_axis_0, split_sizes = var_329_split_sizes_0, x = linear_11_cast_fp16)[name = string("op_329_cast_fp16")]; + tensor var_335_axes_0 = const()[name = string("op_335_axes_0"), val = tensor([1])]; + tensor var_335_cast_fp16 = expand_dims(axes = var_335_axes_0, x = var_329_cast_fp16_2)[name = string("op_335_cast_fp16")]; + tensor x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor([-1])]; + fp16 var_70_to_fp16 = const()[name = string("op_70_to_fp16"), val = fp16(0x1.8p-23)]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, epsilon = var_70_to_fp16, x = input_43_cast_fp16)[name = string("x_3_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = var_329_cast_fp16_1)[name = string("op_338_cast_fp16")]; + fp16 var_339_to_fp16 = const()[name = string("op_339_to_fp16"), val = fp16(0x1p+0)]; + tensor var_340_cast_fp16 = add(x = var_338_cast_fp16, y = var_339_to_fp16)[name = string("op_340_cast_fp16")]; + tensor var_341_cast_fp16 = mul(x = x_3_cast_fp16, y = var_340_cast_fp16)[name = string("op_341_cast_fp16")]; + tensor var_342_axes_0 = const()[name = string("op_342_axes_0"), val = tensor([1])]; + tensor var_342_cast_fp16 = expand_dims(axes = var_342_axes_0, x = var_329_cast_fp16_0)[name = string("op_342_cast_fp16")]; + tensor x_5_cast_fp16 = add(x = var_341_cast_fp16, y = var_342_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3035648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3822144))))[name = string("module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3825280)))]; + tensor linear_12_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_qkv_weight_to_fp16_quantized, x = x_5_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_354 = const()[name = string("op_354"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_1_cast_fp16 = reshape(shape = var_354, x = linear_12_cast_fp16)[name = string("qkv_1_cast_fp16")]; + tensor var_356_split_sizes_0 = const()[name = string("op_356_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_356_axis_0 = const()[name = string("op_356_axis_0"), val = int32(2)]; + tensor var_356_cast_fp16_0, tensor var_356_cast_fp16_1, tensor var_356_cast_fp16_2 = split(axis = var_356_axis_0, split_sizes = var_356_split_sizes_0, x = qkv_1_cast_fp16)[name = string("op_356_cast_fp16")]; + tensor squeeze_0_axes_0 = const()[name = string("squeeze_0_axes_0"), val = tensor([2])]; + tensor squeeze_0_cast_fp16 = squeeze(axes = squeeze_0_axes_0, x = var_356_cast_fp16_0)[name = string("squeeze_0_cast_fp16")]; + tensor squeeze_1_axes_0 = const()[name = string("squeeze_1_axes_0"), val = tensor([2])]; + tensor squeeze_1_cast_fp16 = squeeze(axes = squeeze_1_axes_0, x = var_356_cast_fp16_1)[name = string("squeeze_1_cast_fp16")]; + tensor squeeze_2_axes_0 = const()[name = string("squeeze_2_axes_0"), val = tensor([2])]; + tensor squeeze_2_cast_fp16 = squeeze(axes = squeeze_2_axes_0, x = var_356_cast_fp16_2)[name = string("squeeze_2_cast_fp16")]; + tensor var_361_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = squeeze_0_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor var_363_axes_0 = const()[name = string("op_363_axes_0"), val = tensor([-1])]; + bool var_363_keep_dims_0 = const()[name = string("op_363_keep_dims_0"), val = bool(true)]; + tensor var_363_cast_fp16 = reduce_mean(axes = var_363_axes_0, keep_dims = var_363_keep_dims_0, x = var_361_cast_fp16)[name = string("op_363_cast_fp16")]; + fp16 var_364_to_fp16 = const()[name = string("op_364_to_fp16"), val = fp16(0x1p-23)]; + tensor var_365_cast_fp16 = add(x = var_363_cast_fp16, y = var_364_to_fp16)[name = string("op_365_cast_fp16")]; + fp32 var_366_epsilon_0 = const()[name = string("op_366_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_366_cast_fp16 = rsqrt(epsilon = var_366_epsilon_0, x = var_365_cast_fp16)[name = string("op_366_cast_fp16")]; + tensor var_367_cast_fp16 = mul(x = squeeze_0_cast_fp16, y = var_366_cast_fp16)[name = string("op_367_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828416)))]; + tensor var_368_cast_fp16 = mul(x = var_367_cast_fp16, y = module_model_encoder_module_blocks_0_attn_q_norm_weight_to_fp16)[name = string("op_368_cast_fp16")]; + tensor var_371_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = squeeze_1_cast_fp16)[name = string("op_371_cast_fp16")]; + tensor var_373_axes_0 = const()[name = string("op_373_axes_0"), val = tensor([-1])]; + bool var_373_keep_dims_0 = const()[name = string("op_373_keep_dims_0"), val = bool(true)]; + tensor var_373_cast_fp16 = reduce_mean(axes = var_373_axes_0, keep_dims = var_373_keep_dims_0, x = var_371_cast_fp16)[name = string("op_373_cast_fp16")]; + fp16 var_374_to_fp16 = const()[name = string("op_374_to_fp16"), val = fp16(0x1p-23)]; + tensor var_375_cast_fp16 = add(x = var_373_cast_fp16, y = var_374_to_fp16)[name = string("op_375_cast_fp16")]; + fp32 var_376_epsilon_0 = const()[name = string("op_376_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_376_cast_fp16 = rsqrt(epsilon = var_376_epsilon_0, x = var_375_cast_fp16)[name = string("op_376_cast_fp16")]; + tensor var_377_cast_fp16 = mul(x = squeeze_1_cast_fp16, y = var_376_cast_fp16)[name = string("op_377_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3828608)))]; + tensor var_378_cast_fp16 = mul(x = var_377_cast_fp16, y = module_model_encoder_module_blocks_0_attn_k_norm_weight_to_fp16)[name = string("op_378_cast_fp16")]; + tensor var_383 = const()[name = string("op_383"), val = tensor([1, 1, 1, 864])]; + tensor var_384 = reshape(shape = var_383, x = valid_mask)[name = string("op_384")]; + tensor var_386 = logical_not(x = var_384)[name = string("op_386")]; + fp16 var_69_to_fp16 = const()[name = string("op_69_to_fp16"), val = fp16(-inf)]; + tensor var_382_to_fp16 = const()[name = string("op_382_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60250624)))]; + tensor attn_mask_1_cast_fp16 = select(a = var_69_to_fp16, b = var_382_to_fp16, cond = var_386)[name = string("attn_mask_1_cast_fp16")]; + tensor transpose_72_perm_0 = const()[name = string("transpose_72_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_73_perm_0 = const()[name = string("transpose_73_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74_perm_0 = const()[name = string("transpose_74_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_74 = transpose(perm = transpose_74_perm_0, x = squeeze_2_cast_fp16)[name = string("transpose_154")]; + tensor transpose_73 = transpose(perm = transpose_73_perm_0, x = var_378_cast_fp16)[name = string("transpose_155")]; + tensor transpose_72 = transpose(perm = transpose_72_perm_0, x = var_368_cast_fp16)[name = string("transpose_156")]; + tensor out_1_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_73, query = transpose_72, value = transpose_74)[name = string("out_1_cast_fp16")]; + tensor var_389_perm_0 = const()[name = string("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_390 = const()[name = string("op_390"), val = tensor([1, 864, 512])]; + tensor var_389_cast_fp16 = transpose(perm = var_389_perm_0, x = out_1_cast_fp16)[name = string("transpose_153")]; + tensor input_45_cast_fp16 = reshape(shape = var_390, x = var_389_cast_fp16)[name = string("input_45_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3829312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4091520))))[name = string("module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4092608)))]; + tensor linear_13_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_0_attn_proj_weight_to_fp16_quantized, x = input_45_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_396_cast_fp16 = mul(x = var_335_cast_fp16, y = linear_13_cast_fp16)[name = string("op_396_cast_fp16")]; + tensor input_49_cast_fp16 = add(x = input_43_cast_fp16, y = var_396_cast_fp16)[name = string("input_49_cast_fp16")]; + tensor var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor([1])]; + tensor var_398_cast_fp16 = expand_dims(axes = var_398_axes_0, x = var_329_cast_fp16_5)[name = string("op_398_cast_fp16")]; + tensor x_7_axes_0 = const()[name = string("x_7_axes_0"), val = tensor([-1])]; + tensor x_7_cast_fp16 = layer_norm(axes = x_7_axes_0, epsilon = var_70_to_fp16, x = input_49_cast_fp16)[name = string("x_7_cast_fp16")]; + tensor var_401_axes_0 = const()[name = string("op_401_axes_0"), val = tensor([1])]; + tensor var_401_cast_fp16 = expand_dims(axes = var_401_axes_0, x = var_329_cast_fp16_4)[name = string("op_401_cast_fp16")]; + fp16 var_402_to_fp16 = const()[name = string("op_402_to_fp16"), val = fp16(0x1p+0)]; + tensor var_403_cast_fp16 = add(x = var_401_cast_fp16, y = var_402_to_fp16)[name = string("op_403_cast_fp16")]; + tensor var_404_cast_fp16 = mul(x = x_7_cast_fp16, y = var_403_cast_fp16)[name = string("op_404_cast_fp16")]; + tensor var_405_axes_0 = const()[name = string("op_405_axes_0"), val = tensor([1])]; + tensor var_405_cast_fp16 = expand_dims(axes = var_405_axes_0, x = var_329_cast_fp16_3)[name = string("op_405_cast_fp16")]; + tensor input_51_cast_fp16 = add(x = var_404_cast_fp16, y = var_405_cast_fp16)[name = string("input_51_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4093696))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5142336))))[name = string("module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5146496)))]; + tensor linear_14_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_0_weight_to_fp16_quantized, x = input_51_cast_fp16)[name = string("linear_14_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = linear_14_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5150656))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6199296))))[name = string("module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6200384)))]; + tensor linear_15_cast_fp16 = linear(bias = module_model_encoder_module_blocks_0_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_0_ffn_3_weight_to_fp16_quantized, x = input_55_cast_fp16)[name = string("linear_15_cast_fp16")]; + tensor var_418_cast_fp16 = mul(x = var_398_cast_fp16, y = linear_15_cast_fp16)[name = string("op_418_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_49_cast_fp16, y = var_418_cast_fp16)[name = string("input_63_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6201472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7774400))))[name = string("module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7780608)))]; + tensor linear_16_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_16_cast_fp16")]; + tensor var_427_split_sizes_0 = const()[name = string("op_427_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_427_axis_0 = const()[name = string("op_427_axis_0"), val = int32(-1)]; + tensor var_427_cast_fp16_0, tensor var_427_cast_fp16_1, tensor var_427_cast_fp16_2, tensor var_427_cast_fp16_3, tensor var_427_cast_fp16_4, tensor var_427_cast_fp16_5 = split(axis = var_427_axis_0, split_sizes = var_427_split_sizes_0, x = linear_16_cast_fp16)[name = string("op_427_cast_fp16")]; + tensor var_433_axes_0 = const()[name = string("op_433_axes_0"), val = tensor([1])]; + tensor var_433_cast_fp16 = expand_dims(axes = var_433_axes_0, x = var_427_cast_fp16_2)[name = string("op_433_cast_fp16")]; + tensor x_9_axes_0 = const()[name = string("x_9_axes_0"), val = tensor([-1])]; + tensor x_9_cast_fp16 = layer_norm(axes = x_9_axes_0, epsilon = var_70_to_fp16, x = input_63_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor var_436_axes_0 = const()[name = string("op_436_axes_0"), val = tensor([1])]; + tensor var_436_cast_fp16 = expand_dims(axes = var_436_axes_0, x = var_427_cast_fp16_1)[name = string("op_436_cast_fp16")]; + fp16 var_437_to_fp16 = const()[name = string("op_437_to_fp16"), val = fp16(0x1p+0)]; + tensor var_438_cast_fp16 = add(x = var_436_cast_fp16, y = var_437_to_fp16)[name = string("op_438_cast_fp16")]; + tensor var_439_cast_fp16 = mul(x = x_9_cast_fp16, y = var_438_cast_fp16)[name = string("op_439_cast_fp16")]; + tensor var_440_axes_0 = const()[name = string("op_440_axes_0"), val = tensor([1])]; + tensor var_440_cast_fp16 = expand_dims(axes = var_440_axes_0, x = var_427_cast_fp16_0)[name = string("op_440_cast_fp16")]; + tensor x_11_cast_fp16 = add(x = var_439_cast_fp16, y = var_440_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7786816))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8573312))))[name = string("module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8576448)))]; + tensor linear_17_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_qkv_weight_to_fp16_quantized, x = x_11_cast_fp16)[name = string("linear_17_cast_fp16")]; + tensor var_452 = const()[name = string("op_452"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_3_cast_fp16 = reshape(shape = var_452, x = linear_17_cast_fp16)[name = string("qkv_3_cast_fp16")]; + tensor var_454_split_sizes_0 = const()[name = string("op_454_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_454_axis_0 = const()[name = string("op_454_axis_0"), val = int32(2)]; + tensor var_454_cast_fp16_0, tensor var_454_cast_fp16_1, tensor var_454_cast_fp16_2 = split(axis = var_454_axis_0, split_sizes = var_454_split_sizes_0, x = qkv_3_cast_fp16)[name = string("op_454_cast_fp16")]; + tensor squeeze_3_axes_0 = const()[name = string("squeeze_3_axes_0"), val = tensor([2])]; + tensor squeeze_3_cast_fp16 = squeeze(axes = squeeze_3_axes_0, x = var_454_cast_fp16_0)[name = string("squeeze_3_cast_fp16")]; + tensor squeeze_4_axes_0 = const()[name = string("squeeze_4_axes_0"), val = tensor([2])]; + tensor squeeze_4_cast_fp16 = squeeze(axes = squeeze_4_axes_0, x = var_454_cast_fp16_1)[name = string("squeeze_4_cast_fp16")]; + tensor squeeze_5_axes_0 = const()[name = string("squeeze_5_axes_0"), val = tensor([2])]; + tensor squeeze_5_cast_fp16 = squeeze(axes = squeeze_5_axes_0, x = var_454_cast_fp16_2)[name = string("squeeze_5_cast_fp16")]; + tensor var_459_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = squeeze_3_cast_fp16)[name = string("op_459_cast_fp16")]; + tensor var_461_axes_0 = const()[name = string("op_461_axes_0"), val = tensor([-1])]; + bool var_461_keep_dims_0 = const()[name = string("op_461_keep_dims_0"), val = bool(true)]; + tensor var_461_cast_fp16 = reduce_mean(axes = var_461_axes_0, keep_dims = var_461_keep_dims_0, x = var_459_cast_fp16)[name = string("op_461_cast_fp16")]; + fp16 var_462_to_fp16 = const()[name = string("op_462_to_fp16"), val = fp16(0x1p-23)]; + tensor var_463_cast_fp16 = add(x = var_461_cast_fp16, y = var_462_to_fp16)[name = string("op_463_cast_fp16")]; + fp32 var_464_epsilon_0 = const()[name = string("op_464_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_464_cast_fp16 = rsqrt(epsilon = var_464_epsilon_0, x = var_463_cast_fp16)[name = string("op_464_cast_fp16")]; + tensor var_465_cast_fp16 = mul(x = squeeze_3_cast_fp16, y = var_464_cast_fp16)[name = string("op_465_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579584)))]; + tensor var_466_cast_fp16 = mul(x = var_465_cast_fp16, y = module_model_encoder_module_blocks_1_attn_q_norm_weight_to_fp16)[name = string("op_466_cast_fp16")]; + tensor var_469_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = squeeze_4_cast_fp16)[name = string("op_469_cast_fp16")]; + tensor var_471_axes_0 = const()[name = string("op_471_axes_0"), val = tensor([-1])]; + bool var_471_keep_dims_0 = const()[name = string("op_471_keep_dims_0"), val = bool(true)]; + tensor var_471_cast_fp16 = reduce_mean(axes = var_471_axes_0, keep_dims = var_471_keep_dims_0, x = var_469_cast_fp16)[name = string("op_471_cast_fp16")]; + fp16 var_472_to_fp16 = const()[name = string("op_472_to_fp16"), val = fp16(0x1p-23)]; + tensor var_473_cast_fp16 = add(x = var_471_cast_fp16, y = var_472_to_fp16)[name = string("op_473_cast_fp16")]; + fp32 var_474_epsilon_0 = const()[name = string("op_474_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_474_cast_fp16 = rsqrt(epsilon = var_474_epsilon_0, x = var_473_cast_fp16)[name = string("op_474_cast_fp16")]; + tensor var_475_cast_fp16 = mul(x = squeeze_4_cast_fp16, y = var_474_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579776)))]; + tensor var_476_cast_fp16 = mul(x = var_475_cast_fp16, y = module_model_encoder_module_blocks_1_attn_k_norm_weight_to_fp16)[name = string("op_476_cast_fp16")]; + tensor transpose_75_perm_0 = const()[name = string("transpose_75_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_76_perm_0 = const()[name = string("transpose_76_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77_perm_0 = const()[name = string("transpose_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_77 = transpose(perm = transpose_77_perm_0, x = squeeze_5_cast_fp16)[name = string("transpose_150")]; + tensor transpose_76 = transpose(perm = transpose_76_perm_0, x = var_476_cast_fp16)[name = string("transpose_151")]; + tensor transpose_75 = transpose(perm = transpose_75_perm_0, x = var_466_cast_fp16)[name = string("transpose_152")]; + tensor out_3_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_76, query = transpose_75, value = transpose_77)[name = string("out_3_cast_fp16")]; + tensor var_487_perm_0 = const()[name = string("op_487_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_488 = const()[name = string("op_488"), val = tensor([1, 864, 512])]; + tensor var_487_cast_fp16 = transpose(perm = var_487_perm_0, x = out_3_cast_fp16)[name = string("transpose_149")]; + tensor input_65_cast_fp16 = reshape(shape = var_488, x = var_487_cast_fp16)[name = string("input_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8579968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8842176))))[name = string("module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8843264)))]; + tensor linear_18_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_1_attn_proj_weight_to_fp16_quantized, x = input_65_cast_fp16)[name = string("linear_18_cast_fp16")]; + tensor var_494_cast_fp16 = mul(x = var_433_cast_fp16, y = linear_18_cast_fp16)[name = string("op_494_cast_fp16")]; + tensor input_69_cast_fp16 = add(x = input_63_cast_fp16, y = var_494_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor var_496_axes_0 = const()[name = string("op_496_axes_0"), val = tensor([1])]; + tensor var_496_cast_fp16 = expand_dims(axes = var_496_axes_0, x = var_427_cast_fp16_5)[name = string("op_496_cast_fp16")]; + tensor x_13_axes_0 = const()[name = string("x_13_axes_0"), val = tensor([-1])]; + tensor x_13_cast_fp16 = layer_norm(axes = x_13_axes_0, epsilon = var_70_to_fp16, x = input_69_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = var_427_cast_fp16_4)[name = string("op_499_cast_fp16")]; + fp16 var_500_to_fp16 = const()[name = string("op_500_to_fp16"), val = fp16(0x1p+0)]; + tensor var_501_cast_fp16 = add(x = var_499_cast_fp16, y = var_500_to_fp16)[name = string("op_501_cast_fp16")]; + tensor var_502_cast_fp16 = mul(x = x_13_cast_fp16, y = var_501_cast_fp16)[name = string("op_502_cast_fp16")]; + tensor var_503_axes_0 = const()[name = string("op_503_axes_0"), val = tensor([1])]; + tensor var_503_cast_fp16 = expand_dims(axes = var_503_axes_0, x = var_427_cast_fp16_3)[name = string("op_503_cast_fp16")]; + tensor input_71_cast_fp16 = add(x = var_502_cast_fp16, y = var_503_cast_fp16)[name = string("input_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8844352))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9892992))))[name = string("module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9897152)))]; + tensor linear_19_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_0_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("linear_19_cast_fp16")]; + string input_75_mode_0 = const()[name = string("input_75_mode_0"), val = string("EXACT")]; + tensor input_75_cast_fp16 = gelu(mode = input_75_mode_0, x = linear_19_cast_fp16)[name = string("input_75_cast_fp16")]; + tensor module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9901312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10949952))))[name = string("module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10951040)))]; + tensor linear_20_cast_fp16 = linear(bias = module_model_encoder_module_blocks_1_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_1_ffn_3_weight_to_fp16_quantized, x = input_75_cast_fp16)[name = string("linear_20_cast_fp16")]; + tensor var_516_cast_fp16 = mul(x = var_496_cast_fp16, y = linear_20_cast_fp16)[name = string("op_516_cast_fp16")]; + tensor input_83_cast_fp16 = add(x = input_69_cast_fp16, y = var_516_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12525056))))[name = string("module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12531264)))]; + tensor linear_21_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_21_cast_fp16")]; + tensor var_525_split_sizes_0 = const()[name = string("op_525_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_525_axis_0 = const()[name = string("op_525_axis_0"), val = int32(-1)]; + tensor var_525_cast_fp16_0, tensor var_525_cast_fp16_1, tensor var_525_cast_fp16_2, tensor var_525_cast_fp16_3, tensor var_525_cast_fp16_4, tensor var_525_cast_fp16_5 = split(axis = var_525_axis_0, split_sizes = var_525_split_sizes_0, x = linear_21_cast_fp16)[name = string("op_525_cast_fp16")]; + tensor var_531_axes_0 = const()[name = string("op_531_axes_0"), val = tensor([1])]; + tensor var_531_cast_fp16 = expand_dims(axes = var_531_axes_0, x = var_525_cast_fp16_2)[name = string("op_531_cast_fp16")]; + tensor x_15_axes_0 = const()[name = string("x_15_axes_0"), val = tensor([-1])]; + tensor x_15_cast_fp16 = layer_norm(axes = x_15_axes_0, epsilon = var_70_to_fp16, x = input_83_cast_fp16)[name = string("x_15_cast_fp16")]; + tensor var_534_axes_0 = const()[name = string("op_534_axes_0"), val = tensor([1])]; + tensor var_534_cast_fp16 = expand_dims(axes = var_534_axes_0, x = var_525_cast_fp16_1)[name = string("op_534_cast_fp16")]; + fp16 var_535_to_fp16 = const()[name = string("op_535_to_fp16"), val = fp16(0x1p+0)]; + tensor var_536_cast_fp16 = add(x = var_534_cast_fp16, y = var_535_to_fp16)[name = string("op_536_cast_fp16")]; + tensor var_537_cast_fp16 = mul(x = x_15_cast_fp16, y = var_536_cast_fp16)[name = string("op_537_cast_fp16")]; + tensor var_538_axes_0 = const()[name = string("op_538_axes_0"), val = tensor([1])]; + tensor var_538_cast_fp16 = expand_dims(axes = var_538_axes_0, x = var_525_cast_fp16_0)[name = string("op_538_cast_fp16")]; + tensor x_17_cast_fp16 = add(x = var_537_cast_fp16, y = var_538_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12537472))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13323968))))[name = string("module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13327104)))]; + tensor linear_22_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_qkv_weight_to_fp16_quantized, x = x_17_cast_fp16)[name = string("linear_22_cast_fp16")]; + tensor var_550 = const()[name = string("op_550"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_5_cast_fp16 = reshape(shape = var_550, x = linear_22_cast_fp16)[name = string("qkv_5_cast_fp16")]; + tensor var_552_split_sizes_0 = const()[name = string("op_552_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_552_axis_0 = const()[name = string("op_552_axis_0"), val = int32(2)]; + tensor var_552_cast_fp16_0, tensor var_552_cast_fp16_1, tensor var_552_cast_fp16_2 = split(axis = var_552_axis_0, split_sizes = var_552_split_sizes_0, x = qkv_5_cast_fp16)[name = string("op_552_cast_fp16")]; + tensor squeeze_6_axes_0 = const()[name = string("squeeze_6_axes_0"), val = tensor([2])]; + tensor squeeze_6_cast_fp16 = squeeze(axes = squeeze_6_axes_0, x = var_552_cast_fp16_0)[name = string("squeeze_6_cast_fp16")]; + tensor squeeze_7_axes_0 = const()[name = string("squeeze_7_axes_0"), val = tensor([2])]; + tensor squeeze_7_cast_fp16 = squeeze(axes = squeeze_7_axes_0, x = var_552_cast_fp16_1)[name = string("squeeze_7_cast_fp16")]; + tensor squeeze_8_axes_0 = const()[name = string("squeeze_8_axes_0"), val = tensor([2])]; + tensor squeeze_8_cast_fp16 = squeeze(axes = squeeze_8_axes_0, x = var_552_cast_fp16_2)[name = string("squeeze_8_cast_fp16")]; + tensor var_557_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = squeeze_6_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor var_559_axes_0 = const()[name = string("op_559_axes_0"), val = tensor([-1])]; + bool var_559_keep_dims_0 = const()[name = string("op_559_keep_dims_0"), val = bool(true)]; + tensor var_559_cast_fp16 = reduce_mean(axes = var_559_axes_0, keep_dims = var_559_keep_dims_0, x = var_557_cast_fp16)[name = string("op_559_cast_fp16")]; + fp16 var_560_to_fp16 = const()[name = string("op_560_to_fp16"), val = fp16(0x1p-23)]; + tensor var_561_cast_fp16 = add(x = var_559_cast_fp16, y = var_560_to_fp16)[name = string("op_561_cast_fp16")]; + fp32 var_562_epsilon_0 = const()[name = string("op_562_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_562_cast_fp16 = rsqrt(epsilon = var_562_epsilon_0, x = var_561_cast_fp16)[name = string("op_562_cast_fp16")]; + tensor var_563_cast_fp16 = mul(x = squeeze_6_cast_fp16, y = var_562_cast_fp16)[name = string("op_563_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330240)))]; + tensor var_564_cast_fp16 = mul(x = var_563_cast_fp16, y = module_model_encoder_module_blocks_2_attn_q_norm_weight_to_fp16)[name = string("op_564_cast_fp16")]; + tensor var_567_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = squeeze_7_cast_fp16)[name = string("op_567_cast_fp16")]; + tensor var_569_axes_0 = const()[name = string("op_569_axes_0"), val = tensor([-1])]; + bool var_569_keep_dims_0 = const()[name = string("op_569_keep_dims_0"), val = bool(true)]; + tensor var_569_cast_fp16 = reduce_mean(axes = var_569_axes_0, keep_dims = var_569_keep_dims_0, x = var_567_cast_fp16)[name = string("op_569_cast_fp16")]; + fp16 var_570_to_fp16 = const()[name = string("op_570_to_fp16"), val = fp16(0x1p-23)]; + tensor var_571_cast_fp16 = add(x = var_569_cast_fp16, y = var_570_to_fp16)[name = string("op_571_cast_fp16")]; + fp32 var_572_epsilon_0 = const()[name = string("op_572_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_572_cast_fp16 = rsqrt(epsilon = var_572_epsilon_0, x = var_571_cast_fp16)[name = string("op_572_cast_fp16")]; + tensor var_573_cast_fp16 = mul(x = squeeze_7_cast_fp16, y = var_572_cast_fp16)[name = string("op_573_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330432)))]; + tensor var_574_cast_fp16 = mul(x = var_573_cast_fp16, y = module_model_encoder_module_blocks_2_attn_k_norm_weight_to_fp16)[name = string("op_574_cast_fp16")]; + tensor transpose_78_perm_0 = const()[name = string("transpose_78_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_79_perm_0 = const()[name = string("transpose_79_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80_perm_0 = const()[name = string("transpose_80_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_80 = transpose(perm = transpose_80_perm_0, x = squeeze_8_cast_fp16)[name = string("transpose_146")]; + tensor transpose_79 = transpose(perm = transpose_79_perm_0, x = var_574_cast_fp16)[name = string("transpose_147")]; + tensor transpose_78 = transpose(perm = transpose_78_perm_0, x = var_564_cast_fp16)[name = string("transpose_148")]; + tensor out_5_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_79, query = transpose_78, value = transpose_80)[name = string("out_5_cast_fp16")]; + tensor var_585_perm_0 = const()[name = string("op_585_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_586 = const()[name = string("op_586"), val = tensor([1, 864, 512])]; + tensor var_585_cast_fp16 = transpose(perm = var_585_perm_0, x = out_5_cast_fp16)[name = string("transpose_145")]; + tensor input_85_cast_fp16 = reshape(shape = var_586, x = var_585_cast_fp16)[name = string("input_85_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13330624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13592832))))[name = string("module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13593920)))]; + tensor linear_23_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_2_attn_proj_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("linear_23_cast_fp16")]; + tensor var_592_cast_fp16 = mul(x = var_531_cast_fp16, y = linear_23_cast_fp16)[name = string("op_592_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_83_cast_fp16, y = var_592_cast_fp16)[name = string("input_89_cast_fp16")]; + tensor var_594_axes_0 = const()[name = string("op_594_axes_0"), val = tensor([1])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = var_525_cast_fp16_5)[name = string("op_594_cast_fp16")]; + tensor x_19_axes_0 = const()[name = string("x_19_axes_0"), val = tensor([-1])]; + tensor x_19_cast_fp16 = layer_norm(axes = x_19_axes_0, epsilon = var_70_to_fp16, x = input_89_cast_fp16)[name = string("x_19_cast_fp16")]; + tensor var_597_axes_0 = const()[name = string("op_597_axes_0"), val = tensor([1])]; + tensor var_597_cast_fp16 = expand_dims(axes = var_597_axes_0, x = var_525_cast_fp16_4)[name = string("op_597_cast_fp16")]; + fp16 var_598_to_fp16 = const()[name = string("op_598_to_fp16"), val = fp16(0x1p+0)]; + tensor var_599_cast_fp16 = add(x = var_597_cast_fp16, y = var_598_to_fp16)[name = string("op_599_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = x_19_cast_fp16, y = var_599_cast_fp16)[name = string("op_600_cast_fp16")]; + tensor var_601_axes_0 = const()[name = string("op_601_axes_0"), val = tensor([1])]; + tensor var_601_cast_fp16 = expand_dims(axes = var_601_axes_0, x = var_525_cast_fp16_3)[name = string("op_601_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = var_600_cast_fp16, y = var_601_cast_fp16)[name = string("input_91_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13595008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14643648))))[name = string("module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14647808)))]; + tensor linear_24_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_0_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("linear_24_cast_fp16")]; + string input_95_mode_0 = const()[name = string("input_95_mode_0"), val = string("EXACT")]; + tensor input_95_cast_fp16 = gelu(mode = input_95_mode_0, x = linear_24_cast_fp16)[name = string("input_95_cast_fp16")]; + tensor module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14651968))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15700608))))[name = string("module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15701696)))]; + tensor linear_25_cast_fp16 = linear(bias = module_model_encoder_module_blocks_2_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_2_ffn_3_weight_to_fp16_quantized, x = input_95_cast_fp16)[name = string("linear_25_cast_fp16")]; + tensor var_614_cast_fp16 = mul(x = var_594_cast_fp16, y = linear_25_cast_fp16)[name = string("op_614_cast_fp16")]; + tensor input_103_cast_fp16 = add(x = input_89_cast_fp16, y = var_614_cast_fp16)[name = string("input_103_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15702784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17275712))))[name = string("module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17281920)))]; + tensor linear_26_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_26_cast_fp16")]; + tensor var_623_split_sizes_0 = const()[name = string("op_623_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_623_axis_0 = const()[name = string("op_623_axis_0"), val = int32(-1)]; + tensor var_623_cast_fp16_0, tensor var_623_cast_fp16_1, tensor var_623_cast_fp16_2, tensor var_623_cast_fp16_3, tensor var_623_cast_fp16_4, tensor var_623_cast_fp16_5 = split(axis = var_623_axis_0, split_sizes = var_623_split_sizes_0, x = linear_26_cast_fp16)[name = string("op_623_cast_fp16")]; + tensor var_629_axes_0 = const()[name = string("op_629_axes_0"), val = tensor([1])]; + tensor var_629_cast_fp16 = expand_dims(axes = var_629_axes_0, x = var_623_cast_fp16_2)[name = string("op_629_cast_fp16")]; + tensor x_21_axes_0 = const()[name = string("x_21_axes_0"), val = tensor([-1])]; + tensor x_21_cast_fp16 = layer_norm(axes = x_21_axes_0, epsilon = var_70_to_fp16, x = input_103_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor var_632_axes_0 = const()[name = string("op_632_axes_0"), val = tensor([1])]; + tensor var_632_cast_fp16 = expand_dims(axes = var_632_axes_0, x = var_623_cast_fp16_1)[name = string("op_632_cast_fp16")]; + fp16 var_633_to_fp16 = const()[name = string("op_633_to_fp16"), val = fp16(0x1p+0)]; + tensor var_634_cast_fp16 = add(x = var_632_cast_fp16, y = var_633_to_fp16)[name = string("op_634_cast_fp16")]; + tensor var_635_cast_fp16 = mul(x = x_21_cast_fp16, y = var_634_cast_fp16)[name = string("op_635_cast_fp16")]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([1])]; + tensor var_636_cast_fp16 = expand_dims(axes = var_636_axes_0, x = var_623_cast_fp16_0)[name = string("op_636_cast_fp16")]; + tensor x_23_cast_fp16 = add(x = var_635_cast_fp16, y = var_636_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17288128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18074624))))[name = string("module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18077760)))]; + tensor linear_27_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_qkv_weight_to_fp16_quantized, x = x_23_cast_fp16)[name = string("linear_27_cast_fp16")]; + tensor var_648 = const()[name = string("op_648"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_7_cast_fp16 = reshape(shape = var_648, x = linear_27_cast_fp16)[name = string("qkv_7_cast_fp16")]; + tensor var_650_split_sizes_0 = const()[name = string("op_650_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_650_axis_0 = const()[name = string("op_650_axis_0"), val = int32(2)]; + tensor var_650_cast_fp16_0, tensor var_650_cast_fp16_1, tensor var_650_cast_fp16_2 = split(axis = var_650_axis_0, split_sizes = var_650_split_sizes_0, x = qkv_7_cast_fp16)[name = string("op_650_cast_fp16")]; + tensor squeeze_9_axes_0 = const()[name = string("squeeze_9_axes_0"), val = tensor([2])]; + tensor squeeze_9_cast_fp16 = squeeze(axes = squeeze_9_axes_0, x = var_650_cast_fp16_0)[name = string("squeeze_9_cast_fp16")]; + tensor squeeze_10_axes_0 = const()[name = string("squeeze_10_axes_0"), val = tensor([2])]; + tensor squeeze_10_cast_fp16 = squeeze(axes = squeeze_10_axes_0, x = var_650_cast_fp16_1)[name = string("squeeze_10_cast_fp16")]; + tensor squeeze_11_axes_0 = const()[name = string("squeeze_11_axes_0"), val = tensor([2])]; + tensor squeeze_11_cast_fp16 = squeeze(axes = squeeze_11_axes_0, x = var_650_cast_fp16_2)[name = string("squeeze_11_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = squeeze_9_cast_fp16)[name = string("op_655_cast_fp16")]; + tensor var_657_axes_0 = const()[name = string("op_657_axes_0"), val = tensor([-1])]; + bool var_657_keep_dims_0 = const()[name = string("op_657_keep_dims_0"), val = bool(true)]; + tensor var_657_cast_fp16 = reduce_mean(axes = var_657_axes_0, keep_dims = var_657_keep_dims_0, x = var_655_cast_fp16)[name = string("op_657_cast_fp16")]; + fp16 var_658_to_fp16 = const()[name = string("op_658_to_fp16"), val = fp16(0x1p-23)]; + tensor var_659_cast_fp16 = add(x = var_657_cast_fp16, y = var_658_to_fp16)[name = string("op_659_cast_fp16")]; + fp32 var_660_epsilon_0 = const()[name = string("op_660_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_660_cast_fp16 = rsqrt(epsilon = var_660_epsilon_0, x = var_659_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = mul(x = squeeze_9_cast_fp16, y = var_660_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18080896)))]; + tensor var_662_cast_fp16 = mul(x = var_661_cast_fp16, y = module_model_encoder_module_blocks_3_attn_q_norm_weight_to_fp16)[name = string("op_662_cast_fp16")]; + tensor var_665_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = squeeze_10_cast_fp16)[name = string("op_665_cast_fp16")]; + tensor var_667_axes_0 = const()[name = string("op_667_axes_0"), val = tensor([-1])]; + bool var_667_keep_dims_0 = const()[name = string("op_667_keep_dims_0"), val = bool(true)]; + tensor var_667_cast_fp16 = reduce_mean(axes = var_667_axes_0, keep_dims = var_667_keep_dims_0, x = var_665_cast_fp16)[name = string("op_667_cast_fp16")]; + fp16 var_668_to_fp16 = const()[name = string("op_668_to_fp16"), val = fp16(0x1p-23)]; + tensor var_669_cast_fp16 = add(x = var_667_cast_fp16, y = var_668_to_fp16)[name = string("op_669_cast_fp16")]; + fp32 var_670_epsilon_0 = const()[name = string("op_670_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_670_cast_fp16 = rsqrt(epsilon = var_670_epsilon_0, x = var_669_cast_fp16)[name = string("op_670_cast_fp16")]; + tensor var_671_cast_fp16 = mul(x = squeeze_10_cast_fp16, y = var_670_cast_fp16)[name = string("op_671_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081088)))]; + tensor var_672_cast_fp16 = mul(x = var_671_cast_fp16, y = module_model_encoder_module_blocks_3_attn_k_norm_weight_to_fp16)[name = string("op_672_cast_fp16")]; + tensor transpose_81_perm_0 = const()[name = string("transpose_81_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_82_perm_0 = const()[name = string("transpose_82_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83_perm_0 = const()[name = string("transpose_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_83 = transpose(perm = transpose_83_perm_0, x = squeeze_11_cast_fp16)[name = string("transpose_142")]; + tensor transpose_82 = transpose(perm = transpose_82_perm_0, x = var_672_cast_fp16)[name = string("transpose_143")]; + tensor transpose_81 = transpose(perm = transpose_81_perm_0, x = var_662_cast_fp16)[name = string("transpose_144")]; + tensor out_7_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_82, query = transpose_81, value = transpose_83)[name = string("out_7_cast_fp16")]; + tensor var_683_perm_0 = const()[name = string("op_683_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_684 = const()[name = string("op_684"), val = tensor([1, 864, 512])]; + tensor var_683_cast_fp16 = transpose(perm = var_683_perm_0, x = out_7_cast_fp16)[name = string("transpose_141")]; + tensor input_105_cast_fp16 = reshape(shape = var_684, x = var_683_cast_fp16)[name = string("input_105_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18081280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18343488))))[name = string("module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18344576)))]; + tensor linear_28_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_3_attn_proj_weight_to_fp16_quantized, x = input_105_cast_fp16)[name = string("linear_28_cast_fp16")]; + tensor var_690_cast_fp16 = mul(x = var_629_cast_fp16, y = linear_28_cast_fp16)[name = string("op_690_cast_fp16")]; + tensor input_109_cast_fp16 = add(x = input_103_cast_fp16, y = var_690_cast_fp16)[name = string("input_109_cast_fp16")]; + tensor var_692_axes_0 = const()[name = string("op_692_axes_0"), val = tensor([1])]; + tensor var_692_cast_fp16 = expand_dims(axes = var_692_axes_0, x = var_623_cast_fp16_5)[name = string("op_692_cast_fp16")]; + tensor x_25_axes_0 = const()[name = string("x_25_axes_0"), val = tensor([-1])]; + tensor x_25_cast_fp16 = layer_norm(axes = x_25_axes_0, epsilon = var_70_to_fp16, x = input_109_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor var_695_axes_0 = const()[name = string("op_695_axes_0"), val = tensor([1])]; + tensor var_695_cast_fp16 = expand_dims(axes = var_695_axes_0, x = var_623_cast_fp16_4)[name = string("op_695_cast_fp16")]; + fp16 var_696_to_fp16 = const()[name = string("op_696_to_fp16"), val = fp16(0x1p+0)]; + tensor var_697_cast_fp16 = add(x = var_695_cast_fp16, y = var_696_to_fp16)[name = string("op_697_cast_fp16")]; + tensor var_698_cast_fp16 = mul(x = x_25_cast_fp16, y = var_697_cast_fp16)[name = string("op_698_cast_fp16")]; + tensor var_699_axes_0 = const()[name = string("op_699_axes_0"), val = tensor([1])]; + tensor var_699_cast_fp16 = expand_dims(axes = var_699_axes_0, x = var_623_cast_fp16_3)[name = string("op_699_cast_fp16")]; + tensor input_111_cast_fp16 = add(x = var_698_cast_fp16, y = var_699_cast_fp16)[name = string("input_111_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18345664))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19394304))))[name = string("module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19398464)))]; + tensor linear_29_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_0_weight_to_fp16_quantized, x = input_111_cast_fp16)[name = string("linear_29_cast_fp16")]; + string input_115_mode_0 = const()[name = string("input_115_mode_0"), val = string("EXACT")]; + tensor input_115_cast_fp16 = gelu(mode = input_115_mode_0, x = linear_29_cast_fp16)[name = string("input_115_cast_fp16")]; + tensor module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19402624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20451264))))[name = string("module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20452352)))]; + tensor linear_30_cast_fp16 = linear(bias = module_model_encoder_module_blocks_3_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_3_ffn_3_weight_to_fp16_quantized, x = input_115_cast_fp16)[name = string("linear_30_cast_fp16")]; + tensor var_712_cast_fp16 = mul(x = var_692_cast_fp16, y = linear_30_cast_fp16)[name = string("op_712_cast_fp16")]; + tensor input_123_cast_fp16 = add(x = input_109_cast_fp16, y = var_712_cast_fp16)[name = string("input_123_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20453440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22026368))))[name = string("module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22032576)))]; + tensor linear_31_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_31_cast_fp16")]; + tensor var_721_split_sizes_0 = const()[name = string("op_721_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_721_axis_0 = const()[name = string("op_721_axis_0"), val = int32(-1)]; + tensor var_721_cast_fp16_0, tensor var_721_cast_fp16_1, tensor var_721_cast_fp16_2, tensor var_721_cast_fp16_3, tensor var_721_cast_fp16_4, tensor var_721_cast_fp16_5 = split(axis = var_721_axis_0, split_sizes = var_721_split_sizes_0, x = linear_31_cast_fp16)[name = string("op_721_cast_fp16")]; + tensor var_727_axes_0 = const()[name = string("op_727_axes_0"), val = tensor([1])]; + tensor var_727_cast_fp16 = expand_dims(axes = var_727_axes_0, x = var_721_cast_fp16_2)[name = string("op_727_cast_fp16")]; + tensor x_27_axes_0 = const()[name = string("x_27_axes_0"), val = tensor([-1])]; + tensor x_27_cast_fp16 = layer_norm(axes = x_27_axes_0, epsilon = var_70_to_fp16, x = input_123_cast_fp16)[name = string("x_27_cast_fp16")]; + tensor var_730_axes_0 = const()[name = string("op_730_axes_0"), val = tensor([1])]; + tensor var_730_cast_fp16 = expand_dims(axes = var_730_axes_0, x = var_721_cast_fp16_1)[name = string("op_730_cast_fp16")]; + fp16 var_731_to_fp16 = const()[name = string("op_731_to_fp16"), val = fp16(0x1p+0)]; + tensor var_732_cast_fp16 = add(x = var_730_cast_fp16, y = var_731_to_fp16)[name = string("op_732_cast_fp16")]; + tensor var_733_cast_fp16 = mul(x = x_27_cast_fp16, y = var_732_cast_fp16)[name = string("op_733_cast_fp16")]; + tensor var_734_axes_0 = const()[name = string("op_734_axes_0"), val = tensor([1])]; + tensor var_734_cast_fp16 = expand_dims(axes = var_734_axes_0, x = var_721_cast_fp16_0)[name = string("op_734_cast_fp16")]; + tensor x_29_cast_fp16 = add(x = var_733_cast_fp16, y = var_734_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22038784))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22825280))))[name = string("module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22828416)))]; + tensor linear_32_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_qkv_weight_to_fp16_quantized, x = x_29_cast_fp16)[name = string("linear_32_cast_fp16")]; + tensor var_746 = const()[name = string("op_746"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_9_cast_fp16 = reshape(shape = var_746, x = linear_32_cast_fp16)[name = string("qkv_9_cast_fp16")]; + tensor var_748_split_sizes_0 = const()[name = string("op_748_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_748_axis_0 = const()[name = string("op_748_axis_0"), val = int32(2)]; + tensor var_748_cast_fp16_0, tensor var_748_cast_fp16_1, tensor var_748_cast_fp16_2 = split(axis = var_748_axis_0, split_sizes = var_748_split_sizes_0, x = qkv_9_cast_fp16)[name = string("op_748_cast_fp16")]; + tensor squeeze_12_axes_0 = const()[name = string("squeeze_12_axes_0"), val = tensor([2])]; + tensor squeeze_12_cast_fp16 = squeeze(axes = squeeze_12_axes_0, x = var_748_cast_fp16_0)[name = string("squeeze_12_cast_fp16")]; + tensor squeeze_13_axes_0 = const()[name = string("squeeze_13_axes_0"), val = tensor([2])]; + tensor squeeze_13_cast_fp16 = squeeze(axes = squeeze_13_axes_0, x = var_748_cast_fp16_1)[name = string("squeeze_13_cast_fp16")]; + tensor squeeze_14_axes_0 = const()[name = string("squeeze_14_axes_0"), val = tensor([2])]; + tensor squeeze_14_cast_fp16 = squeeze(axes = squeeze_14_axes_0, x = var_748_cast_fp16_2)[name = string("squeeze_14_cast_fp16")]; + tensor var_753_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = squeeze_12_cast_fp16)[name = string("op_753_cast_fp16")]; + tensor var_755_axes_0 = const()[name = string("op_755_axes_0"), val = tensor([-1])]; + bool var_755_keep_dims_0 = const()[name = string("op_755_keep_dims_0"), val = bool(true)]; + tensor var_755_cast_fp16 = reduce_mean(axes = var_755_axes_0, keep_dims = var_755_keep_dims_0, x = var_753_cast_fp16)[name = string("op_755_cast_fp16")]; + fp16 var_756_to_fp16 = const()[name = string("op_756_to_fp16"), val = fp16(0x1p-23)]; + tensor var_757_cast_fp16 = add(x = var_755_cast_fp16, y = var_756_to_fp16)[name = string("op_757_cast_fp16")]; + fp32 var_758_epsilon_0 = const()[name = string("op_758_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_758_cast_fp16 = rsqrt(epsilon = var_758_epsilon_0, x = var_757_cast_fp16)[name = string("op_758_cast_fp16")]; + tensor var_759_cast_fp16 = mul(x = squeeze_12_cast_fp16, y = var_758_cast_fp16)[name = string("op_759_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831552)))]; + tensor var_760_cast_fp16 = mul(x = var_759_cast_fp16, y = module_model_encoder_module_blocks_4_attn_q_norm_weight_to_fp16)[name = string("op_760_cast_fp16")]; + tensor var_763_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = squeeze_13_cast_fp16)[name = string("op_763_cast_fp16")]; + tensor var_765_axes_0 = const()[name = string("op_765_axes_0"), val = tensor([-1])]; + bool var_765_keep_dims_0 = const()[name = string("op_765_keep_dims_0"), val = bool(true)]; + tensor var_765_cast_fp16 = reduce_mean(axes = var_765_axes_0, keep_dims = var_765_keep_dims_0, x = var_763_cast_fp16)[name = string("op_765_cast_fp16")]; + fp16 var_766_to_fp16 = const()[name = string("op_766_to_fp16"), val = fp16(0x1p-23)]; + tensor var_767_cast_fp16 = add(x = var_765_cast_fp16, y = var_766_to_fp16)[name = string("op_767_cast_fp16")]; + fp32 var_768_epsilon_0 = const()[name = string("op_768_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_768_cast_fp16 = rsqrt(epsilon = var_768_epsilon_0, x = var_767_cast_fp16)[name = string("op_768_cast_fp16")]; + tensor var_769_cast_fp16 = mul(x = squeeze_13_cast_fp16, y = var_768_cast_fp16)[name = string("op_769_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831744)))]; + tensor var_770_cast_fp16 = mul(x = var_769_cast_fp16, y = module_model_encoder_module_blocks_4_attn_k_norm_weight_to_fp16)[name = string("op_770_cast_fp16")]; + tensor transpose_84_perm_0 = const()[name = string("transpose_84_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_85_perm_0 = const()[name = string("transpose_85_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86_perm_0 = const()[name = string("transpose_86_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_86 = transpose(perm = transpose_86_perm_0, x = squeeze_14_cast_fp16)[name = string("transpose_138")]; + tensor transpose_85 = transpose(perm = transpose_85_perm_0, x = var_770_cast_fp16)[name = string("transpose_139")]; + tensor transpose_84 = transpose(perm = transpose_84_perm_0, x = var_760_cast_fp16)[name = string("transpose_140")]; + tensor out_9_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_85, query = transpose_84, value = transpose_86)[name = string("out_9_cast_fp16")]; + tensor var_781_perm_0 = const()[name = string("op_781_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_782 = const()[name = string("op_782"), val = tensor([1, 864, 512])]; + tensor var_781_cast_fp16 = transpose(perm = var_781_perm_0, x = out_9_cast_fp16)[name = string("transpose_137")]; + tensor input_125_cast_fp16 = reshape(shape = var_782, x = var_781_cast_fp16)[name = string("input_125_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22831936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23094144))))[name = string("module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23095232)))]; + tensor linear_33_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_4_attn_proj_weight_to_fp16_quantized, x = input_125_cast_fp16)[name = string("linear_33_cast_fp16")]; + tensor var_788_cast_fp16 = mul(x = var_727_cast_fp16, y = linear_33_cast_fp16)[name = string("op_788_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = input_123_cast_fp16, y = var_788_cast_fp16)[name = string("input_129_cast_fp16")]; + tensor var_790_axes_0 = const()[name = string("op_790_axes_0"), val = tensor([1])]; + tensor var_790_cast_fp16 = expand_dims(axes = var_790_axes_0, x = var_721_cast_fp16_5)[name = string("op_790_cast_fp16")]; + tensor x_31_axes_0 = const()[name = string("x_31_axes_0"), val = tensor([-1])]; + tensor x_31_cast_fp16 = layer_norm(axes = x_31_axes_0, epsilon = var_70_to_fp16, x = input_129_cast_fp16)[name = string("x_31_cast_fp16")]; + tensor var_793_axes_0 = const()[name = string("op_793_axes_0"), val = tensor([1])]; + tensor var_793_cast_fp16 = expand_dims(axes = var_793_axes_0, x = var_721_cast_fp16_4)[name = string("op_793_cast_fp16")]; + fp16 var_794_to_fp16 = const()[name = string("op_794_to_fp16"), val = fp16(0x1p+0)]; + tensor var_795_cast_fp16 = add(x = var_793_cast_fp16, y = var_794_to_fp16)[name = string("op_795_cast_fp16")]; + tensor var_796_cast_fp16 = mul(x = x_31_cast_fp16, y = var_795_cast_fp16)[name = string("op_796_cast_fp16")]; + tensor var_797_axes_0 = const()[name = string("op_797_axes_0"), val = tensor([1])]; + tensor var_797_cast_fp16 = expand_dims(axes = var_797_axes_0, x = var_721_cast_fp16_3)[name = string("op_797_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = var_796_cast_fp16, y = var_797_cast_fp16)[name = string("input_131_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23096320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24144960))))[name = string("module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24149120)))]; + tensor linear_34_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_0_weight_to_fp16_quantized, x = input_131_cast_fp16)[name = string("linear_34_cast_fp16")]; + string input_135_mode_0 = const()[name = string("input_135_mode_0"), val = string("EXACT")]; + tensor input_135_cast_fp16 = gelu(mode = input_135_mode_0, x = linear_34_cast_fp16)[name = string("input_135_cast_fp16")]; + tensor module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24153280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25201920))))[name = string("module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25203008)))]; + tensor linear_35_cast_fp16 = linear(bias = module_model_encoder_module_blocks_4_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_4_ffn_3_weight_to_fp16_quantized, x = input_135_cast_fp16)[name = string("linear_35_cast_fp16")]; + tensor var_810_cast_fp16 = mul(x = var_790_cast_fp16, y = linear_35_cast_fp16)[name = string("op_810_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_129_cast_fp16, y = var_810_cast_fp16)[name = string("input_143_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25204096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777024))))[name = string("module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26783232)))]; + tensor linear_36_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_36_cast_fp16")]; + tensor var_819_split_sizes_0 = const()[name = string("op_819_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_819_axis_0 = const()[name = string("op_819_axis_0"), val = int32(-1)]; + tensor var_819_cast_fp16_0, tensor var_819_cast_fp16_1, tensor var_819_cast_fp16_2, tensor var_819_cast_fp16_3, tensor var_819_cast_fp16_4, tensor var_819_cast_fp16_5 = split(axis = var_819_axis_0, split_sizes = var_819_split_sizes_0, x = linear_36_cast_fp16)[name = string("op_819_cast_fp16")]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([1])]; + tensor var_825_cast_fp16 = expand_dims(axes = var_825_axes_0, x = var_819_cast_fp16_2)[name = string("op_825_cast_fp16")]; + tensor x_33_axes_0 = const()[name = string("x_33_axes_0"), val = tensor([-1])]; + tensor x_33_cast_fp16 = layer_norm(axes = x_33_axes_0, epsilon = var_70_to_fp16, x = input_143_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor var_828_axes_0 = const()[name = string("op_828_axes_0"), val = tensor([1])]; + tensor var_828_cast_fp16 = expand_dims(axes = var_828_axes_0, x = var_819_cast_fp16_1)[name = string("op_828_cast_fp16")]; + fp16 var_829_to_fp16 = const()[name = string("op_829_to_fp16"), val = fp16(0x1p+0)]; + tensor var_830_cast_fp16 = add(x = var_828_cast_fp16, y = var_829_to_fp16)[name = string("op_830_cast_fp16")]; + tensor var_831_cast_fp16 = mul(x = x_33_cast_fp16, y = var_830_cast_fp16)[name = string("op_831_cast_fp16")]; + tensor var_832_axes_0 = const()[name = string("op_832_axes_0"), val = tensor([1])]; + tensor var_832_cast_fp16 = expand_dims(axes = var_832_axes_0, x = var_819_cast_fp16_0)[name = string("op_832_cast_fp16")]; + tensor x_35_cast_fp16 = add(x = var_831_cast_fp16, y = var_832_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26789440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27575936))))[name = string("module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27579072)))]; + tensor linear_37_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_qkv_weight_to_fp16_quantized, x = x_35_cast_fp16)[name = string("linear_37_cast_fp16")]; + tensor var_844 = const()[name = string("op_844"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_11_cast_fp16 = reshape(shape = var_844, x = linear_37_cast_fp16)[name = string("qkv_11_cast_fp16")]; + tensor var_846_split_sizes_0 = const()[name = string("op_846_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_846_axis_0 = const()[name = string("op_846_axis_0"), val = int32(2)]; + tensor var_846_cast_fp16_0, tensor var_846_cast_fp16_1, tensor var_846_cast_fp16_2 = split(axis = var_846_axis_0, split_sizes = var_846_split_sizes_0, x = qkv_11_cast_fp16)[name = string("op_846_cast_fp16")]; + tensor squeeze_15_axes_0 = const()[name = string("squeeze_15_axes_0"), val = tensor([2])]; + tensor squeeze_15_cast_fp16 = squeeze(axes = squeeze_15_axes_0, x = var_846_cast_fp16_0)[name = string("squeeze_15_cast_fp16")]; + tensor squeeze_16_axes_0 = const()[name = string("squeeze_16_axes_0"), val = tensor([2])]; + tensor squeeze_16_cast_fp16 = squeeze(axes = squeeze_16_axes_0, x = var_846_cast_fp16_1)[name = string("squeeze_16_cast_fp16")]; + tensor squeeze_17_axes_0 = const()[name = string("squeeze_17_axes_0"), val = tensor([2])]; + tensor squeeze_17_cast_fp16 = squeeze(axes = squeeze_17_axes_0, x = var_846_cast_fp16_2)[name = string("squeeze_17_cast_fp16")]; + tensor var_851_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = squeeze_15_cast_fp16)[name = string("op_851_cast_fp16")]; + tensor var_853_axes_0 = const()[name = string("op_853_axes_0"), val = tensor([-1])]; + bool var_853_keep_dims_0 = const()[name = string("op_853_keep_dims_0"), val = bool(true)]; + tensor var_853_cast_fp16 = reduce_mean(axes = var_853_axes_0, keep_dims = var_853_keep_dims_0, x = var_851_cast_fp16)[name = string("op_853_cast_fp16")]; + fp16 var_854_to_fp16 = const()[name = string("op_854_to_fp16"), val = fp16(0x1p-23)]; + tensor var_855_cast_fp16 = add(x = var_853_cast_fp16, y = var_854_to_fp16)[name = string("op_855_cast_fp16")]; + fp32 var_856_epsilon_0 = const()[name = string("op_856_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_856_cast_fp16 = rsqrt(epsilon = var_856_epsilon_0, x = var_855_cast_fp16)[name = string("op_856_cast_fp16")]; + tensor var_857_cast_fp16 = mul(x = squeeze_15_cast_fp16, y = var_856_cast_fp16)[name = string("op_857_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582208)))]; + tensor var_858_cast_fp16 = mul(x = var_857_cast_fp16, y = module_model_encoder_module_blocks_5_attn_q_norm_weight_to_fp16)[name = string("op_858_cast_fp16")]; + tensor var_861_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = squeeze_16_cast_fp16)[name = string("op_861_cast_fp16")]; + tensor var_863_axes_0 = const()[name = string("op_863_axes_0"), val = tensor([-1])]; + bool var_863_keep_dims_0 = const()[name = string("op_863_keep_dims_0"), val = bool(true)]; + tensor var_863_cast_fp16 = reduce_mean(axes = var_863_axes_0, keep_dims = var_863_keep_dims_0, x = var_861_cast_fp16)[name = string("op_863_cast_fp16")]; + fp16 var_864_to_fp16 = const()[name = string("op_864_to_fp16"), val = fp16(0x1p-23)]; + tensor var_865_cast_fp16 = add(x = var_863_cast_fp16, y = var_864_to_fp16)[name = string("op_865_cast_fp16")]; + fp32 var_866_epsilon_0 = const()[name = string("op_866_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_866_cast_fp16 = rsqrt(epsilon = var_866_epsilon_0, x = var_865_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor var_867_cast_fp16 = mul(x = squeeze_16_cast_fp16, y = var_866_cast_fp16)[name = string("op_867_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582400)))]; + tensor var_868_cast_fp16 = mul(x = var_867_cast_fp16, y = module_model_encoder_module_blocks_5_attn_k_norm_weight_to_fp16)[name = string("op_868_cast_fp16")]; + tensor transpose_87_perm_0 = const()[name = string("transpose_87_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_88_perm_0 = const()[name = string("transpose_88_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89_perm_0 = const()[name = string("transpose_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_89 = transpose(perm = transpose_89_perm_0, x = squeeze_17_cast_fp16)[name = string("transpose_134")]; + tensor transpose_88 = transpose(perm = transpose_88_perm_0, x = var_868_cast_fp16)[name = string("transpose_135")]; + tensor transpose_87 = transpose(perm = transpose_87_perm_0, x = var_858_cast_fp16)[name = string("transpose_136")]; + tensor out_11_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_88, query = transpose_87, value = transpose_89)[name = string("out_11_cast_fp16")]; + tensor var_879_perm_0 = const()[name = string("op_879_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_880 = const()[name = string("op_880"), val = tensor([1, 864, 512])]; + tensor var_879_cast_fp16 = transpose(perm = var_879_perm_0, x = out_11_cast_fp16)[name = string("transpose_133")]; + tensor input_145_cast_fp16 = reshape(shape = var_880, x = var_879_cast_fp16)[name = string("input_145_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27582592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27844800))))[name = string("module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27845888)))]; + tensor linear_38_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_5_attn_proj_weight_to_fp16_quantized, x = input_145_cast_fp16)[name = string("linear_38_cast_fp16")]; + tensor var_886_cast_fp16 = mul(x = var_825_cast_fp16, y = linear_38_cast_fp16)[name = string("op_886_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = var_886_cast_fp16)[name = string("input_149_cast_fp16")]; + tensor var_888_axes_0 = const()[name = string("op_888_axes_0"), val = tensor([1])]; + tensor var_888_cast_fp16 = expand_dims(axes = var_888_axes_0, x = var_819_cast_fp16_5)[name = string("op_888_cast_fp16")]; + tensor x_37_axes_0 = const()[name = string("x_37_axes_0"), val = tensor([-1])]; + tensor x_37_cast_fp16 = layer_norm(axes = x_37_axes_0, epsilon = var_70_to_fp16, x = input_149_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor var_891_axes_0 = const()[name = string("op_891_axes_0"), val = tensor([1])]; + tensor var_891_cast_fp16 = expand_dims(axes = var_891_axes_0, x = var_819_cast_fp16_4)[name = string("op_891_cast_fp16")]; + fp16 var_892_to_fp16 = const()[name = string("op_892_to_fp16"), val = fp16(0x1p+0)]; + tensor var_893_cast_fp16 = add(x = var_891_cast_fp16, y = var_892_to_fp16)[name = string("op_893_cast_fp16")]; + tensor var_894_cast_fp16 = mul(x = x_37_cast_fp16, y = var_893_cast_fp16)[name = string("op_894_cast_fp16")]; + tensor var_895_axes_0 = const()[name = string("op_895_axes_0"), val = tensor([1])]; + tensor var_895_cast_fp16 = expand_dims(axes = var_895_axes_0, x = var_819_cast_fp16_3)[name = string("op_895_cast_fp16")]; + tensor input_151_cast_fp16 = add(x = var_894_cast_fp16, y = var_895_cast_fp16)[name = string("input_151_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27846976))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28895616))))[name = string("module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28899776)))]; + tensor linear_39_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_0_weight_to_fp16_quantized, x = input_151_cast_fp16)[name = string("linear_39_cast_fp16")]; + string input_155_mode_0 = const()[name = string("input_155_mode_0"), val = string("EXACT")]; + tensor input_155_cast_fp16 = gelu(mode = input_155_mode_0, x = linear_39_cast_fp16)[name = string("input_155_cast_fp16")]; + tensor module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28903936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29952576))))[name = string("module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29953664)))]; + tensor linear_40_cast_fp16 = linear(bias = module_model_encoder_module_blocks_5_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_5_ffn_3_weight_to_fp16_quantized, x = input_155_cast_fp16)[name = string("linear_40_cast_fp16")]; + tensor var_908_cast_fp16 = mul(x = var_888_cast_fp16, y = linear_40_cast_fp16)[name = string("op_908_cast_fp16")]; + tensor input_163_cast_fp16 = add(x = input_149_cast_fp16, y = var_908_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29954752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31527680))))[name = string("module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31533888)))]; + tensor linear_41_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_41_cast_fp16")]; + tensor var_917_split_sizes_0 = const()[name = string("op_917_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_917_axis_0 = const()[name = string("op_917_axis_0"), val = int32(-1)]; + tensor var_917_cast_fp16_0, tensor var_917_cast_fp16_1, tensor var_917_cast_fp16_2, tensor var_917_cast_fp16_3, tensor var_917_cast_fp16_4, tensor var_917_cast_fp16_5 = split(axis = var_917_axis_0, split_sizes = var_917_split_sizes_0, x = linear_41_cast_fp16)[name = string("op_917_cast_fp16")]; + tensor var_923_axes_0 = const()[name = string("op_923_axes_0"), val = tensor([1])]; + tensor var_923_cast_fp16 = expand_dims(axes = var_923_axes_0, x = var_917_cast_fp16_2)[name = string("op_923_cast_fp16")]; + tensor x_39_axes_0 = const()[name = string("x_39_axes_0"), val = tensor([-1])]; + tensor x_39_cast_fp16 = layer_norm(axes = x_39_axes_0, epsilon = var_70_to_fp16, x = input_163_cast_fp16)[name = string("x_39_cast_fp16")]; + tensor var_926_axes_0 = const()[name = string("op_926_axes_0"), val = tensor([1])]; + tensor var_926_cast_fp16 = expand_dims(axes = var_926_axes_0, x = var_917_cast_fp16_1)[name = string("op_926_cast_fp16")]; + fp16 var_927_to_fp16 = const()[name = string("op_927_to_fp16"), val = fp16(0x1p+0)]; + tensor var_928_cast_fp16 = add(x = var_926_cast_fp16, y = var_927_to_fp16)[name = string("op_928_cast_fp16")]; + tensor var_929_cast_fp16 = mul(x = x_39_cast_fp16, y = var_928_cast_fp16)[name = string("op_929_cast_fp16")]; + tensor var_930_axes_0 = const()[name = string("op_930_axes_0"), val = tensor([1])]; + tensor var_930_cast_fp16 = expand_dims(axes = var_930_axes_0, x = var_917_cast_fp16_0)[name = string("op_930_cast_fp16")]; + tensor x_41_cast_fp16 = add(x = var_929_cast_fp16, y = var_930_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31540096))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32326592))))[name = string("module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32329728)))]; + tensor linear_42_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_qkv_weight_to_fp16_quantized, x = x_41_cast_fp16)[name = string("linear_42_cast_fp16")]; + tensor var_942 = const()[name = string("op_942"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_13_cast_fp16 = reshape(shape = var_942, x = linear_42_cast_fp16)[name = string("qkv_13_cast_fp16")]; + tensor var_944_split_sizes_0 = const()[name = string("op_944_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_944_axis_0 = const()[name = string("op_944_axis_0"), val = int32(2)]; + tensor var_944_cast_fp16_0, tensor var_944_cast_fp16_1, tensor var_944_cast_fp16_2 = split(axis = var_944_axis_0, split_sizes = var_944_split_sizes_0, x = qkv_13_cast_fp16)[name = string("op_944_cast_fp16")]; + tensor squeeze_18_axes_0 = const()[name = string("squeeze_18_axes_0"), val = tensor([2])]; + tensor squeeze_18_cast_fp16 = squeeze(axes = squeeze_18_axes_0, x = var_944_cast_fp16_0)[name = string("squeeze_18_cast_fp16")]; + tensor squeeze_19_axes_0 = const()[name = string("squeeze_19_axes_0"), val = tensor([2])]; + tensor squeeze_19_cast_fp16 = squeeze(axes = squeeze_19_axes_0, x = var_944_cast_fp16_1)[name = string("squeeze_19_cast_fp16")]; + tensor squeeze_20_axes_0 = const()[name = string("squeeze_20_axes_0"), val = tensor([2])]; + tensor squeeze_20_cast_fp16 = squeeze(axes = squeeze_20_axes_0, x = var_944_cast_fp16_2)[name = string("squeeze_20_cast_fp16")]; + tensor var_949_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = squeeze_18_cast_fp16)[name = string("op_949_cast_fp16")]; + tensor var_951_axes_0 = const()[name = string("op_951_axes_0"), val = tensor([-1])]; + bool var_951_keep_dims_0 = const()[name = string("op_951_keep_dims_0"), val = bool(true)]; + tensor var_951_cast_fp16 = reduce_mean(axes = var_951_axes_0, keep_dims = var_951_keep_dims_0, x = var_949_cast_fp16)[name = string("op_951_cast_fp16")]; + fp16 var_952_to_fp16 = const()[name = string("op_952_to_fp16"), val = fp16(0x1p-23)]; + tensor var_953_cast_fp16 = add(x = var_951_cast_fp16, y = var_952_to_fp16)[name = string("op_953_cast_fp16")]; + fp32 var_954_epsilon_0 = const()[name = string("op_954_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_954_cast_fp16 = rsqrt(epsilon = var_954_epsilon_0, x = var_953_cast_fp16)[name = string("op_954_cast_fp16")]; + tensor var_955_cast_fp16 = mul(x = squeeze_18_cast_fp16, y = var_954_cast_fp16)[name = string("op_955_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32332864)))]; + tensor var_956_cast_fp16 = mul(x = var_955_cast_fp16, y = module_model_encoder_module_blocks_6_attn_q_norm_weight_to_fp16)[name = string("op_956_cast_fp16")]; + tensor var_959_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = squeeze_19_cast_fp16)[name = string("op_959_cast_fp16")]; + tensor var_961_axes_0 = const()[name = string("op_961_axes_0"), val = tensor([-1])]; + bool var_961_keep_dims_0 = const()[name = string("op_961_keep_dims_0"), val = bool(true)]; + tensor var_961_cast_fp16 = reduce_mean(axes = var_961_axes_0, keep_dims = var_961_keep_dims_0, x = var_959_cast_fp16)[name = string("op_961_cast_fp16")]; + fp16 var_962_to_fp16 = const()[name = string("op_962_to_fp16"), val = fp16(0x1p-23)]; + tensor var_963_cast_fp16 = add(x = var_961_cast_fp16, y = var_962_to_fp16)[name = string("op_963_cast_fp16")]; + fp32 var_964_epsilon_0 = const()[name = string("op_964_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_964_cast_fp16 = rsqrt(epsilon = var_964_epsilon_0, x = var_963_cast_fp16)[name = string("op_964_cast_fp16")]; + tensor var_965_cast_fp16 = mul(x = squeeze_19_cast_fp16, y = var_964_cast_fp16)[name = string("op_965_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333056)))]; + tensor var_966_cast_fp16 = mul(x = var_965_cast_fp16, y = module_model_encoder_module_blocks_6_attn_k_norm_weight_to_fp16)[name = string("op_966_cast_fp16")]; + tensor transpose_90_perm_0 = const()[name = string("transpose_90_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_91_perm_0 = const()[name = string("transpose_91_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92_perm_0 = const()[name = string("transpose_92_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_92 = transpose(perm = transpose_92_perm_0, x = squeeze_20_cast_fp16)[name = string("transpose_130")]; + tensor transpose_91 = transpose(perm = transpose_91_perm_0, x = var_966_cast_fp16)[name = string("transpose_131")]; + tensor transpose_90 = transpose(perm = transpose_90_perm_0, x = var_956_cast_fp16)[name = string("transpose_132")]; + tensor out_13_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_91, query = transpose_90, value = transpose_92)[name = string("out_13_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_978 = const()[name = string("op_978"), val = tensor([1, 864, 512])]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = out_13_cast_fp16)[name = string("transpose_129")]; + tensor input_165_cast_fp16 = reshape(shape = var_978, x = var_977_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32333248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32595456))))[name = string("module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32596544)))]; + tensor linear_43_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_6_attn_proj_weight_to_fp16_quantized, x = input_165_cast_fp16)[name = string("linear_43_cast_fp16")]; + tensor var_984_cast_fp16 = mul(x = var_923_cast_fp16, y = linear_43_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor input_169_cast_fp16 = add(x = input_163_cast_fp16, y = var_984_cast_fp16)[name = string("input_169_cast_fp16")]; + tensor var_986_axes_0 = const()[name = string("op_986_axes_0"), val = tensor([1])]; + tensor var_986_cast_fp16 = expand_dims(axes = var_986_axes_0, x = var_917_cast_fp16_5)[name = string("op_986_cast_fp16")]; + tensor x_43_axes_0 = const()[name = string("x_43_axes_0"), val = tensor([-1])]; + tensor x_43_cast_fp16 = layer_norm(axes = x_43_axes_0, epsilon = var_70_to_fp16, x = input_169_cast_fp16)[name = string("x_43_cast_fp16")]; + tensor var_989_axes_0 = const()[name = string("op_989_axes_0"), val = tensor([1])]; + tensor var_989_cast_fp16 = expand_dims(axes = var_989_axes_0, x = var_917_cast_fp16_4)[name = string("op_989_cast_fp16")]; + fp16 var_990_to_fp16 = const()[name = string("op_990_to_fp16"), val = fp16(0x1p+0)]; + tensor var_991_cast_fp16 = add(x = var_989_cast_fp16, y = var_990_to_fp16)[name = string("op_991_cast_fp16")]; + tensor var_992_cast_fp16 = mul(x = x_43_cast_fp16, y = var_991_cast_fp16)[name = string("op_992_cast_fp16")]; + tensor var_993_axes_0 = const()[name = string("op_993_axes_0"), val = tensor([1])]; + tensor var_993_cast_fp16 = expand_dims(axes = var_993_axes_0, x = var_917_cast_fp16_3)[name = string("op_993_cast_fp16")]; + tensor input_171_cast_fp16 = add(x = var_992_cast_fp16, y = var_993_cast_fp16)[name = string("input_171_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33646272))))[name = string("module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33650432)))]; + tensor linear_44_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_0_weight_to_fp16_quantized, x = input_171_cast_fp16)[name = string("linear_44_cast_fp16")]; + string input_175_mode_0 = const()[name = string("input_175_mode_0"), val = string("EXACT")]; + tensor input_175_cast_fp16 = gelu(mode = input_175_mode_0, x = linear_44_cast_fp16)[name = string("input_175_cast_fp16")]; + tensor module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33654592))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34703232))))[name = string("module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34704320)))]; + tensor linear_45_cast_fp16 = linear(bias = module_model_encoder_module_blocks_6_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_6_ffn_3_weight_to_fp16_quantized, x = input_175_cast_fp16)[name = string("linear_45_cast_fp16")]; + tensor var_1006_cast_fp16 = mul(x = var_986_cast_fp16, y = linear_45_cast_fp16)[name = string("op_1006_cast_fp16")]; + tensor input_183_cast_fp16 = add(x = input_169_cast_fp16, y = var_1006_cast_fp16)[name = string("input_183_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34705408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36278336))))[name = string("module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36284544)))]; + tensor linear_46_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_46_cast_fp16")]; + tensor var_1015_split_sizes_0 = const()[name = string("op_1015_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1015_axis_0 = const()[name = string("op_1015_axis_0"), val = int32(-1)]; + tensor var_1015_cast_fp16_0, tensor var_1015_cast_fp16_1, tensor var_1015_cast_fp16_2, tensor var_1015_cast_fp16_3, tensor var_1015_cast_fp16_4, tensor var_1015_cast_fp16_5 = split(axis = var_1015_axis_0, split_sizes = var_1015_split_sizes_0, x = linear_46_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1021_axes_0 = const()[name = string("op_1021_axes_0"), val = tensor([1])]; + tensor var_1021_cast_fp16 = expand_dims(axes = var_1021_axes_0, x = var_1015_cast_fp16_2)[name = string("op_1021_cast_fp16")]; + tensor x_45_axes_0 = const()[name = string("x_45_axes_0"), val = tensor([-1])]; + tensor x_45_cast_fp16 = layer_norm(axes = x_45_axes_0, epsilon = var_70_to_fp16, x = input_183_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor var_1024_axes_0 = const()[name = string("op_1024_axes_0"), val = tensor([1])]; + tensor var_1024_cast_fp16 = expand_dims(axes = var_1024_axes_0, x = var_1015_cast_fp16_1)[name = string("op_1024_cast_fp16")]; + fp16 var_1025_to_fp16 = const()[name = string("op_1025_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1026_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1025_to_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1027_cast_fp16 = mul(x = x_45_cast_fp16, y = var_1026_cast_fp16)[name = string("op_1027_cast_fp16")]; + tensor var_1028_axes_0 = const()[name = string("op_1028_axes_0"), val = tensor([1])]; + tensor var_1028_cast_fp16 = expand_dims(axes = var_1028_axes_0, x = var_1015_cast_fp16_0)[name = string("op_1028_cast_fp16")]; + tensor x_47_cast_fp16 = add(x = var_1027_cast_fp16, y = var_1028_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(36290752))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37077248))))[name = string("module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37080384)))]; + tensor linear_47_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_qkv_weight_to_fp16_quantized, x = x_47_cast_fp16)[name = string("linear_47_cast_fp16")]; + tensor var_1040 = const()[name = string("op_1040"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_15_cast_fp16 = reshape(shape = var_1040, x = linear_47_cast_fp16)[name = string("qkv_15_cast_fp16")]; + tensor var_1042_split_sizes_0 = const()[name = string("op_1042_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1042_axis_0 = const()[name = string("op_1042_axis_0"), val = int32(2)]; + tensor var_1042_cast_fp16_0, tensor var_1042_cast_fp16_1, tensor var_1042_cast_fp16_2 = split(axis = var_1042_axis_0, split_sizes = var_1042_split_sizes_0, x = qkv_15_cast_fp16)[name = string("op_1042_cast_fp16")]; + tensor squeeze_21_axes_0 = const()[name = string("squeeze_21_axes_0"), val = tensor([2])]; + tensor squeeze_21_cast_fp16 = squeeze(axes = squeeze_21_axes_0, x = var_1042_cast_fp16_0)[name = string("squeeze_21_cast_fp16")]; + tensor squeeze_22_axes_0 = const()[name = string("squeeze_22_axes_0"), val = tensor([2])]; + tensor squeeze_22_cast_fp16 = squeeze(axes = squeeze_22_axes_0, x = var_1042_cast_fp16_1)[name = string("squeeze_22_cast_fp16")]; + tensor squeeze_23_axes_0 = const()[name = string("squeeze_23_axes_0"), val = tensor([2])]; + tensor squeeze_23_cast_fp16 = squeeze(axes = squeeze_23_axes_0, x = var_1042_cast_fp16_2)[name = string("squeeze_23_cast_fp16")]; + tensor var_1047_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = squeeze_21_cast_fp16)[name = string("op_1047_cast_fp16")]; + tensor var_1049_axes_0 = const()[name = string("op_1049_axes_0"), val = tensor([-1])]; + bool var_1049_keep_dims_0 = const()[name = string("op_1049_keep_dims_0"), val = bool(true)]; + tensor var_1049_cast_fp16 = reduce_mean(axes = var_1049_axes_0, keep_dims = var_1049_keep_dims_0, x = var_1047_cast_fp16)[name = string("op_1049_cast_fp16")]; + fp16 var_1050_to_fp16 = const()[name = string("op_1050_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1051_cast_fp16 = add(x = var_1049_cast_fp16, y = var_1050_to_fp16)[name = string("op_1051_cast_fp16")]; + fp32 var_1052_epsilon_0 = const()[name = string("op_1052_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1052_cast_fp16 = rsqrt(epsilon = var_1052_epsilon_0, x = var_1051_cast_fp16)[name = string("op_1052_cast_fp16")]; + tensor var_1053_cast_fp16 = mul(x = squeeze_21_cast_fp16, y = var_1052_cast_fp16)[name = string("op_1053_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083520)))]; + tensor var_1054_cast_fp16 = mul(x = var_1053_cast_fp16, y = module_model_encoder_module_blocks_7_attn_q_norm_weight_to_fp16)[name = string("op_1054_cast_fp16")]; + tensor var_1057_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = squeeze_22_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor var_1059_axes_0 = const()[name = string("op_1059_axes_0"), val = tensor([-1])]; + bool var_1059_keep_dims_0 = const()[name = string("op_1059_keep_dims_0"), val = bool(true)]; + tensor var_1059_cast_fp16 = reduce_mean(axes = var_1059_axes_0, keep_dims = var_1059_keep_dims_0, x = var_1057_cast_fp16)[name = string("op_1059_cast_fp16")]; + fp16 var_1060_to_fp16 = const()[name = string("op_1060_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1061_cast_fp16 = add(x = var_1059_cast_fp16, y = var_1060_to_fp16)[name = string("op_1061_cast_fp16")]; + fp32 var_1062_epsilon_0 = const()[name = string("op_1062_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1062_cast_fp16 = rsqrt(epsilon = var_1062_epsilon_0, x = var_1061_cast_fp16)[name = string("op_1062_cast_fp16")]; + tensor var_1063_cast_fp16 = mul(x = squeeze_22_cast_fp16, y = var_1062_cast_fp16)[name = string("op_1063_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083712)))]; + tensor var_1064_cast_fp16 = mul(x = var_1063_cast_fp16, y = module_model_encoder_module_blocks_7_attn_k_norm_weight_to_fp16)[name = string("op_1064_cast_fp16")]; + tensor transpose_93_perm_0 = const()[name = string("transpose_93_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_94_perm_0 = const()[name = string("transpose_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95_perm_0 = const()[name = string("transpose_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_95 = transpose(perm = transpose_95_perm_0, x = squeeze_23_cast_fp16)[name = string("transpose_126")]; + tensor transpose_94 = transpose(perm = transpose_94_perm_0, x = var_1064_cast_fp16)[name = string("transpose_127")]; + tensor transpose_93 = transpose(perm = transpose_93_perm_0, x = var_1054_cast_fp16)[name = string("transpose_128")]; + tensor out_15_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_94, query = transpose_93, value = transpose_95)[name = string("out_15_cast_fp16")]; + tensor var_1075_perm_0 = const()[name = string("op_1075_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1076 = const()[name = string("op_1076"), val = tensor([1, 864, 512])]; + tensor var_1075_cast_fp16 = transpose(perm = var_1075_perm_0, x = out_15_cast_fp16)[name = string("transpose_125")]; + tensor input_185_cast_fp16 = reshape(shape = var_1076, x = var_1075_cast_fp16)[name = string("input_185_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37083904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37346112))))[name = string("module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37347200)))]; + tensor linear_48_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_7_attn_proj_weight_to_fp16_quantized, x = input_185_cast_fp16)[name = string("linear_48_cast_fp16")]; + tensor var_1082_cast_fp16 = mul(x = var_1021_cast_fp16, y = linear_48_cast_fp16)[name = string("op_1082_cast_fp16")]; + tensor input_189_cast_fp16 = add(x = input_183_cast_fp16, y = var_1082_cast_fp16)[name = string("input_189_cast_fp16")]; + tensor var_1084_axes_0 = const()[name = string("op_1084_axes_0"), val = tensor([1])]; + tensor var_1084_cast_fp16 = expand_dims(axes = var_1084_axes_0, x = var_1015_cast_fp16_5)[name = string("op_1084_cast_fp16")]; + tensor x_49_axes_0 = const()[name = string("x_49_axes_0"), val = tensor([-1])]; + tensor x_49_cast_fp16 = layer_norm(axes = x_49_axes_0, epsilon = var_70_to_fp16, x = input_189_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor var_1087_axes_0 = const()[name = string("op_1087_axes_0"), val = tensor([1])]; + tensor var_1087_cast_fp16 = expand_dims(axes = var_1087_axes_0, x = var_1015_cast_fp16_4)[name = string("op_1087_cast_fp16")]; + fp16 var_1088_to_fp16 = const()[name = string("op_1088_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1089_cast_fp16 = add(x = var_1087_cast_fp16, y = var_1088_to_fp16)[name = string("op_1089_cast_fp16")]; + tensor var_1090_cast_fp16 = mul(x = x_49_cast_fp16, y = var_1089_cast_fp16)[name = string("op_1090_cast_fp16")]; + tensor var_1091_axes_0 = const()[name = string("op_1091_axes_0"), val = tensor([1])]; + tensor var_1091_cast_fp16 = expand_dims(axes = var_1091_axes_0, x = var_1015_cast_fp16_3)[name = string("op_1091_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = var_1090_cast_fp16, y = var_1091_cast_fp16)[name = string("input_191_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37348288))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38396928))))[name = string("module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38401088)))]; + tensor linear_49_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_0_weight_to_fp16_quantized, x = input_191_cast_fp16)[name = string("linear_49_cast_fp16")]; + string input_195_mode_0 = const()[name = string("input_195_mode_0"), val = string("EXACT")]; + tensor input_195_cast_fp16 = gelu(mode = input_195_mode_0, x = linear_49_cast_fp16)[name = string("input_195_cast_fp16")]; + tensor module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38405248))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39453888))))[name = string("module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39454976)))]; + tensor linear_50_cast_fp16 = linear(bias = module_model_encoder_module_blocks_7_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_7_ffn_3_weight_to_fp16_quantized, x = input_195_cast_fp16)[name = string("linear_50_cast_fp16")]; + tensor var_1104_cast_fp16 = mul(x = var_1084_cast_fp16, y = linear_50_cast_fp16)[name = string("op_1104_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = input_189_cast_fp16, y = var_1104_cast_fp16)[name = string("input_203_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(39456064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41028992))))[name = string("module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41035200)))]; + tensor linear_51_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_51_cast_fp16")]; + tensor var_1113_split_sizes_0 = const()[name = string("op_1113_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1113_axis_0 = const()[name = string("op_1113_axis_0"), val = int32(-1)]; + tensor var_1113_cast_fp16_0, tensor var_1113_cast_fp16_1, tensor var_1113_cast_fp16_2, tensor var_1113_cast_fp16_3, tensor var_1113_cast_fp16_4, tensor var_1113_cast_fp16_5 = split(axis = var_1113_axis_0, split_sizes = var_1113_split_sizes_0, x = linear_51_cast_fp16)[name = string("op_1113_cast_fp16")]; + tensor var_1119_axes_0 = const()[name = string("op_1119_axes_0"), val = tensor([1])]; + tensor var_1119_cast_fp16 = expand_dims(axes = var_1119_axes_0, x = var_1113_cast_fp16_2)[name = string("op_1119_cast_fp16")]; + tensor x_51_axes_0 = const()[name = string("x_51_axes_0"), val = tensor([-1])]; + tensor x_51_cast_fp16 = layer_norm(axes = x_51_axes_0, epsilon = var_70_to_fp16, x = input_203_cast_fp16)[name = string("x_51_cast_fp16")]; + tensor var_1122_axes_0 = const()[name = string("op_1122_axes_0"), val = tensor([1])]; + tensor var_1122_cast_fp16 = expand_dims(axes = var_1122_axes_0, x = var_1113_cast_fp16_1)[name = string("op_1122_cast_fp16")]; + fp16 var_1123_to_fp16 = const()[name = string("op_1123_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1124_cast_fp16 = add(x = var_1122_cast_fp16, y = var_1123_to_fp16)[name = string("op_1124_cast_fp16")]; + tensor var_1125_cast_fp16 = mul(x = x_51_cast_fp16, y = var_1124_cast_fp16)[name = string("op_1125_cast_fp16")]; + tensor var_1126_axes_0 = const()[name = string("op_1126_axes_0"), val = tensor([1])]; + tensor var_1126_cast_fp16 = expand_dims(axes = var_1126_axes_0, x = var_1113_cast_fp16_0)[name = string("op_1126_cast_fp16")]; + tensor x_53_cast_fp16 = add(x = var_1125_cast_fp16, y = var_1126_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41041408))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41827904))))[name = string("module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41831040)))]; + tensor linear_52_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_qkv_weight_to_fp16_quantized, x = x_53_cast_fp16)[name = string("linear_52_cast_fp16")]; + tensor var_1138 = const()[name = string("op_1138"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_17_cast_fp16 = reshape(shape = var_1138, x = linear_52_cast_fp16)[name = string("qkv_17_cast_fp16")]; + tensor var_1140_split_sizes_0 = const()[name = string("op_1140_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1140_axis_0 = const()[name = string("op_1140_axis_0"), val = int32(2)]; + tensor var_1140_cast_fp16_0, tensor var_1140_cast_fp16_1, tensor var_1140_cast_fp16_2 = split(axis = var_1140_axis_0, split_sizes = var_1140_split_sizes_0, x = qkv_17_cast_fp16)[name = string("op_1140_cast_fp16")]; + tensor squeeze_24_axes_0 = const()[name = string("squeeze_24_axes_0"), val = tensor([2])]; + tensor squeeze_24_cast_fp16 = squeeze(axes = squeeze_24_axes_0, x = var_1140_cast_fp16_0)[name = string("squeeze_24_cast_fp16")]; + tensor squeeze_25_axes_0 = const()[name = string("squeeze_25_axes_0"), val = tensor([2])]; + tensor squeeze_25_cast_fp16 = squeeze(axes = squeeze_25_axes_0, x = var_1140_cast_fp16_1)[name = string("squeeze_25_cast_fp16")]; + tensor squeeze_26_axes_0 = const()[name = string("squeeze_26_axes_0"), val = tensor([2])]; + tensor squeeze_26_cast_fp16 = squeeze(axes = squeeze_26_axes_0, x = var_1140_cast_fp16_2)[name = string("squeeze_26_cast_fp16")]; + tensor var_1145_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = squeeze_24_cast_fp16)[name = string("op_1145_cast_fp16")]; + tensor var_1147_axes_0 = const()[name = string("op_1147_axes_0"), val = tensor([-1])]; + bool var_1147_keep_dims_0 = const()[name = string("op_1147_keep_dims_0"), val = bool(true)]; + tensor var_1147_cast_fp16 = reduce_mean(axes = var_1147_axes_0, keep_dims = var_1147_keep_dims_0, x = var_1145_cast_fp16)[name = string("op_1147_cast_fp16")]; + fp16 var_1148_to_fp16 = const()[name = string("op_1148_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1149_cast_fp16 = add(x = var_1147_cast_fp16, y = var_1148_to_fp16)[name = string("op_1149_cast_fp16")]; + fp32 var_1150_epsilon_0 = const()[name = string("op_1150_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1150_cast_fp16 = rsqrt(epsilon = var_1150_epsilon_0, x = var_1149_cast_fp16)[name = string("op_1150_cast_fp16")]; + tensor var_1151_cast_fp16 = mul(x = squeeze_24_cast_fp16, y = var_1150_cast_fp16)[name = string("op_1151_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834176)))]; + tensor var_1152_cast_fp16 = mul(x = var_1151_cast_fp16, y = module_model_encoder_module_blocks_8_attn_q_norm_weight_to_fp16)[name = string("op_1152_cast_fp16")]; + tensor var_1155_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = squeeze_25_cast_fp16)[name = string("op_1155_cast_fp16")]; + tensor var_1157_axes_0 = const()[name = string("op_1157_axes_0"), val = tensor([-1])]; + bool var_1157_keep_dims_0 = const()[name = string("op_1157_keep_dims_0"), val = bool(true)]; + tensor var_1157_cast_fp16 = reduce_mean(axes = var_1157_axes_0, keep_dims = var_1157_keep_dims_0, x = var_1155_cast_fp16)[name = string("op_1157_cast_fp16")]; + fp16 var_1158_to_fp16 = const()[name = string("op_1158_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1159_cast_fp16 = add(x = var_1157_cast_fp16, y = var_1158_to_fp16)[name = string("op_1159_cast_fp16")]; + fp32 var_1160_epsilon_0 = const()[name = string("op_1160_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1160_cast_fp16 = rsqrt(epsilon = var_1160_epsilon_0, x = var_1159_cast_fp16)[name = string("op_1160_cast_fp16")]; + tensor var_1161_cast_fp16 = mul(x = squeeze_25_cast_fp16, y = var_1160_cast_fp16)[name = string("op_1161_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834368)))]; + tensor var_1162_cast_fp16 = mul(x = var_1161_cast_fp16, y = module_model_encoder_module_blocks_8_attn_k_norm_weight_to_fp16)[name = string("op_1162_cast_fp16")]; + tensor transpose_96_perm_0 = const()[name = string("transpose_96_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_97_perm_0 = const()[name = string("transpose_97_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98_perm_0 = const()[name = string("transpose_98_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_98 = transpose(perm = transpose_98_perm_0, x = squeeze_26_cast_fp16)[name = string("transpose_122")]; + tensor transpose_97 = transpose(perm = transpose_97_perm_0, x = var_1162_cast_fp16)[name = string("transpose_123")]; + tensor transpose_96 = transpose(perm = transpose_96_perm_0, x = var_1152_cast_fp16)[name = string("transpose_124")]; + tensor out_17_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_97, query = transpose_96, value = transpose_98)[name = string("out_17_cast_fp16")]; + tensor var_1173_perm_0 = const()[name = string("op_1173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1174 = const()[name = string("op_1174"), val = tensor([1, 864, 512])]; + tensor var_1173_cast_fp16 = transpose(perm = var_1173_perm_0, x = out_17_cast_fp16)[name = string("transpose_121")]; + tensor input_205_cast_fp16 = reshape(shape = var_1174, x = var_1173_cast_fp16)[name = string("input_205_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(41834560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42096768))))[name = string("module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42097856)))]; + tensor linear_53_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_8_attn_proj_weight_to_fp16_quantized, x = input_205_cast_fp16)[name = string("linear_53_cast_fp16")]; + tensor var_1180_cast_fp16 = mul(x = var_1119_cast_fp16, y = linear_53_cast_fp16)[name = string("op_1180_cast_fp16")]; + tensor input_209_cast_fp16 = add(x = input_203_cast_fp16, y = var_1180_cast_fp16)[name = string("input_209_cast_fp16")]; + tensor var_1182_axes_0 = const()[name = string("op_1182_axes_0"), val = tensor([1])]; + tensor var_1182_cast_fp16 = expand_dims(axes = var_1182_axes_0, x = var_1113_cast_fp16_5)[name = string("op_1182_cast_fp16")]; + tensor x_55_axes_0 = const()[name = string("x_55_axes_0"), val = tensor([-1])]; + tensor x_55_cast_fp16 = layer_norm(axes = x_55_axes_0, epsilon = var_70_to_fp16, x = input_209_cast_fp16)[name = string("x_55_cast_fp16")]; + tensor var_1185_axes_0 = const()[name = string("op_1185_axes_0"), val = tensor([1])]; + tensor var_1185_cast_fp16 = expand_dims(axes = var_1185_axes_0, x = var_1113_cast_fp16_4)[name = string("op_1185_cast_fp16")]; + fp16 var_1186_to_fp16 = const()[name = string("op_1186_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1187_cast_fp16 = add(x = var_1185_cast_fp16, y = var_1186_to_fp16)[name = string("op_1187_cast_fp16")]; + tensor var_1188_cast_fp16 = mul(x = x_55_cast_fp16, y = var_1187_cast_fp16)[name = string("op_1188_cast_fp16")]; + tensor var_1189_axes_0 = const()[name = string("op_1189_axes_0"), val = tensor([1])]; + tensor var_1189_cast_fp16 = expand_dims(axes = var_1189_axes_0, x = var_1113_cast_fp16_3)[name = string("op_1189_cast_fp16")]; + tensor input_211_cast_fp16 = add(x = var_1188_cast_fp16, y = var_1189_cast_fp16)[name = string("input_211_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42098944))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43147584))))[name = string("module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43151744)))]; + tensor linear_54_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_0_weight_to_fp16_quantized, x = input_211_cast_fp16)[name = string("linear_54_cast_fp16")]; + string input_215_mode_0 = const()[name = string("input_215_mode_0"), val = string("EXACT")]; + tensor input_215_cast_fp16 = gelu(mode = input_215_mode_0, x = linear_54_cast_fp16)[name = string("input_215_cast_fp16")]; + tensor module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43155904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44204544))))[name = string("module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44205632)))]; + tensor linear_55_cast_fp16 = linear(bias = module_model_encoder_module_blocks_8_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_8_ffn_3_weight_to_fp16_quantized, x = input_215_cast_fp16)[name = string("linear_55_cast_fp16")]; + tensor var_1202_cast_fp16 = mul(x = var_1182_cast_fp16, y = linear_55_cast_fp16)[name = string("op_1202_cast_fp16")]; + tensor input_223_cast_fp16 = add(x = input_209_cast_fp16, y = var_1202_cast_fp16)[name = string("input_223_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44206720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45779648))))[name = string("module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45785856)))]; + tensor linear_56_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_56_cast_fp16")]; + tensor var_1211_split_sizes_0 = const()[name = string("op_1211_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1211_axis_0 = const()[name = string("op_1211_axis_0"), val = int32(-1)]; + tensor var_1211_cast_fp16_0, tensor var_1211_cast_fp16_1, tensor var_1211_cast_fp16_2, tensor var_1211_cast_fp16_3, tensor var_1211_cast_fp16_4, tensor var_1211_cast_fp16_5 = split(axis = var_1211_axis_0, split_sizes = var_1211_split_sizes_0, x = linear_56_cast_fp16)[name = string("op_1211_cast_fp16")]; + tensor var_1217_axes_0 = const()[name = string("op_1217_axes_0"), val = tensor([1])]; + tensor var_1217_cast_fp16 = expand_dims(axes = var_1217_axes_0, x = var_1211_cast_fp16_2)[name = string("op_1217_cast_fp16")]; + tensor x_57_axes_0 = const()[name = string("x_57_axes_0"), val = tensor([-1])]; + tensor x_57_cast_fp16 = layer_norm(axes = x_57_axes_0, epsilon = var_70_to_fp16, x = input_223_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor var_1220_axes_0 = const()[name = string("op_1220_axes_0"), val = tensor([1])]; + tensor var_1220_cast_fp16 = expand_dims(axes = var_1220_axes_0, x = var_1211_cast_fp16_1)[name = string("op_1220_cast_fp16")]; + fp16 var_1221_to_fp16 = const()[name = string("op_1221_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1222_cast_fp16 = add(x = var_1220_cast_fp16, y = var_1221_to_fp16)[name = string("op_1222_cast_fp16")]; + tensor var_1223_cast_fp16 = mul(x = x_57_cast_fp16, y = var_1222_cast_fp16)[name = string("op_1223_cast_fp16")]; + tensor var_1224_axes_0 = const()[name = string("op_1224_axes_0"), val = tensor([1])]; + tensor var_1224_cast_fp16 = expand_dims(axes = var_1224_axes_0, x = var_1211_cast_fp16_0)[name = string("op_1224_cast_fp16")]; + tensor x_59_cast_fp16 = add(x = var_1223_cast_fp16, y = var_1224_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45792064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46578560))))[name = string("module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46581696)))]; + tensor linear_57_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_qkv_weight_to_fp16_quantized, x = x_59_cast_fp16)[name = string("linear_57_cast_fp16")]; + tensor var_1236 = const()[name = string("op_1236"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_19_cast_fp16 = reshape(shape = var_1236, x = linear_57_cast_fp16)[name = string("qkv_19_cast_fp16")]; + tensor var_1238_split_sizes_0 = const()[name = string("op_1238_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1238_axis_0 = const()[name = string("op_1238_axis_0"), val = int32(2)]; + tensor var_1238_cast_fp16_0, tensor var_1238_cast_fp16_1, tensor var_1238_cast_fp16_2 = split(axis = var_1238_axis_0, split_sizes = var_1238_split_sizes_0, x = qkv_19_cast_fp16)[name = string("op_1238_cast_fp16")]; + tensor squeeze_27_axes_0 = const()[name = string("squeeze_27_axes_0"), val = tensor([2])]; + tensor squeeze_27_cast_fp16 = squeeze(axes = squeeze_27_axes_0, x = var_1238_cast_fp16_0)[name = string("squeeze_27_cast_fp16")]; + tensor squeeze_28_axes_0 = const()[name = string("squeeze_28_axes_0"), val = tensor([2])]; + tensor squeeze_28_cast_fp16 = squeeze(axes = squeeze_28_axes_0, x = var_1238_cast_fp16_1)[name = string("squeeze_28_cast_fp16")]; + tensor squeeze_29_axes_0 = const()[name = string("squeeze_29_axes_0"), val = tensor([2])]; + tensor squeeze_29_cast_fp16 = squeeze(axes = squeeze_29_axes_0, x = var_1238_cast_fp16_2)[name = string("squeeze_29_cast_fp16")]; + tensor var_1243_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = squeeze_27_cast_fp16)[name = string("op_1243_cast_fp16")]; + tensor var_1245_axes_0 = const()[name = string("op_1245_axes_0"), val = tensor([-1])]; + bool var_1245_keep_dims_0 = const()[name = string("op_1245_keep_dims_0"), val = bool(true)]; + tensor var_1245_cast_fp16 = reduce_mean(axes = var_1245_axes_0, keep_dims = var_1245_keep_dims_0, x = var_1243_cast_fp16)[name = string("op_1245_cast_fp16")]; + fp16 var_1246_to_fp16 = const()[name = string("op_1246_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1247_cast_fp16 = add(x = var_1245_cast_fp16, y = var_1246_to_fp16)[name = string("op_1247_cast_fp16")]; + fp32 var_1248_epsilon_0 = const()[name = string("op_1248_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1248_cast_fp16 = rsqrt(epsilon = var_1248_epsilon_0, x = var_1247_cast_fp16)[name = string("op_1248_cast_fp16")]; + tensor var_1249_cast_fp16 = mul(x = squeeze_27_cast_fp16, y = var_1248_cast_fp16)[name = string("op_1249_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46584832)))]; + tensor var_1250_cast_fp16 = mul(x = var_1249_cast_fp16, y = module_model_encoder_module_blocks_9_attn_q_norm_weight_to_fp16)[name = string("op_1250_cast_fp16")]; + tensor var_1253_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = squeeze_28_cast_fp16)[name = string("op_1253_cast_fp16")]; + tensor var_1255_axes_0 = const()[name = string("op_1255_axes_0"), val = tensor([-1])]; + bool var_1255_keep_dims_0 = const()[name = string("op_1255_keep_dims_0"), val = bool(true)]; + tensor var_1255_cast_fp16 = reduce_mean(axes = var_1255_axes_0, keep_dims = var_1255_keep_dims_0, x = var_1253_cast_fp16)[name = string("op_1255_cast_fp16")]; + fp16 var_1256_to_fp16 = const()[name = string("op_1256_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1257_cast_fp16 = add(x = var_1255_cast_fp16, y = var_1256_to_fp16)[name = string("op_1257_cast_fp16")]; + fp32 var_1258_epsilon_0 = const()[name = string("op_1258_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1258_cast_fp16 = rsqrt(epsilon = var_1258_epsilon_0, x = var_1257_cast_fp16)[name = string("op_1258_cast_fp16")]; + tensor var_1259_cast_fp16 = mul(x = squeeze_28_cast_fp16, y = var_1258_cast_fp16)[name = string("op_1259_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585024)))]; + tensor var_1260_cast_fp16 = mul(x = var_1259_cast_fp16, y = module_model_encoder_module_blocks_9_attn_k_norm_weight_to_fp16)[name = string("op_1260_cast_fp16")]; + tensor transpose_99_perm_0 = const()[name = string("transpose_99_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_100_perm_0 = const()[name = string("transpose_100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101_perm_0 = const()[name = string("transpose_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_101 = transpose(perm = transpose_101_perm_0, x = squeeze_29_cast_fp16)[name = string("transpose_118")]; + tensor transpose_100 = transpose(perm = transpose_100_perm_0, x = var_1260_cast_fp16)[name = string("transpose_119")]; + tensor transpose_99 = transpose(perm = transpose_99_perm_0, x = var_1250_cast_fp16)[name = string("transpose_120")]; + tensor out_19_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_100, query = transpose_99, value = transpose_101)[name = string("out_19_cast_fp16")]; + tensor var_1271_perm_0 = const()[name = string("op_1271_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1272 = const()[name = string("op_1272"), val = tensor([1, 864, 512])]; + tensor var_1271_cast_fp16 = transpose(perm = var_1271_perm_0, x = out_19_cast_fp16)[name = string("transpose_117")]; + tensor input_225_cast_fp16 = reshape(shape = var_1272, x = var_1271_cast_fp16)[name = string("input_225_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46585216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46847424))))[name = string("module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46848512)))]; + tensor linear_58_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_9_attn_proj_weight_to_fp16_quantized, x = input_225_cast_fp16)[name = string("linear_58_cast_fp16")]; + tensor var_1278_cast_fp16 = mul(x = var_1217_cast_fp16, y = linear_58_cast_fp16)[name = string("op_1278_cast_fp16")]; + tensor input_229_cast_fp16 = add(x = input_223_cast_fp16, y = var_1278_cast_fp16)[name = string("input_229_cast_fp16")]; + tensor var_1280_axes_0 = const()[name = string("op_1280_axes_0"), val = tensor([1])]; + tensor var_1280_cast_fp16 = expand_dims(axes = var_1280_axes_0, x = var_1211_cast_fp16_5)[name = string("op_1280_cast_fp16")]; + tensor x_61_axes_0 = const()[name = string("x_61_axes_0"), val = tensor([-1])]; + tensor x_61_cast_fp16 = layer_norm(axes = x_61_axes_0, epsilon = var_70_to_fp16, x = input_229_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor var_1283_axes_0 = const()[name = string("op_1283_axes_0"), val = tensor([1])]; + tensor var_1283_cast_fp16 = expand_dims(axes = var_1283_axes_0, x = var_1211_cast_fp16_4)[name = string("op_1283_cast_fp16")]; + fp16 var_1284_to_fp16 = const()[name = string("op_1284_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1285_cast_fp16 = add(x = var_1283_cast_fp16, y = var_1284_to_fp16)[name = string("op_1285_cast_fp16")]; + tensor var_1286_cast_fp16 = mul(x = x_61_cast_fp16, y = var_1285_cast_fp16)[name = string("op_1286_cast_fp16")]; + tensor var_1287_axes_0 = const()[name = string("op_1287_axes_0"), val = tensor([1])]; + tensor var_1287_cast_fp16 = expand_dims(axes = var_1287_axes_0, x = var_1211_cast_fp16_3)[name = string("op_1287_cast_fp16")]; + tensor input_231_cast_fp16 = add(x = var_1286_cast_fp16, y = var_1287_cast_fp16)[name = string("input_231_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46849600))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47898240))))[name = string("module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47902400)))]; + tensor linear_59_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_0_weight_to_fp16_quantized, x = input_231_cast_fp16)[name = string("linear_59_cast_fp16")]; + string input_235_mode_0 = const()[name = string("input_235_mode_0"), val = string("EXACT")]; + tensor input_235_cast_fp16 = gelu(mode = input_235_mode_0, x = linear_59_cast_fp16)[name = string("input_235_cast_fp16")]; + tensor module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47906560))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48955200))))[name = string("module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48956288)))]; + tensor linear_60_cast_fp16 = linear(bias = module_model_encoder_module_blocks_9_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_9_ffn_3_weight_to_fp16_quantized, x = input_235_cast_fp16)[name = string("linear_60_cast_fp16")]; + tensor var_1300_cast_fp16 = mul(x = var_1280_cast_fp16, y = linear_60_cast_fp16)[name = string("op_1300_cast_fp16")]; + tensor input_243_cast_fp16 = add(x = input_229_cast_fp16, y = var_1300_cast_fp16)[name = string("input_243_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48957376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50530304))))[name = string("module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50536512)))]; + tensor linear_61_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_61_cast_fp16")]; + tensor var_1309_split_sizes_0 = const()[name = string("op_1309_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1309_axis_0 = const()[name = string("op_1309_axis_0"), val = int32(-1)]; + tensor var_1309_cast_fp16_0, tensor var_1309_cast_fp16_1, tensor var_1309_cast_fp16_2, tensor var_1309_cast_fp16_3, tensor var_1309_cast_fp16_4, tensor var_1309_cast_fp16_5 = split(axis = var_1309_axis_0, split_sizes = var_1309_split_sizes_0, x = linear_61_cast_fp16)[name = string("op_1309_cast_fp16")]; + tensor var_1315_axes_0 = const()[name = string("op_1315_axes_0"), val = tensor([1])]; + tensor var_1315_cast_fp16 = expand_dims(axes = var_1315_axes_0, x = var_1309_cast_fp16_2)[name = string("op_1315_cast_fp16")]; + tensor x_63_axes_0 = const()[name = string("x_63_axes_0"), val = tensor([-1])]; + tensor x_63_cast_fp16 = layer_norm(axes = x_63_axes_0, epsilon = var_70_to_fp16, x = input_243_cast_fp16)[name = string("x_63_cast_fp16")]; + tensor var_1318_axes_0 = const()[name = string("op_1318_axes_0"), val = tensor([1])]; + tensor var_1318_cast_fp16 = expand_dims(axes = var_1318_axes_0, x = var_1309_cast_fp16_1)[name = string("op_1318_cast_fp16")]; + fp16 var_1319_to_fp16 = const()[name = string("op_1319_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1320_cast_fp16 = add(x = var_1318_cast_fp16, y = var_1319_to_fp16)[name = string("op_1320_cast_fp16")]; + tensor var_1321_cast_fp16 = mul(x = x_63_cast_fp16, y = var_1320_cast_fp16)[name = string("op_1321_cast_fp16")]; + tensor var_1322_axes_0 = const()[name = string("op_1322_axes_0"), val = tensor([1])]; + tensor var_1322_cast_fp16 = expand_dims(axes = var_1322_axes_0, x = var_1309_cast_fp16_0)[name = string("op_1322_cast_fp16")]; + tensor x_65_cast_fp16 = add(x = var_1321_cast_fp16, y = var_1322_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(50542720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51329216))))[name = string("module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51332352)))]; + tensor linear_62_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_qkv_weight_to_fp16_quantized, x = x_65_cast_fp16)[name = string("linear_62_cast_fp16")]; + tensor var_1334 = const()[name = string("op_1334"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_21_cast_fp16 = reshape(shape = var_1334, x = linear_62_cast_fp16)[name = string("qkv_21_cast_fp16")]; + tensor var_1336_split_sizes_0 = const()[name = string("op_1336_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1336_axis_0 = const()[name = string("op_1336_axis_0"), val = int32(2)]; + tensor var_1336_cast_fp16_0, tensor var_1336_cast_fp16_1, tensor var_1336_cast_fp16_2 = split(axis = var_1336_axis_0, split_sizes = var_1336_split_sizes_0, x = qkv_21_cast_fp16)[name = string("op_1336_cast_fp16")]; + tensor squeeze_30_axes_0 = const()[name = string("squeeze_30_axes_0"), val = tensor([2])]; + tensor squeeze_30_cast_fp16 = squeeze(axes = squeeze_30_axes_0, x = var_1336_cast_fp16_0)[name = string("squeeze_30_cast_fp16")]; + tensor squeeze_31_axes_0 = const()[name = string("squeeze_31_axes_0"), val = tensor([2])]; + tensor squeeze_31_cast_fp16 = squeeze(axes = squeeze_31_axes_0, x = var_1336_cast_fp16_1)[name = string("squeeze_31_cast_fp16")]; + tensor squeeze_32_axes_0 = const()[name = string("squeeze_32_axes_0"), val = tensor([2])]; + tensor squeeze_32_cast_fp16 = squeeze(axes = squeeze_32_axes_0, x = var_1336_cast_fp16_2)[name = string("squeeze_32_cast_fp16")]; + tensor var_1341_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = squeeze_30_cast_fp16)[name = string("op_1341_cast_fp16")]; + tensor var_1343_axes_0 = const()[name = string("op_1343_axes_0"), val = tensor([-1])]; + bool var_1343_keep_dims_0 = const()[name = string("op_1343_keep_dims_0"), val = bool(true)]; + tensor var_1343_cast_fp16 = reduce_mean(axes = var_1343_axes_0, keep_dims = var_1343_keep_dims_0, x = var_1341_cast_fp16)[name = string("op_1343_cast_fp16")]; + fp16 var_1344_to_fp16 = const()[name = string("op_1344_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1345_cast_fp16 = add(x = var_1343_cast_fp16, y = var_1344_to_fp16)[name = string("op_1345_cast_fp16")]; + fp32 var_1346_epsilon_0 = const()[name = string("op_1346_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1346_cast_fp16 = rsqrt(epsilon = var_1346_epsilon_0, x = var_1345_cast_fp16)[name = string("op_1346_cast_fp16")]; + tensor var_1347_cast_fp16 = mul(x = squeeze_30_cast_fp16, y = var_1346_cast_fp16)[name = string("op_1347_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335488)))]; + tensor var_1348_cast_fp16 = mul(x = var_1347_cast_fp16, y = module_model_encoder_module_blocks_10_attn_q_norm_weight_to_fp16)[name = string("op_1348_cast_fp16")]; + tensor var_1351_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = squeeze_31_cast_fp16)[name = string("op_1351_cast_fp16")]; + tensor var_1353_axes_0 = const()[name = string("op_1353_axes_0"), val = tensor([-1])]; + bool var_1353_keep_dims_0 = const()[name = string("op_1353_keep_dims_0"), val = bool(true)]; + tensor var_1353_cast_fp16 = reduce_mean(axes = var_1353_axes_0, keep_dims = var_1353_keep_dims_0, x = var_1351_cast_fp16)[name = string("op_1353_cast_fp16")]; + fp16 var_1354_to_fp16 = const()[name = string("op_1354_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1355_cast_fp16 = add(x = var_1353_cast_fp16, y = var_1354_to_fp16)[name = string("op_1355_cast_fp16")]; + fp32 var_1356_epsilon_0 = const()[name = string("op_1356_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1356_cast_fp16 = rsqrt(epsilon = var_1356_epsilon_0, x = var_1355_cast_fp16)[name = string("op_1356_cast_fp16")]; + tensor var_1357_cast_fp16 = mul(x = squeeze_31_cast_fp16, y = var_1356_cast_fp16)[name = string("op_1357_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335680)))]; + tensor var_1358_cast_fp16 = mul(x = var_1357_cast_fp16, y = module_model_encoder_module_blocks_10_attn_k_norm_weight_to_fp16)[name = string("op_1358_cast_fp16")]; + tensor transpose_102_perm_0 = const()[name = string("transpose_102_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_103_perm_0 = const()[name = string("transpose_103_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104_perm_0 = const()[name = string("transpose_104_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_104 = transpose(perm = transpose_104_perm_0, x = squeeze_32_cast_fp16)[name = string("transpose_114")]; + tensor transpose_103 = transpose(perm = transpose_103_perm_0, x = var_1358_cast_fp16)[name = string("transpose_115")]; + tensor transpose_102 = transpose(perm = transpose_102_perm_0, x = var_1348_cast_fp16)[name = string("transpose_116")]; + tensor out_21_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_103, query = transpose_102, value = transpose_104)[name = string("out_21_cast_fp16")]; + tensor var_1369_perm_0 = const()[name = string("op_1369_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1370 = const()[name = string("op_1370"), val = tensor([1, 864, 512])]; + tensor var_1369_cast_fp16 = transpose(perm = var_1369_perm_0, x = out_21_cast_fp16)[name = string("transpose_113")]; + tensor input_245_cast_fp16 = reshape(shape = var_1370, x = var_1369_cast_fp16)[name = string("input_245_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51335872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51598080))))[name = string("module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51599168)))]; + tensor linear_63_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_10_attn_proj_weight_to_fp16_quantized, x = input_245_cast_fp16)[name = string("linear_63_cast_fp16")]; + tensor var_1376_cast_fp16 = mul(x = var_1315_cast_fp16, y = linear_63_cast_fp16)[name = string("op_1376_cast_fp16")]; + tensor input_249_cast_fp16 = add(x = input_243_cast_fp16, y = var_1376_cast_fp16)[name = string("input_249_cast_fp16")]; + tensor var_1378_axes_0 = const()[name = string("op_1378_axes_0"), val = tensor([1])]; + tensor var_1378_cast_fp16 = expand_dims(axes = var_1378_axes_0, x = var_1309_cast_fp16_5)[name = string("op_1378_cast_fp16")]; + tensor x_67_axes_0 = const()[name = string("x_67_axes_0"), val = tensor([-1])]; + tensor x_67_cast_fp16 = layer_norm(axes = x_67_axes_0, epsilon = var_70_to_fp16, x = input_249_cast_fp16)[name = string("x_67_cast_fp16")]; + tensor var_1381_axes_0 = const()[name = string("op_1381_axes_0"), val = tensor([1])]; + tensor var_1381_cast_fp16 = expand_dims(axes = var_1381_axes_0, x = var_1309_cast_fp16_4)[name = string("op_1381_cast_fp16")]; + fp16 var_1382_to_fp16 = const()[name = string("op_1382_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1383_cast_fp16 = add(x = var_1381_cast_fp16, y = var_1382_to_fp16)[name = string("op_1383_cast_fp16")]; + tensor var_1384_cast_fp16 = mul(x = x_67_cast_fp16, y = var_1383_cast_fp16)[name = string("op_1384_cast_fp16")]; + tensor var_1385_axes_0 = const()[name = string("op_1385_axes_0"), val = tensor([1])]; + tensor var_1385_cast_fp16 = expand_dims(axes = var_1385_axes_0, x = var_1309_cast_fp16_3)[name = string("op_1385_cast_fp16")]; + tensor input_251_cast_fp16 = add(x = var_1384_cast_fp16, y = var_1385_cast_fp16)[name = string("input_251_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51600256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52648896))))[name = string("module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52653056)))]; + tensor linear_64_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_0_weight_to_fp16_quantized, x = input_251_cast_fp16)[name = string("linear_64_cast_fp16")]; + string input_255_mode_0 = const()[name = string("input_255_mode_0"), val = string("EXACT")]; + tensor input_255_cast_fp16 = gelu(mode = input_255_mode_0, x = linear_64_cast_fp16)[name = string("input_255_cast_fp16")]; + tensor module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(52657216))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53705856))))[name = string("module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53706944)))]; + tensor linear_65_cast_fp16 = linear(bias = module_model_encoder_module_blocks_10_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_10_ffn_3_weight_to_fp16_quantized, x = input_255_cast_fp16)[name = string("linear_65_cast_fp16")]; + tensor var_1398_cast_fp16 = mul(x = var_1378_cast_fp16, y = linear_65_cast_fp16)[name = string("op_1398_cast_fp16")]; + tensor input_263_cast_fp16 = add(x = input_249_cast_fp16, y = var_1398_cast_fp16)[name = string("input_263_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(53708032))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55280960))))[name = string("module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ada_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ada_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55287168)))]; + tensor linear_66_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ada_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ada_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_66_cast_fp16")]; + tensor var_1407_split_sizes_0 = const()[name = string("op_1407_split_sizes_0"), val = tensor([512, 512, 512, 512, 512, 512])]; + int32 var_1407_axis_0 = const()[name = string("op_1407_axis_0"), val = int32(-1)]; + tensor var_1407_cast_fp16_0, tensor var_1407_cast_fp16_1, tensor var_1407_cast_fp16_2, tensor var_1407_cast_fp16_3, tensor var_1407_cast_fp16_4, tensor var_1407_cast_fp16_5 = split(axis = var_1407_axis_0, split_sizes = var_1407_split_sizes_0, x = linear_66_cast_fp16)[name = string("op_1407_cast_fp16")]; + tensor var_1413_axes_0 = const()[name = string("op_1413_axes_0"), val = tensor([1])]; + tensor var_1413_cast_fp16 = expand_dims(axes = var_1413_axes_0, x = var_1407_cast_fp16_2)[name = string("op_1413_cast_fp16")]; + tensor x_69_axes_0 = const()[name = string("x_69_axes_0"), val = tensor([-1])]; + tensor x_69_cast_fp16 = layer_norm(axes = x_69_axes_0, epsilon = var_70_to_fp16, x = input_263_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor var_1416_axes_0 = const()[name = string("op_1416_axes_0"), val = tensor([1])]; + tensor var_1416_cast_fp16 = expand_dims(axes = var_1416_axes_0, x = var_1407_cast_fp16_1)[name = string("op_1416_cast_fp16")]; + fp16 var_1417_to_fp16 = const()[name = string("op_1417_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1418_cast_fp16 = add(x = var_1416_cast_fp16, y = var_1417_to_fp16)[name = string("op_1418_cast_fp16")]; + tensor var_1419_cast_fp16 = mul(x = x_69_cast_fp16, y = var_1418_cast_fp16)[name = string("op_1419_cast_fp16")]; + tensor var_1420_axes_0 = const()[name = string("op_1420_axes_0"), val = tensor([1])]; + tensor var_1420_cast_fp16 = expand_dims(axes = var_1420_axes_0, x = var_1407_cast_fp16_0)[name = string("op_1420_cast_fp16")]; + tensor x_71_cast_fp16 = add(x = var_1419_cast_fp16, y = var_1420_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(55293376))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56079872))))[name = string("module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56083008)))]; + tensor linear_67_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_qkv_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_qkv_weight_to_fp16_quantized, x = x_71_cast_fp16)[name = string("linear_67_cast_fp16")]; + tensor var_1432 = const()[name = string("op_1432"), val = tensor([1, 864, 3, 8, 64])]; + tensor qkv_23_cast_fp16 = reshape(shape = var_1432, x = linear_67_cast_fp16)[name = string("qkv_23_cast_fp16")]; + tensor var_1434_split_sizes_0 = const()[name = string("op_1434_split_sizes_0"), val = tensor([1, 1, 1])]; + int32 var_1434_axis_0 = const()[name = string("op_1434_axis_0"), val = int32(2)]; + tensor var_1434_cast_fp16_0, tensor var_1434_cast_fp16_1, tensor var_1434_cast_fp16_2 = split(axis = var_1434_axis_0, split_sizes = var_1434_split_sizes_0, x = qkv_23_cast_fp16)[name = string("op_1434_cast_fp16")]; + tensor squeeze_33_axes_0 = const()[name = string("squeeze_33_axes_0"), val = tensor([2])]; + tensor squeeze_33_cast_fp16 = squeeze(axes = squeeze_33_axes_0, x = var_1434_cast_fp16_0)[name = string("squeeze_33_cast_fp16")]; + tensor squeeze_34_axes_0 = const()[name = string("squeeze_34_axes_0"), val = tensor([2])]; + tensor squeeze_34_cast_fp16 = squeeze(axes = squeeze_34_axes_0, x = var_1434_cast_fp16_1)[name = string("squeeze_34_cast_fp16")]; + tensor squeeze_35_axes_0 = const()[name = string("squeeze_35_axes_0"), val = tensor([2])]; + tensor squeeze_35_cast_fp16 = squeeze(axes = squeeze_35_axes_0, x = var_1434_cast_fp16_2)[name = string("squeeze_35_cast_fp16")]; + tensor var_1439_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = squeeze_33_cast_fp16)[name = string("op_1439_cast_fp16")]; + tensor var_1441_axes_0 = const()[name = string("op_1441_axes_0"), val = tensor([-1])]; + bool var_1441_keep_dims_0 = const()[name = string("op_1441_keep_dims_0"), val = bool(true)]; + tensor var_1441_cast_fp16 = reduce_mean(axes = var_1441_axes_0, keep_dims = var_1441_keep_dims_0, x = var_1439_cast_fp16)[name = string("op_1441_cast_fp16")]; + fp16 var_1442_to_fp16 = const()[name = string("op_1442_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1443_cast_fp16 = add(x = var_1441_cast_fp16, y = var_1442_to_fp16)[name = string("op_1443_cast_fp16")]; + fp32 var_1444_epsilon_0 = const()[name = string("op_1444_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1444_cast_fp16 = rsqrt(epsilon = var_1444_epsilon_0, x = var_1443_cast_fp16)[name = string("op_1444_cast_fp16")]; + tensor var_1445_cast_fp16 = mul(x = squeeze_33_cast_fp16, y = var_1444_cast_fp16)[name = string("op_1445_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086144)))]; + tensor var_1446_cast_fp16 = mul(x = var_1445_cast_fp16, y = module_model_encoder_module_blocks_11_attn_q_norm_weight_to_fp16)[name = string("op_1446_cast_fp16")]; + tensor var_1449_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = squeeze_34_cast_fp16)[name = string("op_1449_cast_fp16")]; + tensor var_1451_axes_0 = const()[name = string("op_1451_axes_0"), val = tensor([-1])]; + bool var_1451_keep_dims_0 = const()[name = string("op_1451_keep_dims_0"), val = bool(true)]; + tensor var_1451_cast_fp16 = reduce_mean(axes = var_1451_axes_0, keep_dims = var_1451_keep_dims_0, x = var_1449_cast_fp16)[name = string("op_1451_cast_fp16")]; + fp16 var_1452_to_fp16 = const()[name = string("op_1452_to_fp16"), val = fp16(0x1p-23)]; + tensor var_1453_cast_fp16 = add(x = var_1451_cast_fp16, y = var_1452_to_fp16)[name = string("op_1453_cast_fp16")]; + fp32 var_1454_epsilon_0 = const()[name = string("op_1454_epsilon_0"), val = fp32(0x1.197998p-40)]; + tensor var_1454_cast_fp16 = rsqrt(epsilon = var_1454_epsilon_0, x = var_1453_cast_fp16)[name = string("op_1454_cast_fp16")]; + tensor var_1455_cast_fp16 = mul(x = squeeze_34_cast_fp16, y = var_1454_cast_fp16)[name = string("op_1455_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086336)))]; + tensor var_1456_cast_fp16 = mul(x = var_1455_cast_fp16, y = module_model_encoder_module_blocks_11_attn_k_norm_weight_to_fp16)[name = string("op_1456_cast_fp16")]; + tensor transpose_105_perm_0 = const()[name = string("transpose_105_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_106_perm_0 = const()[name = string("transpose_106_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107_perm_0 = const()[name = string("transpose_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_107 = transpose(perm = transpose_107_perm_0, x = squeeze_35_cast_fp16)[name = string("transpose_110")]; + tensor transpose_106 = transpose(perm = transpose_106_perm_0, x = var_1456_cast_fp16)[name = string("transpose_111")]; + tensor transpose_105 = transpose(perm = transpose_105_perm_0, x = var_1446_cast_fp16)[name = string("transpose_112")]; + tensor out_23_cast_fp16 = scaled_dot_product_attention(attn_mask = attn_mask_1_cast_fp16, key = transpose_106, query = transpose_105, value = transpose_107)[name = string("out_23_cast_fp16")]; + tensor var_1467_perm_0 = const()[name = string("op_1467_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1468 = const()[name = string("op_1468"), val = tensor([1, 864, 512])]; + tensor var_1467_cast_fp16 = transpose(perm = var_1467_perm_0, x = out_23_cast_fp16)[name = string("transpose_109")]; + tensor input_265_cast_fp16 = reshape(shape = var_1468, x = var_1467_cast_fp16)[name = string("input_265_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56086528))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56348736))))[name = string("module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56349824)))]; + tensor linear_68_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_attn_proj_bias_to_fp16, weight = module_model_encoder_module_blocks_11_attn_proj_weight_to_fp16_quantized, x = input_265_cast_fp16)[name = string("linear_68_cast_fp16")]; + tensor var_1474_cast_fp16 = mul(x = var_1413_cast_fp16, y = linear_68_cast_fp16)[name = string("op_1474_cast_fp16")]; + tensor input_269_cast_fp16 = add(x = input_263_cast_fp16, y = var_1474_cast_fp16)[name = string("input_269_cast_fp16")]; + tensor var_1476_axes_0 = const()[name = string("op_1476_axes_0"), val = tensor([1])]; + tensor var_1476_cast_fp16 = expand_dims(axes = var_1476_axes_0, x = var_1407_cast_fp16_5)[name = string("op_1476_cast_fp16")]; + tensor x_73_axes_0 = const()[name = string("x_73_axes_0"), val = tensor([-1])]; + tensor x_73_cast_fp16 = layer_norm(axes = x_73_axes_0, epsilon = var_70_to_fp16, x = input_269_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor var_1479_axes_0 = const()[name = string("op_1479_axes_0"), val = tensor([1])]; + tensor var_1479_cast_fp16 = expand_dims(axes = var_1479_axes_0, x = var_1407_cast_fp16_4)[name = string("op_1479_cast_fp16")]; + fp16 var_1480_to_fp16 = const()[name = string("op_1480_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1481_cast_fp16 = add(x = var_1479_cast_fp16, y = var_1480_to_fp16)[name = string("op_1481_cast_fp16")]; + tensor var_1482_cast_fp16 = mul(x = x_73_cast_fp16, y = var_1481_cast_fp16)[name = string("op_1482_cast_fp16")]; + tensor var_1483_axes_0 = const()[name = string("op_1483_axes_0"), val = tensor([1])]; + tensor var_1483_cast_fp16 = expand_dims(axes = var_1483_axes_0, x = var_1407_cast_fp16_3)[name = string("op_1483_cast_fp16")]; + tensor input_271_cast_fp16 = add(x = var_1482_cast_fp16, y = var_1483_cast_fp16)[name = string("input_271_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56350912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57399552))))[name = string("module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57403712)))]; + tensor linear_69_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_0_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_0_weight_to_fp16_quantized, x = input_271_cast_fp16)[name = string("linear_69_cast_fp16")]; + string input_275_mode_0 = const()[name = string("input_275_mode_0"), val = string("EXACT")]; + tensor input_275_cast_fp16 = gelu(mode = input_275_mode_0, x = linear_69_cast_fp16)[name = string("input_275_cast_fp16")]; + tensor module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57407872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58456512))))[name = string("module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16 = const()[name = string("module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58457600)))]; + tensor linear_70_cast_fp16 = linear(bias = module_model_encoder_module_blocks_11_ffn_3_bias_to_fp16, weight = module_model_encoder_module_blocks_11_ffn_3_weight_to_fp16_quantized, x = input_275_cast_fp16)[name = string("linear_70_cast_fp16")]; + tensor var_1496_cast_fp16 = mul(x = var_1476_cast_fp16, y = linear_70_cast_fp16)[name = string("op_1496_cast_fp16")]; + tensor input_283_cast_fp16 = add(x = input_269_cast_fp16, y = var_1496_cast_fp16)[name = string("input_283_cast_fp16")]; + tensor module_model_encoder_module_ada_out_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58458688))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58983040))))[name = string("module_model_encoder_module_ada_out_weight_to_fp16_quantized")]; + tensor module_model_encoder_module_ada_out_bias_to_fp16 = const()[name = string("module_model_encoder_module_ada_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58985152)))]; + tensor linear_71_cast_fp16 = linear(bias = module_model_encoder_module_ada_out_bias_to_fp16, weight = module_model_encoder_module_ada_out_weight_to_fp16_quantized, x = input_41_cast_fp16)[name = string("linear_71_cast_fp16")]; + tensor var_1502_split_sizes_0 = const()[name = string("op_1502_split_sizes_0"), val = tensor([512, 512])]; + int32 var_1502_axis_0 = const()[name = string("op_1502_axis_0"), val = int32(-1)]; + tensor var_1502_cast_fp16_0, tensor var_1502_cast_fp16_1 = split(axis = var_1502_axis_0, split_sizes = var_1502_split_sizes_0, x = linear_71_cast_fp16)[name = string("op_1502_cast_fp16")]; + tensor x_axes_0 = const()[name = string("x_axes_0"), val = tensor([-1])]; + tensor x_cast_fp16 = layer_norm(axes = x_axes_0, epsilon = var_70_to_fp16, x = input_283_cast_fp16)[name = string("x_cast_fp16")]; + tensor var_1506_axes_0 = const()[name = string("op_1506_axes_0"), val = tensor([1])]; + tensor var_1506_cast_fp16 = expand_dims(axes = var_1506_axes_0, x = var_1502_cast_fp16_1)[name = string("op_1506_cast_fp16")]; + fp16 var_1507_to_fp16 = const()[name = string("op_1507_to_fp16"), val = fp16(0x1p+0)]; + tensor var_1508_cast_fp16 = add(x = var_1506_cast_fp16, y = var_1507_to_fp16)[name = string("op_1508_cast_fp16")]; + tensor var_1509_cast_fp16 = mul(x = x_cast_fp16, y = var_1508_cast_fp16)[name = string("op_1509_cast_fp16")]; + tensor var_1510_axes_0 = const()[name = string("op_1510_axes_0"), val = tensor([1])]; + tensor var_1510_cast_fp16 = expand_dims(axes = var_1510_axes_0, x = var_1502_cast_fp16_0)[name = string("op_1510_cast_fp16")]; + tensor encoded_cast_fp16 = add(x = var_1509_cast_fp16, y = var_1510_cast_fp16)[name = string("encoded_cast_fp16")]; + tensor input_285_begin_0 = const()[name = string("input_285_begin_0"), val = tensor([0, 96, 0])]; + tensor input_285_end_0 = const()[name = string("input_285_end_0"), val = tensor([1, 864, 512])]; + tensor input_285_end_mask_0 = const()[name = string("input_285_end_mask_0"), val = tensor([true, true, true])]; + tensor input_285_cast_fp16 = slice_by_index(begin = input_285_begin_0, end = input_285_end_0, end_mask = input_285_end_mask_0, x = encoded_cast_fp16)[name = string("input_285_cast_fp16")]; + tensor module_model_out_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58987264))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59249472))))[name = string("module_model_out_0_weight_to_fp16_quantized")]; + tensor module_model_out_0_bias_to_fp16 = const()[name = string("module_model_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59250560)))]; + tensor linear_72_cast_fp16 = linear(bias = module_model_out_0_bias_to_fp16, weight = module_model_out_0_weight_to_fp16_quantized, x = input_285_cast_fp16)[name = string("linear_72_cast_fp16")]; + string input_289_mode_0 = const()[name = string("input_289_mode_0"), val = string("EXACT")]; + tensor input_289_cast_fp16 = gelu(mode = input_289_mode_0, x = linear_72_cast_fp16)[name = string("input_289_cast_fp16")]; + tensor module_model_out_3_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59251648))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264000))))[name = string("module_model_out_3_weight_to_fp16_quantized")]; + tensor module_model_out_3_bias_to_fp16 = const()[name = string("module_model_out_3_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(59264128)))]; + tensor linear_73_cast_fp16 = linear(bias = module_model_out_3_bias_to_fp16, weight = module_model_out_3_weight_to_fp16_quantized, x = input_289_cast_fp16)[name = string("linear_73_cast_fp16")]; + tensor var_1524_perm_0 = const()[name = string("op_1524_perm_0"), val = tensor([0, 2, 1])]; + string var_1524_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_1524_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor var_1524_cast_fp16 = transpose(perm = var_1524_perm_0, x = linear_73_cast_fp16)[name = string("transpose_108")]; + tensor velocity = cast(dtype = var_1524_cast_fp16_to_fp32_dtype_0, x = var_1524_cast_fp16)[name = string("cast_1")]; + } -> (velocity); +} \ No newline at end of file diff --git a/coreml/coreml/vector_estimator.mlmodelc/weights/weight.bin b/coreml/coreml/vector_estimator.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..272749d94f559d39f84508d53f308f5131f00dda --- /dev/null +++ b/coreml/coreml/vector_estimator.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b808c10f9838b2bbf671ccef3d765cebabf58243457a01c06ec11480a9392388 +size 60779072 diff --git a/coreml/coreml/vocoder.mlmodelc/analytics/coremldata.bin b/coreml/coreml/vocoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0ae18f4ba9389d42de1a7b2bf9e5f3a78a72baa1 --- /dev/null +++ b/coreml/coreml/vocoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cfb1b21d4bfb610ff2e628803f2dc948f6265d1baddc2171c088e148da1a3117 +size 243 diff --git a/coreml/coreml/vocoder.mlmodelc/coremldata.bin b/coreml/coreml/vocoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..9c2913a225edcb7b090fb3eb1d0222641e6b3755 --- /dev/null +++ b/coreml/coreml/vocoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:04bf9f9de7edfc6ce58e5928a0a1d6669d09095ebc03cb5ca24dd85a416653df +size 1190 diff --git a/coreml/coreml/vocoder.mlmodelc/model.mil b/coreml/coreml/vocoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..39ad01dbccc9e8b3b9310af29a398b77e2a3d03e --- /dev/null +++ b/coreml/coreml/vocoder.mlmodelc/model.mil @@ -0,0 +1,5386 @@ +program(1.3) +[buildInfo = dict({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}})] +{ + func vocoder(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + int32 var_41 = const()[name = string("op_41"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_85 = maximum(x = emotion_id, y = var_39)[name = string("op_85")]; + tensor input_5 = minimum(x = var_85, y = var_41)[name = string("input_5")]; + int32 var_88_batch_dims_0 = const()[name = string("op_88_batch_dims_0"), val = int32(0)]; + bool var_88_validate_indices_0 = const()[name = string("op_88_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_88_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_88_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_88_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_88_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_88_batch_dims_0, indices = select_0_1, validate_indices = var_88_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_88_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_90_axis_0 = const()[name = string("op_90_axis_0"), val = int32(0)]; + int32 var_90_batch_dims_0 = const()[name = string("op_90_batch_dims_0"), val = int32(0)]; + bool var_90_validate_indices_0 = const()[name = string("op_90_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_90_cast_fp16_cast_uint16 = gather(axis = var_90_axis_0, batch_dims = var_90_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_90_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_90_cast_fp16_cast_uint16")]; + tensor var_91_cast_fp16 = add(x = op_88_cast_fp16_cast_uint16_cast_uint16, y = var_90_cast_fp16_cast_uint16)[name = string("op_91_cast_fp16")]; + int32 var_93_axis_0 = const()[name = string("op_93_axis_0"), val = int32(0)]; + int32 var_93_batch_dims_0 = const()[name = string("op_93_batch_dims_0"), val = int32(0)]; + bool var_93_validate_indices_0 = const()[name = string("op_93_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_93_cast_fp16_cast_uint16 = gather(axis = var_93_axis_0, batch_dims = var_93_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_93_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_93_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_91_cast_fp16, y = var_93_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_95_axes_0 = const()[name = string("op_95_axes_0"), val = tensor([-1])]; + tensor var_95_cast_fp16 = expand_dims(axes = var_95_axes_0, x = cond_cast_fp16)[name = string("op_95_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_95_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_98_axes_0 = const()[name = string("op_98_axes_0"), val = tensor([1])]; + tensor var_98 = expand_dims(axes = var_98_axes_0, x = latent_mask_1)[name = string("op_98")]; + string var_99_to_fp16_dtype_0 = const()[name = string("op_99_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_98_to_fp16 = cast(dtype = var_99_to_fp16_dtype_0, x = var_98)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_98_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_98_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_48_to_fp16 = const()[name = string("op_48_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_131_axes_0 = const()[name = string("op_131_axes_0"), val = tensor([-1])]; + tensor var_131_cast_fp16 = expand_dims(axes = var_131_axes_0, x = linear_0_cast_fp16)[name = string("op_131_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_131_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_98_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_axes_0 = const()[name = string("op_155_axes_0"), val = tensor([-1])]; + tensor var_155_cast_fp16 = expand_dims(axes = var_155_axes_0, x = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_155_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_98_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_98_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_98_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_200_axes_0 = const()[name = string("op_200_axes_0"), val = tensor([-1])]; + tensor var_200_cast_fp16 = expand_dims(axes = var_200_axes_0, x = linear_2_cast_fp16)[name = string("op_200_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_200_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_98_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_224_axes_0 = const()[name = string("op_224_axes_0"), val = tensor([-1])]; + tensor var_224_cast_fp16 = expand_dims(axes = var_224_axes_0, x = linear_3_cast_fp16)[name = string("op_224_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_224_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_98_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_98_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_98_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_269_axes_0 = const()[name = string("op_269_axes_0"), val = tensor([-1])]; + tensor var_269_cast_fp16 = expand_dims(axes = var_269_axes_0, x = linear_4_cast_fp16)[name = string("op_269_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_269_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_98_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_293_axes_0 = const()[name = string("op_293_axes_0"), val = tensor([-1])]; + tensor var_293_cast_fp16 = expand_dims(axes = var_293_axes_0, x = linear_5_cast_fp16)[name = string("op_293_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_293_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_98_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_98_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_98_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([-1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = linear_6_cast_fp16)[name = string("op_338_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_338_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_98_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([-1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = linear_7_cast_fp16)[name = string("op_362_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_362_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_98_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_98_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_98_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_407_axes_0 = const()[name = string("op_407_axes_0"), val = tensor([-1])]; + tensor var_407_cast_fp16 = expand_dims(axes = var_407_axes_0, x = linear_8_cast_fp16)[name = string("op_407_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_407_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_98_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_431_axes_0 = const()[name = string("op_431_axes_0"), val = tensor([-1])]; + tensor var_431_cast_fp16 = expand_dims(axes = var_431_axes_0, x = linear_9_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_431_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_98_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_98_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_98_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + tensor var_476_cast_fp16 = expand_dims(axes = var_476_axes_0, x = linear_10_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_476_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_98_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_500_axes_0 = const()[name = string("op_500_axes_0"), val = tensor([-1])]; + tensor var_500_cast_fp16 = expand_dims(axes = var_500_axes_0, x = linear_11_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_500_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_98_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_98_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 2048])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 1024, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_524 = expand_dims(axes = var_524_axes_0, x = mask_53)[name = string("op_524")]; + string var_525_to_fp16_dtype_0 = const()[name = string("op_525_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_524_to_fp16 = cast(dtype = var_525_to_fp16_dtype_0, x = var_524)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_524_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_524_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_557_axes_0 = const()[name = string("op_557_axes_0"), val = tensor([-1])]; + tensor var_557_cast_fp16 = expand_dims(axes = var_557_axes_0, x = linear_12_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_557_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_524_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_581_axes_0 = const()[name = string("op_581_axes_0"), val = tensor([-1])]; + tensor var_581_cast_fp16 = expand_dims(axes = var_581_axes_0, x = linear_13_cast_fp16)[name = string("op_581_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_581_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_524_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_524_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_524_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_637_axes_0 = const()[name = string("op_637_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_36_to_fp16 = const()[name = string("op_36_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_637_cast_fp16 = layer_norm(axes = var_637_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_637_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_637_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_663_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_663_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_661_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_663_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_702_axes_0 = const()[name = string("op_702_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_702_cast_fp16 = layer_norm(axes = var_702_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_702_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_704_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_704_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_702_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_704_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_743_axes_0 = const()[name = string("op_743_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_743_cast_fp16 = layer_norm(axes = var_743_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_743_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_745_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_745_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_743_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_745_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_784_axes_0 = const()[name = string("op_784_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_784_cast_fp16 = layer_norm(axes = var_784_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_784_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_786_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_786_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_784_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_786_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_825_cast_fp16 = layer_norm(axes = var_825_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_825_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_827_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_827_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_825_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_827_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_866_cast_fp16 = layer_norm(axes = var_866_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_868_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_868_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_866_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_868_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_907_axes_0 = const()[name = string("op_907_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_907_cast_fp16 = layer_norm(axes = var_907_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_907_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_909_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_909_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_907_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_909_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_948_axes_0 = const()[name = string("op_948_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_948_cast_fp16 = layer_norm(axes = var_948_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_948_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_950_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_950_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_948_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_950_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_978_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_978_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_978_to_fp16_quantized, x = var_977_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_984_split_sizes_0 = const()[name = string("op_984_split_sizes_0"), val = tensor([513, 513])]; + int32 var_984_axis_0 = const()[name = string("op_984_axis_0"), val = int32(1)]; + tensor var_984_cast_fp16_0, tensor var_984_cast_fp16_1 = split(axis = var_984_axis_0, split_sizes = var_984_split_sizes_0, x = values_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_986_cast_fp16 = exp(x = var_984_cast_fp16_0)[name = string("op_986_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_986_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_988_cast_fp16 = cos(x = var_984_cast_fp16_1)[name = string("op_988_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_988_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_990_cast_fp16 = sin(x = var_984_cast_fp16_1)[name = string("op_990_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_990_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1005_pad_type_0 = const()[name = string("op_1005_pad_type_0"), val = string("valid")]; + tensor var_1005_strides_0 = const()[name = string("op_1005_strides_0"), val = tensor([1])]; + tensor var_1005_pad_0 = const()[name = string("op_1005_pad_0"), val = tensor([0, 0])]; + tensor var_1005_dilations_0 = const()[name = string("op_1005_dilations_0"), val = tensor([1])]; + int32 var_1005_groups_0 = const()[name = string("op_1005_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1005_cast_fp16 = conv(dilations = var_1005_dilations_0, groups = var_1005_groups_0, pad = var_1005_pad_0, pad_type = var_1005_pad_type_0, strides = var_1005_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1005_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1005_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1009_to_fp16 = const()[name = string("op_1009_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1009_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1012 = const()[name = string("op_1012"), val = tensor([-1, 4, 256, 2048])]; + tensor pieces_cast_fp16 = reshape(shape = var_1012, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1015_begin_0 = const()[name = string("op_1015_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1015_end_0 = const()[name = string("op_1015_end_0"), val = tensor([1, 1, 256, 2048])]; + tensor var_1015_end_mask_0 = const()[name = string("op_1015_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1015_squeeze_mask_0 = const()[name = string("op_1015_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1015_cast_fp16 = slice_by_index(begin = var_1015_begin_0, end = var_1015_end_0, end_mask = var_1015_end_mask_0, squeeze_mask = var_1015_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1018_perm_0 = const()[name = string("op_1018_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1019 = const()[name = string("op_1019"), val = tensor([-1, 524288])]; + tensor var_1018_cast_fp16 = transpose(perm = var_1018_perm_0, x = var_1015_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1019, x = var_1018_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1023_pad_0 = const()[name = string("op_1023_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1023_mode_0 = const()[name = string("op_1023_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1023_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1023_mode_0, pad = var_1023_pad_0, x = input_163_cast_fp16)[name = string("op_1023_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1023_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1026_begin_0 = const()[name = string("op_1026_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1026_end_0 = const()[name = string("op_1026_end_0"), val = tensor([1, 2, 256, 2048])]; + tensor var_1026_end_mask_0 = const()[name = string("op_1026_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1026_squeeze_mask_0 = const()[name = string("op_1026_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1026_cast_fp16 = slice_by_index(begin = var_1026_begin_0, end = var_1026_end_0, end_mask = var_1026_end_mask_0, squeeze_mask = var_1026_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1029_perm_0 = const()[name = string("op_1029_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1030 = const()[name = string("op_1030"), val = tensor([-1, 524288])]; + tensor var_1029_cast_fp16 = transpose(perm = var_1029_perm_0, x = var_1026_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1030, x = var_1029_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1034_pad_0 = const()[name = string("op_1034_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1034_mode_0 = const()[name = string("op_1034_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1034_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1034_mode_0, pad = var_1034_pad_0, x = input_165_cast_fp16)[name = string("op_1034_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1034_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1038_begin_0 = const()[name = string("op_1038_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1038_end_0 = const()[name = string("op_1038_end_0"), val = tensor([1, 3, 256, 2048])]; + tensor var_1038_end_mask_0 = const()[name = string("op_1038_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1038_squeeze_mask_0 = const()[name = string("op_1038_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1038_cast_fp16 = slice_by_index(begin = var_1038_begin_0, end = var_1038_end_0, end_mask = var_1038_end_mask_0, squeeze_mask = var_1038_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor var_1041_perm_0 = const()[name = string("op_1041_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1042 = const()[name = string("op_1042"), val = tensor([-1, 524288])]; + tensor var_1041_cast_fp16 = transpose(perm = var_1041_perm_0, x = var_1038_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1042, x = var_1041_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1046_pad_0 = const()[name = string("op_1046_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1046_mode_0 = const()[name = string("op_1046_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1046_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1046_mode_0, pad = var_1046_pad_0, x = input_167_cast_fp16)[name = string("op_1046_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1046_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1050_begin_0 = const()[name = string("op_1050_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1050_end_0 = const()[name = string("op_1050_end_0"), val = tensor([1, 4, 256, 2048])]; + tensor var_1050_end_mask_0 = const()[name = string("op_1050_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1050_squeeze_mask_0 = const()[name = string("op_1050_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1050_cast_fp16 = slice_by_index(begin = var_1050_begin_0, end = var_1050_end_0, end_mask = var_1050_end_mask_0, squeeze_mask = var_1050_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1050_cast_fp16")]; + tensor var_1053_perm_0 = const()[name = string("op_1053_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1054 = const()[name = string("op_1054"), val = tensor([-1, 524288])]; + tensor var_1053_cast_fp16 = transpose(perm = var_1053_perm_0, x = var_1050_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1054, x = var_1053_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1058_pad_0 = const()[name = string("op_1058_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1058_mode_0 = const()[name = string("op_1058_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1058_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1058_mode_0, pad = var_1058_pad_0, x = input_cast_fp16)[name = string("op_1058_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1058_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 524544])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1063_y_0_to_fp16 = const()[name = string("_inversed_1063_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29993280)))]; + tensor _inversed_1063_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1063_y_0_to_fp16)[name = string("_inversed_1063_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1063_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); + func vocoder_128(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + int32 var_40 = const()[name = string("op_40"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_84 = maximum(x = emotion_id, y = var_38)[name = string("op_84")]; + tensor input_5 = minimum(x = var_84, y = var_40)[name = string("input_5")]; + int32 var_87_batch_dims_0 = const()[name = string("op_87_batch_dims_0"), val = int32(0)]; + bool var_87_validate_indices_0 = const()[name = string("op_87_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_87_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_87_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_87_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_87_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_87_batch_dims_0, indices = select_0_1, validate_indices = var_87_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_87_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_89_axis_0 = const()[name = string("op_89_axis_0"), val = int32(0)]; + int32 var_89_batch_dims_0 = const()[name = string("op_89_batch_dims_0"), val = int32(0)]; + bool var_89_validate_indices_0 = const()[name = string("op_89_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_89_cast_fp16_cast_uint16 = gather(axis = var_89_axis_0, batch_dims = var_89_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_89_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_89_cast_fp16_cast_uint16")]; + tensor var_90_cast_fp16 = add(x = op_87_cast_fp16_cast_uint16_cast_uint16, y = var_89_cast_fp16_cast_uint16)[name = string("op_90_cast_fp16")]; + int32 var_92_axis_0 = const()[name = string("op_92_axis_0"), val = int32(0)]; + int32 var_92_batch_dims_0 = const()[name = string("op_92_batch_dims_0"), val = int32(0)]; + bool var_92_validate_indices_0 = const()[name = string("op_92_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_92_cast_fp16_cast_uint16 = gather(axis = var_92_axis_0, batch_dims = var_92_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_92_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_92_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_90_cast_fp16, y = var_92_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_94_axes_0 = const()[name = string("op_94_axes_0"), val = tensor([-1])]; + tensor var_94_cast_fp16 = expand_dims(axes = var_94_axes_0, x = cond_cast_fp16)[name = string("op_94_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_94_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_97_axes_0 = const()[name = string("op_97_axes_0"), val = tensor([1])]; + tensor var_97 = expand_dims(axes = var_97_axes_0, x = latent_mask_1)[name = string("op_97")]; + string var_98_to_fp16_dtype_0 = const()[name = string("op_98_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_97_to_fp16 = cast(dtype = var_98_to_fp16_dtype_0, x = var_97)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_97_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_97_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_47_to_fp16 = const()[name = string("op_47_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_130_axes_0 = const()[name = string("op_130_axes_0"), val = tensor([-1])]; + tensor var_130_cast_fp16 = expand_dims(axes = var_130_axes_0, x = linear_0_cast_fp16)[name = string("op_130_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_130_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_97_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_154_axes_0 = const()[name = string("op_154_axes_0"), val = tensor([-1])]; + tensor var_154_cast_fp16 = expand_dims(axes = var_154_axes_0, x = linear_1_cast_fp16)[name = string("op_154_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_154_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_97_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_97_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_97_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_199_axes_0 = const()[name = string("op_199_axes_0"), val = tensor([-1])]; + tensor var_199_cast_fp16 = expand_dims(axes = var_199_axes_0, x = linear_2_cast_fp16)[name = string("op_199_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_199_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_97_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_223_axes_0 = const()[name = string("op_223_axes_0"), val = tensor([-1])]; + tensor var_223_cast_fp16 = expand_dims(axes = var_223_axes_0, x = linear_3_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_223_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_97_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_97_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_97_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_268_axes_0 = const()[name = string("op_268_axes_0"), val = tensor([-1])]; + tensor var_268_cast_fp16 = expand_dims(axes = var_268_axes_0, x = linear_4_cast_fp16)[name = string("op_268_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_268_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_97_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([-1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = linear_5_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_292_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_97_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_97_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_97_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_337_axes_0 = const()[name = string("op_337_axes_0"), val = tensor([-1])]; + tensor var_337_cast_fp16 = expand_dims(axes = var_337_axes_0, x = linear_6_cast_fp16)[name = string("op_337_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_337_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_97_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_361_axes_0 = const()[name = string("op_361_axes_0"), val = tensor([-1])]; + tensor var_361_cast_fp16 = expand_dims(axes = var_361_axes_0, x = linear_7_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_361_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_97_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_97_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_97_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_406_axes_0 = const()[name = string("op_406_axes_0"), val = tensor([-1])]; + tensor var_406_cast_fp16 = expand_dims(axes = var_406_axes_0, x = linear_8_cast_fp16)[name = string("op_406_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_406_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_97_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_430_axes_0 = const()[name = string("op_430_axes_0"), val = tensor([-1])]; + tensor var_430_cast_fp16 = expand_dims(axes = var_430_axes_0, x = linear_9_cast_fp16)[name = string("op_430_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_430_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_97_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_97_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_97_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_475_axes_0 = const()[name = string("op_475_axes_0"), val = tensor([-1])]; + tensor var_475_cast_fp16 = expand_dims(axes = var_475_axes_0, x = linear_10_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_475_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_97_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([-1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = linear_11_cast_fp16)[name = string("op_499_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_499_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_97_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_97_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 256])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 128, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_523_axes_0 = const()[name = string("op_523_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_523 = expand_dims(axes = var_523_axes_0, x = mask_53)[name = string("op_523")]; + string var_524_to_fp16_dtype_0 = const()[name = string("op_524_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_523_to_fp16 = cast(dtype = var_524_to_fp16_dtype_0, x = var_523)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_523_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_523_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_556_axes_0 = const()[name = string("op_556_axes_0"), val = tensor([-1])]; + tensor var_556_cast_fp16 = expand_dims(axes = var_556_axes_0, x = linear_12_cast_fp16)[name = string("op_556_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_556_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_523_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_580_axes_0 = const()[name = string("op_580_axes_0"), val = tensor([-1])]; + tensor var_580_cast_fp16 = expand_dims(axes = var_580_axes_0, x = linear_13_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_580_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_523_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_523_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_523_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_35_to_fp16 = const()[name = string("op_35_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_636_cast_fp16 = layer_norm(axes = var_636_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_636_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_636_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_660_cast_fp16 = layer_norm(axes = var_660_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_662_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_662_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_660_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_662_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_701_axes_0 = const()[name = string("op_701_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_701_cast_fp16 = layer_norm(axes = var_701_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_701_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_703_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_703_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_701_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_703_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_742_axes_0 = const()[name = string("op_742_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_742_cast_fp16 = layer_norm(axes = var_742_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_742_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_744_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_744_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_742_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_744_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_783_cast_fp16 = layer_norm(axes = var_783_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_783_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_785_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_785_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_783_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_785_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_824_axes_0 = const()[name = string("op_824_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_824_cast_fp16 = layer_norm(axes = var_824_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_826_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_826_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_824_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_826_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_865_axes_0 = const()[name = string("op_865_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_865_cast_fp16 = layer_norm(axes = var_865_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_867_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_867_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_865_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_867_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_906_axes_0 = const()[name = string("op_906_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_906_cast_fp16 = layer_norm(axes = var_906_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_908_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_908_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_906_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_908_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_947_axes_0 = const()[name = string("op_947_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_947_cast_fp16 = layer_norm(axes = var_947_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_947_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_949_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_949_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_947_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_949_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_976_perm_0 = const()[name = string("op_976_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_977_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_977_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_976_cast_fp16 = transpose(perm = var_976_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_977_to_fp16_quantized, x = var_976_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_983_split_sizes_0 = const()[name = string("op_983_split_sizes_0"), val = tensor([513, 513])]; + int32 var_983_axis_0 = const()[name = string("op_983_axis_0"), val = int32(1)]; + tensor var_983_cast_fp16_0, tensor var_983_cast_fp16_1 = split(axis = var_983_axis_0, split_sizes = var_983_split_sizes_0, x = values_cast_fp16)[name = string("op_983_cast_fp16")]; + tensor var_985_cast_fp16 = exp(x = var_983_cast_fp16_0)[name = string("op_985_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_985_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_987_cast_fp16 = cos(x = var_983_cast_fp16_1)[name = string("op_987_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_987_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_989_cast_fp16 = sin(x = var_983_cast_fp16_1)[name = string("op_989_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_989_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1004_pad_type_0 = const()[name = string("op_1004_pad_type_0"), val = string("valid")]; + tensor var_1004_strides_0 = const()[name = string("op_1004_strides_0"), val = tensor([1])]; + tensor var_1004_pad_0 = const()[name = string("op_1004_pad_0"), val = tensor([0, 0])]; + tensor var_1004_dilations_0 = const()[name = string("op_1004_dilations_0"), val = tensor([1])]; + int32 var_1004_groups_0 = const()[name = string("op_1004_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1004_cast_fp16 = conv(dilations = var_1004_dilations_0, groups = var_1004_groups_0, pad = var_1004_pad_0, pad_type = var_1004_pad_type_0, strides = var_1004_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1004_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1008_to_fp16 = const()[name = string("op_1008_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1008_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1011 = const()[name = string("op_1011"), val = tensor([-1, 4, 256, 256])]; + tensor pieces_cast_fp16 = reshape(shape = var_1011, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1014_begin_0 = const()[name = string("op_1014_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1014_end_0 = const()[name = string("op_1014_end_0"), val = tensor([1, 1, 256, 256])]; + tensor var_1014_end_mask_0 = const()[name = string("op_1014_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1014_squeeze_mask_0 = const()[name = string("op_1014_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1014_cast_fp16 = slice_by_index(begin = var_1014_begin_0, end = var_1014_end_0, end_mask = var_1014_end_mask_0, squeeze_mask = var_1014_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1017_perm_0 = const()[name = string("op_1017_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1018 = const()[name = string("op_1018"), val = tensor([-1, 65536])]; + tensor var_1017_cast_fp16 = transpose(perm = var_1017_perm_0, x = var_1014_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1018, x = var_1017_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1022_pad_0 = const()[name = string("op_1022_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1022_mode_0 = const()[name = string("op_1022_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1022_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1022_mode_0, pad = var_1022_pad_0, x = input_163_cast_fp16)[name = string("op_1022_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1022_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1025_begin_0 = const()[name = string("op_1025_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1025_end_0 = const()[name = string("op_1025_end_0"), val = tensor([1, 2, 256, 256])]; + tensor var_1025_end_mask_0 = const()[name = string("op_1025_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1025_squeeze_mask_0 = const()[name = string("op_1025_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1025_cast_fp16 = slice_by_index(begin = var_1025_begin_0, end = var_1025_end_0, end_mask = var_1025_end_mask_0, squeeze_mask = var_1025_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1025_cast_fp16")]; + tensor var_1028_perm_0 = const()[name = string("op_1028_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1029 = const()[name = string("op_1029"), val = tensor([-1, 65536])]; + tensor var_1028_cast_fp16 = transpose(perm = var_1028_perm_0, x = var_1025_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1029, x = var_1028_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1033_pad_0 = const()[name = string("op_1033_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1033_mode_0 = const()[name = string("op_1033_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1033_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1033_mode_0, pad = var_1033_pad_0, x = input_165_cast_fp16)[name = string("op_1033_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1033_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1037_begin_0 = const()[name = string("op_1037_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1037_end_0 = const()[name = string("op_1037_end_0"), val = tensor([1, 3, 256, 256])]; + tensor var_1037_end_mask_0 = const()[name = string("op_1037_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1037_squeeze_mask_0 = const()[name = string("op_1037_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1037_cast_fp16 = slice_by_index(begin = var_1037_begin_0, end = var_1037_end_0, end_mask = var_1037_end_mask_0, squeeze_mask = var_1037_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1037_cast_fp16")]; + tensor var_1040_perm_0 = const()[name = string("op_1040_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1041 = const()[name = string("op_1041"), val = tensor([-1, 65536])]; + tensor var_1040_cast_fp16 = transpose(perm = var_1040_perm_0, x = var_1037_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1041, x = var_1040_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1045_pad_0 = const()[name = string("op_1045_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1045_mode_0 = const()[name = string("op_1045_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1045_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1045_mode_0, pad = var_1045_pad_0, x = input_167_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1045_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1049_begin_0 = const()[name = string("op_1049_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1049_end_0 = const()[name = string("op_1049_end_0"), val = tensor([1, 4, 256, 256])]; + tensor var_1049_end_mask_0 = const()[name = string("op_1049_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1049_squeeze_mask_0 = const()[name = string("op_1049_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1049_cast_fp16 = slice_by_index(begin = var_1049_begin_0, end = var_1049_end_0, end_mask = var_1049_end_mask_0, squeeze_mask = var_1049_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1049_cast_fp16")]; + tensor var_1052_perm_0 = const()[name = string("op_1052_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1053 = const()[name = string("op_1053"), val = tensor([-1, 65536])]; + tensor var_1052_cast_fp16 = transpose(perm = var_1052_perm_0, x = var_1049_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1053, x = var_1052_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1057_pad_0 = const()[name = string("op_1057_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1057_mode_0 = const()[name = string("op_1057_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1057_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1057_mode_0, pad = var_1057_pad_0, x = input_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1057_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 65792])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1062_y_0_to_fp16 = const()[name = string("_inversed_1062_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27898368)))]; + tensor _inversed_1062_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1062_y_0_to_fp16)[name = string("_inversed_1062_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1062_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); + func vocoder_256(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + int32 var_40 = const()[name = string("op_40"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_84 = maximum(x = emotion_id, y = var_38)[name = string("op_84")]; + tensor input_5 = minimum(x = var_84, y = var_40)[name = string("input_5")]; + int32 var_87_batch_dims_0 = const()[name = string("op_87_batch_dims_0"), val = int32(0)]; + bool var_87_validate_indices_0 = const()[name = string("op_87_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_87_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_87_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_87_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_87_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_87_batch_dims_0, indices = select_0_1, validate_indices = var_87_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_87_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_89_axis_0 = const()[name = string("op_89_axis_0"), val = int32(0)]; + int32 var_89_batch_dims_0 = const()[name = string("op_89_batch_dims_0"), val = int32(0)]; + bool var_89_validate_indices_0 = const()[name = string("op_89_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_89_cast_fp16_cast_uint16 = gather(axis = var_89_axis_0, batch_dims = var_89_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_89_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_89_cast_fp16_cast_uint16")]; + tensor var_90_cast_fp16 = add(x = op_87_cast_fp16_cast_uint16_cast_uint16, y = var_89_cast_fp16_cast_uint16)[name = string("op_90_cast_fp16")]; + int32 var_92_axis_0 = const()[name = string("op_92_axis_0"), val = int32(0)]; + int32 var_92_batch_dims_0 = const()[name = string("op_92_batch_dims_0"), val = int32(0)]; + bool var_92_validate_indices_0 = const()[name = string("op_92_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_92_cast_fp16_cast_uint16 = gather(axis = var_92_axis_0, batch_dims = var_92_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_92_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_92_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_90_cast_fp16, y = var_92_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_94_axes_0 = const()[name = string("op_94_axes_0"), val = tensor([-1])]; + tensor var_94_cast_fp16 = expand_dims(axes = var_94_axes_0, x = cond_cast_fp16)[name = string("op_94_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_94_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_97_axes_0 = const()[name = string("op_97_axes_0"), val = tensor([1])]; + tensor var_97 = expand_dims(axes = var_97_axes_0, x = latent_mask_1)[name = string("op_97")]; + string var_98_to_fp16_dtype_0 = const()[name = string("op_98_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_97_to_fp16 = cast(dtype = var_98_to_fp16_dtype_0, x = var_97)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_97_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_97_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_47_to_fp16 = const()[name = string("op_47_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_130_axes_0 = const()[name = string("op_130_axes_0"), val = tensor([-1])]; + tensor var_130_cast_fp16 = expand_dims(axes = var_130_axes_0, x = linear_0_cast_fp16)[name = string("op_130_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_130_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_97_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_154_axes_0 = const()[name = string("op_154_axes_0"), val = tensor([-1])]; + tensor var_154_cast_fp16 = expand_dims(axes = var_154_axes_0, x = linear_1_cast_fp16)[name = string("op_154_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_154_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_97_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_97_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_97_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_199_axes_0 = const()[name = string("op_199_axes_0"), val = tensor([-1])]; + tensor var_199_cast_fp16 = expand_dims(axes = var_199_axes_0, x = linear_2_cast_fp16)[name = string("op_199_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_199_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_97_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_223_axes_0 = const()[name = string("op_223_axes_0"), val = tensor([-1])]; + tensor var_223_cast_fp16 = expand_dims(axes = var_223_axes_0, x = linear_3_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_223_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_97_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_97_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_97_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_268_axes_0 = const()[name = string("op_268_axes_0"), val = tensor([-1])]; + tensor var_268_cast_fp16 = expand_dims(axes = var_268_axes_0, x = linear_4_cast_fp16)[name = string("op_268_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_268_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_97_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([-1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = linear_5_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_292_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_97_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_97_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_97_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_337_axes_0 = const()[name = string("op_337_axes_0"), val = tensor([-1])]; + tensor var_337_cast_fp16 = expand_dims(axes = var_337_axes_0, x = linear_6_cast_fp16)[name = string("op_337_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_337_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_97_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_361_axes_0 = const()[name = string("op_361_axes_0"), val = tensor([-1])]; + tensor var_361_cast_fp16 = expand_dims(axes = var_361_axes_0, x = linear_7_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_361_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_97_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_97_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_97_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_406_axes_0 = const()[name = string("op_406_axes_0"), val = tensor([-1])]; + tensor var_406_cast_fp16 = expand_dims(axes = var_406_axes_0, x = linear_8_cast_fp16)[name = string("op_406_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_406_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_97_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_430_axes_0 = const()[name = string("op_430_axes_0"), val = tensor([-1])]; + tensor var_430_cast_fp16 = expand_dims(axes = var_430_axes_0, x = linear_9_cast_fp16)[name = string("op_430_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_430_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_97_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_97_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_97_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_475_axes_0 = const()[name = string("op_475_axes_0"), val = tensor([-1])]; + tensor var_475_cast_fp16 = expand_dims(axes = var_475_axes_0, x = linear_10_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_475_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_97_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([-1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = linear_11_cast_fp16)[name = string("op_499_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_499_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_97_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_97_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 512])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 256, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_523_axes_0 = const()[name = string("op_523_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_523 = expand_dims(axes = var_523_axes_0, x = mask_53)[name = string("op_523")]; + string var_524_to_fp16_dtype_0 = const()[name = string("op_524_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_523_to_fp16 = cast(dtype = var_524_to_fp16_dtype_0, x = var_523)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_523_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_523_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_556_axes_0 = const()[name = string("op_556_axes_0"), val = tensor([-1])]; + tensor var_556_cast_fp16 = expand_dims(axes = var_556_axes_0, x = linear_12_cast_fp16)[name = string("op_556_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_556_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_523_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_580_axes_0 = const()[name = string("op_580_axes_0"), val = tensor([-1])]; + tensor var_580_cast_fp16 = expand_dims(axes = var_580_axes_0, x = linear_13_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_580_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_523_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_523_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_523_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_35_to_fp16 = const()[name = string("op_35_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_636_cast_fp16 = layer_norm(axes = var_636_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_636_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_636_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_660_cast_fp16 = layer_norm(axes = var_660_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_662_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_662_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_660_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_662_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_701_axes_0 = const()[name = string("op_701_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_701_cast_fp16 = layer_norm(axes = var_701_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_701_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_703_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_703_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_701_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_703_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_742_axes_0 = const()[name = string("op_742_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_742_cast_fp16 = layer_norm(axes = var_742_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_742_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_744_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_744_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_742_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_744_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_783_cast_fp16 = layer_norm(axes = var_783_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_783_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_785_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_785_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_783_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_785_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_824_axes_0 = const()[name = string("op_824_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_824_cast_fp16 = layer_norm(axes = var_824_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_826_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_826_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_824_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_826_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_865_axes_0 = const()[name = string("op_865_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_865_cast_fp16 = layer_norm(axes = var_865_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_867_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_867_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_865_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_867_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_906_axes_0 = const()[name = string("op_906_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_906_cast_fp16 = layer_norm(axes = var_906_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_908_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_908_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_906_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_908_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_947_axes_0 = const()[name = string("op_947_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_947_cast_fp16 = layer_norm(axes = var_947_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_947_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_949_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_949_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_947_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_949_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_976_perm_0 = const()[name = string("op_976_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_977_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_977_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_976_cast_fp16 = transpose(perm = var_976_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_977_to_fp16_quantized, x = var_976_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_983_split_sizes_0 = const()[name = string("op_983_split_sizes_0"), val = tensor([513, 513])]; + int32 var_983_axis_0 = const()[name = string("op_983_axis_0"), val = int32(1)]; + tensor var_983_cast_fp16_0, tensor var_983_cast_fp16_1 = split(axis = var_983_axis_0, split_sizes = var_983_split_sizes_0, x = values_cast_fp16)[name = string("op_983_cast_fp16")]; + tensor var_985_cast_fp16 = exp(x = var_983_cast_fp16_0)[name = string("op_985_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_985_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_987_cast_fp16 = cos(x = var_983_cast_fp16_1)[name = string("op_987_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_987_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_989_cast_fp16 = sin(x = var_983_cast_fp16_1)[name = string("op_989_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_989_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1004_pad_type_0 = const()[name = string("op_1004_pad_type_0"), val = string("valid")]; + tensor var_1004_strides_0 = const()[name = string("op_1004_strides_0"), val = tensor([1])]; + tensor var_1004_pad_0 = const()[name = string("op_1004_pad_0"), val = tensor([0, 0])]; + tensor var_1004_dilations_0 = const()[name = string("op_1004_dilations_0"), val = tensor([1])]; + int32 var_1004_groups_0 = const()[name = string("op_1004_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1004_cast_fp16 = conv(dilations = var_1004_dilations_0, groups = var_1004_groups_0, pad = var_1004_pad_0, pad_type = var_1004_pad_type_0, strides = var_1004_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1004_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1008_to_fp16 = const()[name = string("op_1008_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1008_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1011 = const()[name = string("op_1011"), val = tensor([-1, 4, 256, 512])]; + tensor pieces_cast_fp16 = reshape(shape = var_1011, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1014_begin_0 = const()[name = string("op_1014_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1014_end_0 = const()[name = string("op_1014_end_0"), val = tensor([1, 1, 256, 512])]; + tensor var_1014_end_mask_0 = const()[name = string("op_1014_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1014_squeeze_mask_0 = const()[name = string("op_1014_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1014_cast_fp16 = slice_by_index(begin = var_1014_begin_0, end = var_1014_end_0, end_mask = var_1014_end_mask_0, squeeze_mask = var_1014_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1017_perm_0 = const()[name = string("op_1017_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1018 = const()[name = string("op_1018"), val = tensor([-1, 131072])]; + tensor var_1017_cast_fp16 = transpose(perm = var_1017_perm_0, x = var_1014_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1018, x = var_1017_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1022_pad_0 = const()[name = string("op_1022_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1022_mode_0 = const()[name = string("op_1022_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1022_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1022_mode_0, pad = var_1022_pad_0, x = input_163_cast_fp16)[name = string("op_1022_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1022_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1025_begin_0 = const()[name = string("op_1025_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1025_end_0 = const()[name = string("op_1025_end_0"), val = tensor([1, 2, 256, 512])]; + tensor var_1025_end_mask_0 = const()[name = string("op_1025_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1025_squeeze_mask_0 = const()[name = string("op_1025_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1025_cast_fp16 = slice_by_index(begin = var_1025_begin_0, end = var_1025_end_0, end_mask = var_1025_end_mask_0, squeeze_mask = var_1025_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1025_cast_fp16")]; + tensor var_1028_perm_0 = const()[name = string("op_1028_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1029 = const()[name = string("op_1029"), val = tensor([-1, 131072])]; + tensor var_1028_cast_fp16 = transpose(perm = var_1028_perm_0, x = var_1025_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1029, x = var_1028_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1033_pad_0 = const()[name = string("op_1033_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1033_mode_0 = const()[name = string("op_1033_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1033_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1033_mode_0, pad = var_1033_pad_0, x = input_165_cast_fp16)[name = string("op_1033_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1033_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1037_begin_0 = const()[name = string("op_1037_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1037_end_0 = const()[name = string("op_1037_end_0"), val = tensor([1, 3, 256, 512])]; + tensor var_1037_end_mask_0 = const()[name = string("op_1037_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1037_squeeze_mask_0 = const()[name = string("op_1037_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1037_cast_fp16 = slice_by_index(begin = var_1037_begin_0, end = var_1037_end_0, end_mask = var_1037_end_mask_0, squeeze_mask = var_1037_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1037_cast_fp16")]; + tensor var_1040_perm_0 = const()[name = string("op_1040_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1041 = const()[name = string("op_1041"), val = tensor([-1, 131072])]; + tensor var_1040_cast_fp16 = transpose(perm = var_1040_perm_0, x = var_1037_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1041, x = var_1040_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1045_pad_0 = const()[name = string("op_1045_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1045_mode_0 = const()[name = string("op_1045_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1045_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1045_mode_0, pad = var_1045_pad_0, x = input_167_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1045_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1049_begin_0 = const()[name = string("op_1049_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1049_end_0 = const()[name = string("op_1049_end_0"), val = tensor([1, 4, 256, 512])]; + tensor var_1049_end_mask_0 = const()[name = string("op_1049_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1049_squeeze_mask_0 = const()[name = string("op_1049_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1049_cast_fp16 = slice_by_index(begin = var_1049_begin_0, end = var_1049_end_0, end_mask = var_1049_end_mask_0, squeeze_mask = var_1049_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1049_cast_fp16")]; + tensor var_1052_perm_0 = const()[name = string("op_1052_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1053 = const()[name = string("op_1053"), val = tensor([-1, 131072])]; + tensor var_1052_cast_fp16 = transpose(perm = var_1052_perm_0, x = var_1049_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1053, x = var_1052_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1057_pad_0 = const()[name = string("op_1057_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1057_mode_0 = const()[name = string("op_1057_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1057_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1057_mode_0, pad = var_1057_pad_0, x = input_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1057_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 131328])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1062_y_0_to_fp16 = const()[name = string("_inversed_1062_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28028992)))]; + tensor _inversed_1062_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1062_y_0_to_fp16)[name = string("_inversed_1062_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1062_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); + func vocoder_384(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_38 = const()[name = string("op_38"), val = int32(0)]; + int32 var_40 = const()[name = string("op_40"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_84 = maximum(x = emotion_id, y = var_38)[name = string("op_84")]; + tensor input_5 = minimum(x = var_84, y = var_40)[name = string("input_5")]; + int32 var_87_batch_dims_0 = const()[name = string("op_87_batch_dims_0"), val = int32(0)]; + bool var_87_validate_indices_0 = const()[name = string("op_87_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_87_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_87_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_87_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_87_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_87_batch_dims_0, indices = select_0_1, validate_indices = var_87_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_87_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_89_axis_0 = const()[name = string("op_89_axis_0"), val = int32(0)]; + int32 var_89_batch_dims_0 = const()[name = string("op_89_batch_dims_0"), val = int32(0)]; + bool var_89_validate_indices_0 = const()[name = string("op_89_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_89_cast_fp16_cast_uint16 = gather(axis = var_89_axis_0, batch_dims = var_89_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_89_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_89_cast_fp16_cast_uint16")]; + tensor var_90_cast_fp16 = add(x = op_87_cast_fp16_cast_uint16_cast_uint16, y = var_89_cast_fp16_cast_uint16)[name = string("op_90_cast_fp16")]; + int32 var_92_axis_0 = const()[name = string("op_92_axis_0"), val = int32(0)]; + int32 var_92_batch_dims_0 = const()[name = string("op_92_batch_dims_0"), val = int32(0)]; + bool var_92_validate_indices_0 = const()[name = string("op_92_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_92_cast_fp16_cast_uint16 = gather(axis = var_92_axis_0, batch_dims = var_92_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_92_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_92_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_90_cast_fp16, y = var_92_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_94_axes_0 = const()[name = string("op_94_axes_0"), val = tensor([-1])]; + tensor var_94_cast_fp16 = expand_dims(axes = var_94_axes_0, x = cond_cast_fp16)[name = string("op_94_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_94_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_97_axes_0 = const()[name = string("op_97_axes_0"), val = tensor([1])]; + tensor var_97 = expand_dims(axes = var_97_axes_0, x = latent_mask_1)[name = string("op_97")]; + string var_98_to_fp16_dtype_0 = const()[name = string("op_98_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_97_to_fp16 = cast(dtype = var_98_to_fp16_dtype_0, x = var_97)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_97_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_97_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_47_to_fp16 = const()[name = string("op_47_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_130_axes_0 = const()[name = string("op_130_axes_0"), val = tensor([-1])]; + tensor var_130_cast_fp16 = expand_dims(axes = var_130_axes_0, x = linear_0_cast_fp16)[name = string("op_130_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_130_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_97_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_154_axes_0 = const()[name = string("op_154_axes_0"), val = tensor([-1])]; + tensor var_154_cast_fp16 = expand_dims(axes = var_154_axes_0, x = linear_1_cast_fp16)[name = string("op_154_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_154_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_97_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_97_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_97_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_199_axes_0 = const()[name = string("op_199_axes_0"), val = tensor([-1])]; + tensor var_199_cast_fp16 = expand_dims(axes = var_199_axes_0, x = linear_2_cast_fp16)[name = string("op_199_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_199_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_97_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_223_axes_0 = const()[name = string("op_223_axes_0"), val = tensor([-1])]; + tensor var_223_cast_fp16 = expand_dims(axes = var_223_axes_0, x = linear_3_cast_fp16)[name = string("op_223_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_223_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_97_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_97_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_97_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_268_axes_0 = const()[name = string("op_268_axes_0"), val = tensor([-1])]; + tensor var_268_cast_fp16 = expand_dims(axes = var_268_axes_0, x = linear_4_cast_fp16)[name = string("op_268_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_268_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_97_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_292_axes_0 = const()[name = string("op_292_axes_0"), val = tensor([-1])]; + tensor var_292_cast_fp16 = expand_dims(axes = var_292_axes_0, x = linear_5_cast_fp16)[name = string("op_292_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_292_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_97_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_97_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_97_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_337_axes_0 = const()[name = string("op_337_axes_0"), val = tensor([-1])]; + tensor var_337_cast_fp16 = expand_dims(axes = var_337_axes_0, x = linear_6_cast_fp16)[name = string("op_337_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_337_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_97_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_361_axes_0 = const()[name = string("op_361_axes_0"), val = tensor([-1])]; + tensor var_361_cast_fp16 = expand_dims(axes = var_361_axes_0, x = linear_7_cast_fp16)[name = string("op_361_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_361_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_97_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_97_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_97_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_406_axes_0 = const()[name = string("op_406_axes_0"), val = tensor([-1])]; + tensor var_406_cast_fp16 = expand_dims(axes = var_406_axes_0, x = linear_8_cast_fp16)[name = string("op_406_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_406_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_97_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_430_axes_0 = const()[name = string("op_430_axes_0"), val = tensor([-1])]; + tensor var_430_cast_fp16 = expand_dims(axes = var_430_axes_0, x = linear_9_cast_fp16)[name = string("op_430_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_430_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_97_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_97_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_97_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_475_axes_0 = const()[name = string("op_475_axes_0"), val = tensor([-1])]; + tensor var_475_cast_fp16 = expand_dims(axes = var_475_axes_0, x = linear_10_cast_fp16)[name = string("op_475_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_475_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_97_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_499_axes_0 = const()[name = string("op_499_axes_0"), val = tensor([-1])]; + tensor var_499_cast_fp16 = expand_dims(axes = var_499_axes_0, x = linear_11_cast_fp16)[name = string("op_499_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_499_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_97_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_97_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 768])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 384, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_523_axes_0 = const()[name = string("op_523_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_523 = expand_dims(axes = var_523_axes_0, x = mask_53)[name = string("op_523")]; + string var_524_to_fp16_dtype_0 = const()[name = string("op_524_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_523_to_fp16 = cast(dtype = var_524_to_fp16_dtype_0, x = var_523)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_523_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_523_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_556_axes_0 = const()[name = string("op_556_axes_0"), val = tensor([-1])]; + tensor var_556_cast_fp16 = expand_dims(axes = var_556_axes_0, x = linear_12_cast_fp16)[name = string("op_556_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_556_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_523_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_47_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_580_axes_0 = const()[name = string("op_580_axes_0"), val = tensor([-1])]; + tensor var_580_cast_fp16 = expand_dims(axes = var_580_axes_0, x = linear_13_cast_fp16)[name = string("op_580_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_580_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_523_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_523_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_523_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_636_axes_0 = const()[name = string("op_636_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_35_to_fp16 = const()[name = string("op_35_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_636_cast_fp16 = layer_norm(axes = var_636_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_636_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_636_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_660_axes_0 = const()[name = string("op_660_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_660_cast_fp16 = layer_norm(axes = var_660_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_660_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_662_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_662_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_660_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_662_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_701_axes_0 = const()[name = string("op_701_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_701_cast_fp16 = layer_norm(axes = var_701_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_701_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_703_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_703_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_701_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_703_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_742_axes_0 = const()[name = string("op_742_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_742_cast_fp16 = layer_norm(axes = var_742_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_742_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_744_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_744_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_742_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_744_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_783_axes_0 = const()[name = string("op_783_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_783_cast_fp16 = layer_norm(axes = var_783_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_783_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_785_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_785_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_783_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_785_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_824_axes_0 = const()[name = string("op_824_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_824_cast_fp16 = layer_norm(axes = var_824_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_824_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_826_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_826_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_824_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_826_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_865_axes_0 = const()[name = string("op_865_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_865_cast_fp16 = layer_norm(axes = var_865_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_865_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_867_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_867_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_865_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_867_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_906_axes_0 = const()[name = string("op_906_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_906_cast_fp16 = layer_norm(axes = var_906_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_906_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_908_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_908_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_906_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_908_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_947_axes_0 = const()[name = string("op_947_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_947_cast_fp16 = layer_norm(axes = var_947_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_947_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_949_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_949_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_947_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_949_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_35_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_976_perm_0 = const()[name = string("op_976_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_977_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_977_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_976_cast_fp16 = transpose(perm = var_976_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_977_to_fp16_quantized, x = var_976_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_983_split_sizes_0 = const()[name = string("op_983_split_sizes_0"), val = tensor([513, 513])]; + int32 var_983_axis_0 = const()[name = string("op_983_axis_0"), val = int32(1)]; + tensor var_983_cast_fp16_0, tensor var_983_cast_fp16_1 = split(axis = var_983_axis_0, split_sizes = var_983_split_sizes_0, x = values_cast_fp16)[name = string("op_983_cast_fp16")]; + tensor var_985_cast_fp16 = exp(x = var_983_cast_fp16_0)[name = string("op_985_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_985_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_987_cast_fp16 = cos(x = var_983_cast_fp16_1)[name = string("op_987_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_987_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_989_cast_fp16 = sin(x = var_983_cast_fp16_1)[name = string("op_989_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_989_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1004_pad_type_0 = const()[name = string("op_1004_pad_type_0"), val = string("valid")]; + tensor var_1004_strides_0 = const()[name = string("op_1004_strides_0"), val = tensor([1])]; + tensor var_1004_pad_0 = const()[name = string("op_1004_pad_0"), val = tensor([0, 0])]; + tensor var_1004_dilations_0 = const()[name = string("op_1004_dilations_0"), val = tensor([1])]; + int32 var_1004_groups_0 = const()[name = string("op_1004_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1004_cast_fp16 = conv(dilations = var_1004_dilations_0, groups = var_1004_groups_0, pad = var_1004_pad_0, pad_type = var_1004_pad_type_0, strides = var_1004_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1004_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1004_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1008_to_fp16 = const()[name = string("op_1008_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1008_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1011 = const()[name = string("op_1011"), val = tensor([-1, 4, 256, 768])]; + tensor pieces_cast_fp16 = reshape(shape = var_1011, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1014_begin_0 = const()[name = string("op_1014_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1014_end_0 = const()[name = string("op_1014_end_0"), val = tensor([1, 1, 256, 768])]; + tensor var_1014_end_mask_0 = const()[name = string("op_1014_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1014_squeeze_mask_0 = const()[name = string("op_1014_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1014_cast_fp16 = slice_by_index(begin = var_1014_begin_0, end = var_1014_end_0, end_mask = var_1014_end_mask_0, squeeze_mask = var_1014_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1014_cast_fp16")]; + tensor var_1017_perm_0 = const()[name = string("op_1017_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1018 = const()[name = string("op_1018"), val = tensor([-1, 196608])]; + tensor var_1017_cast_fp16 = transpose(perm = var_1017_perm_0, x = var_1014_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1018, x = var_1017_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1022_pad_0 = const()[name = string("op_1022_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1022_mode_0 = const()[name = string("op_1022_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1022_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1022_mode_0, pad = var_1022_pad_0, x = input_163_cast_fp16)[name = string("op_1022_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1022_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1025_begin_0 = const()[name = string("op_1025_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1025_end_0 = const()[name = string("op_1025_end_0"), val = tensor([1, 2, 256, 768])]; + tensor var_1025_end_mask_0 = const()[name = string("op_1025_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1025_squeeze_mask_0 = const()[name = string("op_1025_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1025_cast_fp16 = slice_by_index(begin = var_1025_begin_0, end = var_1025_end_0, end_mask = var_1025_end_mask_0, squeeze_mask = var_1025_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1025_cast_fp16")]; + tensor var_1028_perm_0 = const()[name = string("op_1028_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1029 = const()[name = string("op_1029"), val = tensor([-1, 196608])]; + tensor var_1028_cast_fp16 = transpose(perm = var_1028_perm_0, x = var_1025_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1029, x = var_1028_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1033_pad_0 = const()[name = string("op_1033_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1033_mode_0 = const()[name = string("op_1033_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1033_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1033_mode_0, pad = var_1033_pad_0, x = input_165_cast_fp16)[name = string("op_1033_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1033_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1037_begin_0 = const()[name = string("op_1037_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1037_end_0 = const()[name = string("op_1037_end_0"), val = tensor([1, 3, 256, 768])]; + tensor var_1037_end_mask_0 = const()[name = string("op_1037_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1037_squeeze_mask_0 = const()[name = string("op_1037_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1037_cast_fp16 = slice_by_index(begin = var_1037_begin_0, end = var_1037_end_0, end_mask = var_1037_end_mask_0, squeeze_mask = var_1037_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1037_cast_fp16")]; + tensor var_1040_perm_0 = const()[name = string("op_1040_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1041 = const()[name = string("op_1041"), val = tensor([-1, 196608])]; + tensor var_1040_cast_fp16 = transpose(perm = var_1040_perm_0, x = var_1037_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1041, x = var_1040_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1045_pad_0 = const()[name = string("op_1045_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1045_mode_0 = const()[name = string("op_1045_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1045_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1045_mode_0, pad = var_1045_pad_0, x = input_167_cast_fp16)[name = string("op_1045_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1045_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1049_begin_0 = const()[name = string("op_1049_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1049_end_0 = const()[name = string("op_1049_end_0"), val = tensor([1, 4, 256, 768])]; + tensor var_1049_end_mask_0 = const()[name = string("op_1049_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1049_squeeze_mask_0 = const()[name = string("op_1049_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1049_cast_fp16 = slice_by_index(begin = var_1049_begin_0, end = var_1049_end_0, end_mask = var_1049_end_mask_0, squeeze_mask = var_1049_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1049_cast_fp16")]; + tensor var_1052_perm_0 = const()[name = string("op_1052_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1053 = const()[name = string("op_1053"), val = tensor([-1, 196608])]; + tensor var_1052_cast_fp16 = transpose(perm = var_1052_perm_0, x = var_1049_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1053, x = var_1052_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1057_pad_0 = const()[name = string("op_1057_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1057_mode_0 = const()[name = string("op_1057_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1057_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1057_mode_0, pad = var_1057_pad_0, x = input_cast_fp16)[name = string("op_1057_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1057_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 196864])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1062_y_0_to_fp16 = const()[name = string("_inversed_1062_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28290688)))]; + tensor _inversed_1062_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1062_y_0_to_fp16)[name = string("_inversed_1062_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1062_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); + func vocoder_512(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + int32 var_41 = const()[name = string("op_41"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_85 = maximum(x = emotion_id, y = var_39)[name = string("op_85")]; + tensor input_5 = minimum(x = var_85, y = var_41)[name = string("input_5")]; + int32 var_88_batch_dims_0 = const()[name = string("op_88_batch_dims_0"), val = int32(0)]; + bool var_88_validate_indices_0 = const()[name = string("op_88_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_88_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_88_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_88_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_88_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_88_batch_dims_0, indices = select_0_1, validate_indices = var_88_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_88_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_90_axis_0 = const()[name = string("op_90_axis_0"), val = int32(0)]; + int32 var_90_batch_dims_0 = const()[name = string("op_90_batch_dims_0"), val = int32(0)]; + bool var_90_validate_indices_0 = const()[name = string("op_90_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_90_cast_fp16_cast_uint16 = gather(axis = var_90_axis_0, batch_dims = var_90_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_90_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_90_cast_fp16_cast_uint16")]; + tensor var_91_cast_fp16 = add(x = op_88_cast_fp16_cast_uint16_cast_uint16, y = var_90_cast_fp16_cast_uint16)[name = string("op_91_cast_fp16")]; + int32 var_93_axis_0 = const()[name = string("op_93_axis_0"), val = int32(0)]; + int32 var_93_batch_dims_0 = const()[name = string("op_93_batch_dims_0"), val = int32(0)]; + bool var_93_validate_indices_0 = const()[name = string("op_93_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_93_cast_fp16_cast_uint16 = gather(axis = var_93_axis_0, batch_dims = var_93_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_93_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_93_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_91_cast_fp16, y = var_93_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_95_axes_0 = const()[name = string("op_95_axes_0"), val = tensor([-1])]; + tensor var_95_cast_fp16 = expand_dims(axes = var_95_axes_0, x = cond_cast_fp16)[name = string("op_95_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_95_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_98_axes_0 = const()[name = string("op_98_axes_0"), val = tensor([1])]; + tensor var_98 = expand_dims(axes = var_98_axes_0, x = latent_mask_1)[name = string("op_98")]; + string var_99_to_fp16_dtype_0 = const()[name = string("op_99_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_98_to_fp16 = cast(dtype = var_99_to_fp16_dtype_0, x = var_98)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_98_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_98_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_48_to_fp16 = const()[name = string("op_48_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_131_axes_0 = const()[name = string("op_131_axes_0"), val = tensor([-1])]; + tensor var_131_cast_fp16 = expand_dims(axes = var_131_axes_0, x = linear_0_cast_fp16)[name = string("op_131_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_131_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_98_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_axes_0 = const()[name = string("op_155_axes_0"), val = tensor([-1])]; + tensor var_155_cast_fp16 = expand_dims(axes = var_155_axes_0, x = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_155_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_98_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_98_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_98_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_200_axes_0 = const()[name = string("op_200_axes_0"), val = tensor([-1])]; + tensor var_200_cast_fp16 = expand_dims(axes = var_200_axes_0, x = linear_2_cast_fp16)[name = string("op_200_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_200_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_98_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_224_axes_0 = const()[name = string("op_224_axes_0"), val = tensor([-1])]; + tensor var_224_cast_fp16 = expand_dims(axes = var_224_axes_0, x = linear_3_cast_fp16)[name = string("op_224_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_224_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_98_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_98_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_98_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_269_axes_0 = const()[name = string("op_269_axes_0"), val = tensor([-1])]; + tensor var_269_cast_fp16 = expand_dims(axes = var_269_axes_0, x = linear_4_cast_fp16)[name = string("op_269_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_269_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_98_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_293_axes_0 = const()[name = string("op_293_axes_0"), val = tensor([-1])]; + tensor var_293_cast_fp16 = expand_dims(axes = var_293_axes_0, x = linear_5_cast_fp16)[name = string("op_293_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_293_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_98_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_98_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_98_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([-1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = linear_6_cast_fp16)[name = string("op_338_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_338_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_98_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([-1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = linear_7_cast_fp16)[name = string("op_362_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_362_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_98_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_98_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_98_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_407_axes_0 = const()[name = string("op_407_axes_0"), val = tensor([-1])]; + tensor var_407_cast_fp16 = expand_dims(axes = var_407_axes_0, x = linear_8_cast_fp16)[name = string("op_407_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_407_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_98_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_431_axes_0 = const()[name = string("op_431_axes_0"), val = tensor([-1])]; + tensor var_431_cast_fp16 = expand_dims(axes = var_431_axes_0, x = linear_9_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_431_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_98_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_98_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_98_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + tensor var_476_cast_fp16 = expand_dims(axes = var_476_axes_0, x = linear_10_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_476_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_98_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_500_axes_0 = const()[name = string("op_500_axes_0"), val = tensor([-1])]; + tensor var_500_cast_fp16 = expand_dims(axes = var_500_axes_0, x = linear_11_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_500_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_98_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_98_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 1024])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 512, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_524 = expand_dims(axes = var_524_axes_0, x = mask_53)[name = string("op_524")]; + string var_525_to_fp16_dtype_0 = const()[name = string("op_525_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_524_to_fp16 = cast(dtype = var_525_to_fp16_dtype_0, x = var_524)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_524_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_524_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_557_axes_0 = const()[name = string("op_557_axes_0"), val = tensor([-1])]; + tensor var_557_cast_fp16 = expand_dims(axes = var_557_axes_0, x = linear_12_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_557_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_524_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_581_axes_0 = const()[name = string("op_581_axes_0"), val = tensor([-1])]; + tensor var_581_cast_fp16 = expand_dims(axes = var_581_axes_0, x = linear_13_cast_fp16)[name = string("op_581_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_581_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_524_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_524_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_524_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_637_axes_0 = const()[name = string("op_637_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_36_to_fp16 = const()[name = string("op_36_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_637_cast_fp16 = layer_norm(axes = var_637_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_637_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_637_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_663_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_663_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_661_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_663_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_702_axes_0 = const()[name = string("op_702_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_702_cast_fp16 = layer_norm(axes = var_702_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_702_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_704_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_704_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_702_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_704_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_743_axes_0 = const()[name = string("op_743_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_743_cast_fp16 = layer_norm(axes = var_743_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_743_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_745_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_745_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_743_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_745_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_784_axes_0 = const()[name = string("op_784_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_784_cast_fp16 = layer_norm(axes = var_784_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_784_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_786_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_786_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_784_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_786_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_825_cast_fp16 = layer_norm(axes = var_825_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_825_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_827_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_827_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_825_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_827_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_866_cast_fp16 = layer_norm(axes = var_866_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_868_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_868_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_866_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_868_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_907_axes_0 = const()[name = string("op_907_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_907_cast_fp16 = layer_norm(axes = var_907_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_907_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_909_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_909_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_907_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_909_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_948_axes_0 = const()[name = string("op_948_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_948_cast_fp16 = layer_norm(axes = var_948_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_948_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_950_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_950_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_948_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_950_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_978_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_978_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_978_to_fp16_quantized, x = var_977_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_984_split_sizes_0 = const()[name = string("op_984_split_sizes_0"), val = tensor([513, 513])]; + int32 var_984_axis_0 = const()[name = string("op_984_axis_0"), val = int32(1)]; + tensor var_984_cast_fp16_0, tensor var_984_cast_fp16_1 = split(axis = var_984_axis_0, split_sizes = var_984_split_sizes_0, x = values_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_986_cast_fp16 = exp(x = var_984_cast_fp16_0)[name = string("op_986_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_986_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_988_cast_fp16 = cos(x = var_984_cast_fp16_1)[name = string("op_988_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_988_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_990_cast_fp16 = sin(x = var_984_cast_fp16_1)[name = string("op_990_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_990_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1005_pad_type_0 = const()[name = string("op_1005_pad_type_0"), val = string("valid")]; + tensor var_1005_strides_0 = const()[name = string("op_1005_strides_0"), val = tensor([1])]; + tensor var_1005_pad_0 = const()[name = string("op_1005_pad_0"), val = tensor([0, 0])]; + tensor var_1005_dilations_0 = const()[name = string("op_1005_dilations_0"), val = tensor([1])]; + int32 var_1005_groups_0 = const()[name = string("op_1005_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1005_cast_fp16 = conv(dilations = var_1005_dilations_0, groups = var_1005_groups_0, pad = var_1005_pad_0, pad_type = var_1005_pad_type_0, strides = var_1005_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1005_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1005_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1009_to_fp16 = const()[name = string("op_1009_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1009_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1012 = const()[name = string("op_1012"), val = tensor([-1, 4, 256, 1024])]; + tensor pieces_cast_fp16 = reshape(shape = var_1012, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1015_begin_0 = const()[name = string("op_1015_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1015_end_0 = const()[name = string("op_1015_end_0"), val = tensor([1, 1, 256, 1024])]; + tensor var_1015_end_mask_0 = const()[name = string("op_1015_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1015_squeeze_mask_0 = const()[name = string("op_1015_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1015_cast_fp16 = slice_by_index(begin = var_1015_begin_0, end = var_1015_end_0, end_mask = var_1015_end_mask_0, squeeze_mask = var_1015_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1018_perm_0 = const()[name = string("op_1018_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1019 = const()[name = string("op_1019"), val = tensor([-1, 262144])]; + tensor var_1018_cast_fp16 = transpose(perm = var_1018_perm_0, x = var_1015_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1019, x = var_1018_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1023_pad_0 = const()[name = string("op_1023_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1023_mode_0 = const()[name = string("op_1023_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1023_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1023_mode_0, pad = var_1023_pad_0, x = input_163_cast_fp16)[name = string("op_1023_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1023_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1026_begin_0 = const()[name = string("op_1026_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1026_end_0 = const()[name = string("op_1026_end_0"), val = tensor([1, 2, 256, 1024])]; + tensor var_1026_end_mask_0 = const()[name = string("op_1026_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1026_squeeze_mask_0 = const()[name = string("op_1026_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1026_cast_fp16 = slice_by_index(begin = var_1026_begin_0, end = var_1026_end_0, end_mask = var_1026_end_mask_0, squeeze_mask = var_1026_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1029_perm_0 = const()[name = string("op_1029_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1030 = const()[name = string("op_1030"), val = tensor([-1, 262144])]; + tensor var_1029_cast_fp16 = transpose(perm = var_1029_perm_0, x = var_1026_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1030, x = var_1029_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1034_pad_0 = const()[name = string("op_1034_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1034_mode_0 = const()[name = string("op_1034_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1034_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1034_mode_0, pad = var_1034_pad_0, x = input_165_cast_fp16)[name = string("op_1034_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1034_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1038_begin_0 = const()[name = string("op_1038_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1038_end_0 = const()[name = string("op_1038_end_0"), val = tensor([1, 3, 256, 1024])]; + tensor var_1038_end_mask_0 = const()[name = string("op_1038_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1038_squeeze_mask_0 = const()[name = string("op_1038_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1038_cast_fp16 = slice_by_index(begin = var_1038_begin_0, end = var_1038_end_0, end_mask = var_1038_end_mask_0, squeeze_mask = var_1038_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor var_1041_perm_0 = const()[name = string("op_1041_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1042 = const()[name = string("op_1042"), val = tensor([-1, 262144])]; + tensor var_1041_cast_fp16 = transpose(perm = var_1041_perm_0, x = var_1038_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1042, x = var_1041_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1046_pad_0 = const()[name = string("op_1046_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1046_mode_0 = const()[name = string("op_1046_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1046_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1046_mode_0, pad = var_1046_pad_0, x = input_167_cast_fp16)[name = string("op_1046_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1046_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1050_begin_0 = const()[name = string("op_1050_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1050_end_0 = const()[name = string("op_1050_end_0"), val = tensor([1, 4, 256, 1024])]; + tensor var_1050_end_mask_0 = const()[name = string("op_1050_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1050_squeeze_mask_0 = const()[name = string("op_1050_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1050_cast_fp16 = slice_by_index(begin = var_1050_begin_0, end = var_1050_end_0, end_mask = var_1050_end_mask_0, squeeze_mask = var_1050_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1050_cast_fp16")]; + tensor var_1053_perm_0 = const()[name = string("op_1053_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1054 = const()[name = string("op_1054"), val = tensor([-1, 262144])]; + tensor var_1053_cast_fp16 = transpose(perm = var_1053_perm_0, x = var_1050_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1054, x = var_1053_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1058_pad_0 = const()[name = string("op_1058_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1058_mode_0 = const()[name = string("op_1058_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1058_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1058_mode_0, pad = var_1058_pad_0, x = input_cast_fp16)[name = string("op_1058_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1058_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 262400])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1063_y_0_to_fp16 = const()[name = string("_inversed_1063_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28683456)))]; + tensor _inversed_1063_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1063_y_0_to_fp16)[name = string("_inversed_1063_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1063_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); + func vocoder_768(tensor emotion_id, tensor language_id, tensor latent_mask, tensor latents, tensor voice_id) { + int32 var_7 = const()[name = string("op_7"), val = int32(0)]; + tensor latent_mask_1 = not_equal(x = latent_mask, y = var_7)[name = string("latent_mask")]; + int32 var_39 = const()[name = string("op_39"), val = int32(0)]; + int32 var_41 = const()[name = string("op_41"), val = int32(12)]; + string hidden_1_pad_type_0 = const()[name = string("hidden_1_pad_type_0"), val = string("custom")]; + tensor hidden_1_pad_0 = const()[name = string("hidden_1_pad_0"), val = tensor([1, 1])]; + tensor hidden_1_strides_0 = const()[name = string("hidden_1_strides_0"), val = tensor([1])]; + tensor hidden_1_dilations_0 = const()[name = string("hidden_1_dilations_0"), val = tensor([1])]; + int32 hidden_1_groups_0 = const()[name = string("hidden_1_groups_0"), val = int32(1)]; + string latents_to_fp16_dtype_0 = const()[name = string("latents_to_fp16_dtype_0"), val = string("fp16")]; + tensor module_adapter_latent_in_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27776))))[name = string("module_adapter_latent_in_weight_to_fp16_quantized")]; + tensor module_adapter_latent_in_bias_to_fp16 = const()[name = string("module_adapter_latent_in_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28608)))]; + tensor latents_to_fp16 = cast(dtype = latents_to_fp16_dtype_0, x = latents)[name = string("cast_10")]; + tensor hidden_1_cast_fp16 = conv(bias = module_adapter_latent_in_bias_to_fp16, dilations = hidden_1_dilations_0, groups = hidden_1_groups_0, pad = hidden_1_pad_0, pad_type = hidden_1_pad_type_0, strides = hidden_1_strides_0, weight = module_adapter_latent_in_weight_to_fp16_quantized, x = latents_to_fp16)[name = string("hidden_1_cast_fp16")]; + tensor var_85 = maximum(x = emotion_id, y = var_39)[name = string("op_85")]; + tensor input_5 = minimum(x = var_85, y = var_41)[name = string("input_5")]; + int32 var_88_batch_dims_0 = const()[name = string("op_88_batch_dims_0"), val = int32(0)]; + bool var_88_validate_indices_0 = const()[name = string("op_88_validate_indices_0"), val = bool(false)]; + tensor module_adapter_voice_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(34880))))[name = string("module_adapter_voice_embed_weight_to_fp16_quantized")]; + string voice_id_to_int16_dtype_0 = const()[name = string("voice_id_to_int16_dtype_0"), val = string("int16")]; + string cast_18_dtype_0 = const()[name = string("cast_18_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)]; + tensor voice_id_to_int16 = cast(dtype = voice_id_to_int16_dtype_0, x = voice_id)[name = string("cast_9")]; + tensor cast_18 = cast(dtype = cast_18_dtype_0, x = voice_id_to_int16)[name = string("cast_8")]; + tensor greater_equal_0 = greater_equal(x = cast_18, y = greater_equal_0_y_0)[name = string("greater_equal_0")]; + int32 slice_by_index_1 = const()[name = string("slice_by_index_1"), val = int32(14)]; + tensor add_0 = add(x = cast_18, y = slice_by_index_1)[name = string("add_0")]; + tensor select_0 = select(a = cast_18, b = add_0, cond = greater_equal_0)[name = string("select_0")]; + string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")]; + string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("int32")]; + int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)]; + tensor select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_7")]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = select_0_to_int16)[name = string("cast_6")]; + tensor greater_equal_0_1 = greater_equal(x = cast_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")]; + int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(14)]; + tensor add_0_1 = add(x = cast_0, y = slice_by_index_0)[name = string("add_0_1")]; + tensor select_0_1 = select(a = cast_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")]; + int32 op_88_cast_fp16_cast_uint16_cast_uint16_axis_0 = const()[name = string("op_88_cast_fp16_cast_uint16_cast_uint16_axis_0"), val = int32(0)]; + tensor op_88_cast_fp16_cast_uint16_cast_uint16 = gather(axis = op_88_cast_fp16_cast_uint16_cast_uint16_axis_0, batch_dims = var_88_batch_dims_0, indices = select_0_1, validate_indices = var_88_validate_indices_0, x = module_adapter_voice_embed_weight_to_fp16_quantized)[name = string("op_88_cast_fp16_cast_uint16_cast_uint16")]; + int32 var_90_axis_0 = const()[name = string("op_90_axis_0"), val = int32(0)]; + int32 var_90_batch_dims_0 = const()[name = string("op_90_batch_dims_0"), val = int32(0)]; + bool var_90_validate_indices_0 = const()[name = string("op_90_validate_indices_0"), val = bool(false)]; + tensor module_adapter_language_embed_weight_to_fp16 = const()[name = string("module_adapter_language_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35008)))]; + string language_id_to_uint16_dtype_0 = const()[name = string("language_id_to_uint16_dtype_0"), val = string("uint16")]; + tensor language_id_to_uint16 = cast(dtype = language_id_to_uint16_dtype_0, x = language_id)[name = string("cast_5")]; + tensor var_90_cast_fp16_cast_uint16 = gather(axis = var_90_axis_0, batch_dims = var_90_batch_dims_0, indices = language_id_to_uint16, validate_indices = var_90_validate_indices_0, x = module_adapter_language_embed_weight_to_fp16)[name = string("op_90_cast_fp16_cast_uint16")]; + tensor var_91_cast_fp16 = add(x = op_88_cast_fp16_cast_uint16_cast_uint16, y = var_90_cast_fp16_cast_uint16)[name = string("op_91_cast_fp16")]; + int32 var_93_axis_0 = const()[name = string("op_93_axis_0"), val = int32(0)]; + int32 var_93_batch_dims_0 = const()[name = string("op_93_batch_dims_0"), val = int32(0)]; + bool var_93_validate_indices_0 = const()[name = string("op_93_validate_indices_0"), val = bool(false)]; + tensor module_adapter_emotion_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40448))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45504))))[name = string("module_adapter_emotion_embed_weight_to_fp16_quantized")]; + string input_5_to_uint16_dtype_0 = const()[name = string("input_5_to_uint16_dtype_0"), val = string("uint16")]; + tensor input_5_to_uint16 = cast(dtype = input_5_to_uint16_dtype_0, x = input_5)[name = string("cast_4")]; + tensor var_93_cast_fp16_cast_uint16 = gather(axis = var_93_axis_0, batch_dims = var_93_batch_dims_0, indices = input_5_to_uint16, validate_indices = var_93_validate_indices_0, x = module_adapter_emotion_embed_weight_to_fp16_quantized)[name = string("op_93_cast_fp16_cast_uint16")]; + tensor cond_cast_fp16 = add(x = var_91_cast_fp16, y = var_93_cast_fp16_cast_uint16)[name = string("cond_cast_fp16")]; + tensor var_95_axes_0 = const()[name = string("op_95_axes_0"), val = tensor([-1])]; + tensor var_95_cast_fp16 = expand_dims(axes = var_95_axes_0, x = cond_cast_fp16)[name = string("op_95_cast_fp16")]; + tensor x_1_cast_fp16 = add(x = hidden_1_cast_fp16, y = var_95_cast_fp16)[name = string("x_1_cast_fp16")]; + tensor var_98_axes_0 = const()[name = string("op_98_axes_0"), val = tensor([1])]; + tensor var_98 = expand_dims(axes = var_98_axes_0, x = latent_mask_1)[name = string("op_98")]; + string var_99_to_fp16_dtype_0 = const()[name = string("op_99_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_98_to_fp16 = cast(dtype = var_99_to_fp16_dtype_0, x = var_98)[name = string("cast_3")]; + tensor x_3_cast_fp16 = mul(x = x_1_cast_fp16, y = var_98_to_fp16)[name = string("x_3_cast_fp16")]; + tensor input_7_cast_fp16 = mul(x = x_3_cast_fp16, y = var_98_to_fp16)[name = string("input_7_cast_fp16")]; + string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("custom")]; + tensor x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor([2, 2])]; + tensor x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor([1])]; + int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(45632))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(782976))))[name = string("module_adapter_blocks_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783808)))]; + tensor x_5_cast_fp16 = conv(bias = module_adapter_blocks_0_net_0_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = module_adapter_blocks_0_net_0_weight_to_fp16_quantized, x = input_7_cast_fp16)[name = string("x_5_cast_fp16")]; + tensor input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor([0, 2, 1])]; + tensor work_1_axes_0 = const()[name = string("work_1_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(784640)))]; + tensor module_adapter_blocks_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(785472)))]; + fp16 var_48_to_fp16 = const()[name = string("op_48_to_fp16"), val = fp16(0x1.5p-17)]; + tensor input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_5_cast_fp16)[name = string("transpose_54")]; + tensor work_1_cast_fp16 = layer_norm(axes = work_1_axes_0, beta = module_adapter_blocks_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_1_weight_to_fp16, x = input_9_cast_fp16)[name = string("work_1_cast_fp16")]; + tensor h_1_perm_0 = const()[name = string("h_1_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(786304))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(933824))))[name = string("module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(934656)))]; + tensor linear_0_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_0_cast_fp16")]; + tensor var_131_axes_0 = const()[name = string("op_131_axes_0"), val = tensor([-1])]; + tensor var_131_cast_fp16 = expand_dims(axes = var_131_axes_0, x = linear_0_cast_fp16)[name = string("op_131_cast_fp16")]; + tensor h_1_cast_fp16 = transpose(perm = h_1_perm_0, x = work_1_cast_fp16)[name = string("transpose_53")]; + tensor input_11_cast_fp16 = add(x = h_1_cast_fp16, y = var_131_cast_fp16)[name = string("input_11_cast_fp16")]; + string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")]; + tensor input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")]; + tensor input_15_cast_fp16 = mul(x = input_13_cast_fp16, y = var_98_to_fp16)[name = string("input_15_cast_fp16")]; + string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("custom")]; + tensor x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor([2, 2])]; + tensor x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor([1])]; + tensor x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor([1])]; + int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(935488))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832))))[name = string("module_adapter_blocks_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673664)))]; + tensor x_9_cast_fp16 = conv(bias = module_adapter_blocks_0_net_4_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = module_adapter_blocks_0_net_4_weight_to_fp16_quantized, x = input_15_cast_fp16)[name = string("x_9_cast_fp16")]; + tensor input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor work_3_axes_0 = const()[name = string("work_3_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1674496)))]; + tensor module_adapter_blocks_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1675328)))]; + tensor input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = x_9_cast_fp16)[name = string("transpose_52")]; + tensor work_3_cast_fp16 = layer_norm(axes = work_3_axes_0, beta = module_adapter_blocks_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_0_net_5_weight_to_fp16, x = input_17_cast_fp16)[name = string("work_3_cast_fp16")]; + tensor h_3_perm_0 = const()[name = string("h_3_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1676160))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1823680))))[name = string("module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1824512)))]; + tensor linear_1_cast_fp16 = linear(bias = module_adapter_blocks_0_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_1_cast_fp16")]; + tensor var_155_axes_0 = const()[name = string("op_155_axes_0"), val = tensor([-1])]; + tensor var_155_cast_fp16 = expand_dims(axes = var_155_axes_0, x = linear_1_cast_fp16)[name = string("op_155_cast_fp16")]; + tensor h_3_cast_fp16 = transpose(perm = h_3_perm_0, x = work_3_cast_fp16)[name = string("transpose_51")]; + tensor x_11_cast_fp16 = add(x = h_3_cast_fp16, y = var_155_cast_fp16)[name = string("x_11_cast_fp16")]; + tensor h_5_cast_fp16 = mul(x = x_11_cast_fp16, y = var_98_to_fp16)[name = string("h_5_cast_fp16")]; + tensor input_19_cast_fp16 = add(x = input_7_cast_fp16, y = h_5_cast_fp16)[name = string("input_19_cast_fp16")]; + string x_13_mode_0 = const()[name = string("x_13_mode_0"), val = string("EXACT")]; + tensor x_13_cast_fp16 = gelu(mode = x_13_mode_0, x = input_19_cast_fp16)[name = string("x_13_cast_fp16")]; + tensor x_15_cast_fp16 = mul(x = x_13_cast_fp16, y = var_98_to_fp16)[name = string("x_15_cast_fp16")]; + tensor input_21_cast_fp16 = mul(x = x_15_cast_fp16, y = var_98_to_fp16)[name = string("input_21_cast_fp16")]; + string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("custom")]; + tensor x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor([2, 2])]; + tensor x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor([1])]; + tensor x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor([1])]; + int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1825344))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2562688))))[name = string("module_adapter_blocks_1_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2563520)))]; + tensor x_17_cast_fp16 = conv(bias = module_adapter_blocks_1_net_0_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = module_adapter_blocks_1_net_0_weight_to_fp16_quantized, x = input_21_cast_fp16)[name = string("x_17_cast_fp16")]; + tensor input_23_perm_0 = const()[name = string("input_23_perm_0"), val = tensor([0, 2, 1])]; + tensor work_5_axes_0 = const()[name = string("work_5_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2564352)))]; + tensor module_adapter_blocks_1_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2565184)))]; + tensor input_23_cast_fp16 = transpose(perm = input_23_perm_0, x = x_17_cast_fp16)[name = string("transpose_50")]; + tensor work_5_cast_fp16 = layer_norm(axes = work_5_axes_0, beta = module_adapter_blocks_1_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_1_weight_to_fp16, x = input_23_cast_fp16)[name = string("work_5_cast_fp16")]; + tensor h_7_perm_0 = const()[name = string("h_7_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2566016))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2713536))))[name = string("module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2714368)))]; + tensor linear_2_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_2_cast_fp16")]; + tensor var_200_axes_0 = const()[name = string("op_200_axes_0"), val = tensor([-1])]; + tensor var_200_cast_fp16 = expand_dims(axes = var_200_axes_0, x = linear_2_cast_fp16)[name = string("op_200_cast_fp16")]; + tensor h_7_cast_fp16 = transpose(perm = h_7_perm_0, x = work_5_cast_fp16)[name = string("transpose_49")]; + tensor input_25_cast_fp16 = add(x = h_7_cast_fp16, y = var_200_cast_fp16)[name = string("input_25_cast_fp16")]; + string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("EXACT")]; + tensor input_27_cast_fp16 = gelu(mode = input_27_mode_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")]; + tensor input_29_cast_fp16 = mul(x = input_27_cast_fp16, y = var_98_to_fp16)[name = string("input_29_cast_fp16")]; + string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("custom")]; + tensor x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor([2, 2])]; + tensor x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor([1])]; + int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_1_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2715200))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3452544))))[name = string("module_adapter_blocks_1_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3453376)))]; + tensor x_21_cast_fp16 = conv(bias = module_adapter_blocks_1_net_4_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = module_adapter_blocks_1_net_4_weight_to_fp16_quantized, x = input_29_cast_fp16)[name = string("x_21_cast_fp16")]; + tensor input_31_perm_0 = const()[name = string("input_31_perm_0"), val = tensor([0, 2, 1])]; + tensor work_7_axes_0 = const()[name = string("work_7_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_1_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3454208)))]; + tensor module_adapter_blocks_1_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455040)))]; + tensor input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = x_21_cast_fp16)[name = string("transpose_48")]; + tensor work_7_cast_fp16 = layer_norm(axes = work_7_axes_0, beta = module_adapter_blocks_1_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_1_net_5_weight_to_fp16, x = input_31_cast_fp16)[name = string("work_7_cast_fp16")]; + tensor h_9_perm_0 = const()[name = string("h_9_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3455872))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3603392))))[name = string("module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_1_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_1_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3604224)))]; + tensor linear_3_cast_fp16 = linear(bias = module_adapter_blocks_1_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_1_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_3_cast_fp16")]; + tensor var_224_axes_0 = const()[name = string("op_224_axes_0"), val = tensor([-1])]; + tensor var_224_cast_fp16 = expand_dims(axes = var_224_axes_0, x = linear_3_cast_fp16)[name = string("op_224_cast_fp16")]; + tensor h_9_cast_fp16 = transpose(perm = h_9_perm_0, x = work_7_cast_fp16)[name = string("transpose_47")]; + tensor x_23_cast_fp16 = add(x = h_9_cast_fp16, y = var_224_cast_fp16)[name = string("x_23_cast_fp16")]; + tensor h_11_cast_fp16 = mul(x = x_23_cast_fp16, y = var_98_to_fp16)[name = string("h_11_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_21_cast_fp16, y = h_11_cast_fp16)[name = string("input_33_cast_fp16")]; + string x_25_mode_0 = const()[name = string("x_25_mode_0"), val = string("EXACT")]; + tensor x_25_cast_fp16 = gelu(mode = x_25_mode_0, x = input_33_cast_fp16)[name = string("x_25_cast_fp16")]; + tensor x_27_cast_fp16 = mul(x = x_25_cast_fp16, y = var_98_to_fp16)[name = string("x_27_cast_fp16")]; + tensor input_35_cast_fp16 = mul(x = x_27_cast_fp16, y = var_98_to_fp16)[name = string("input_35_cast_fp16")]; + string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("custom")]; + tensor x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor([2, 2])]; + tensor x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor([1])]; + int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3605056))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4342400))))[name = string("module_adapter_blocks_2_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4343232)))]; + tensor x_29_cast_fp16 = conv(bias = module_adapter_blocks_2_net_0_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = module_adapter_blocks_2_net_0_weight_to_fp16_quantized, x = input_35_cast_fp16)[name = string("x_29_cast_fp16")]; + tensor input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor([0, 2, 1])]; + tensor work_9_axes_0 = const()[name = string("work_9_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344064)))]; + tensor module_adapter_blocks_2_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4344896)))]; + tensor input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = x_29_cast_fp16)[name = string("transpose_46")]; + tensor work_9_cast_fp16 = layer_norm(axes = work_9_axes_0, beta = module_adapter_blocks_2_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_1_weight_to_fp16, x = input_37_cast_fp16)[name = string("work_9_cast_fp16")]; + tensor h_13_perm_0 = const()[name = string("h_13_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4345728))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4493248))))[name = string("module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494080)))]; + tensor linear_4_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_4_cast_fp16")]; + tensor var_269_axes_0 = const()[name = string("op_269_axes_0"), val = tensor([-1])]; + tensor var_269_cast_fp16 = expand_dims(axes = var_269_axes_0, x = linear_4_cast_fp16)[name = string("op_269_cast_fp16")]; + tensor h_13_cast_fp16 = transpose(perm = h_13_perm_0, x = work_9_cast_fp16)[name = string("transpose_45")]; + tensor input_39_cast_fp16 = add(x = h_13_cast_fp16, y = var_269_cast_fp16)[name = string("input_39_cast_fp16")]; + string input_41_mode_0 = const()[name = string("input_41_mode_0"), val = string("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = input_39_cast_fp16)[name = string("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = mul(x = input_41_cast_fp16, y = var_98_to_fp16)[name = string("input_43_cast_fp16")]; + string x_33_pad_type_0 = const()[name = string("x_33_pad_type_0"), val = string("custom")]; + tensor x_33_pad_0 = const()[name = string("x_33_pad_0"), val = tensor([2, 2])]; + tensor x_33_strides_0 = const()[name = string("x_33_strides_0"), val = tensor([1])]; + tensor x_33_dilations_0 = const()[name = string("x_33_dilations_0"), val = tensor([1])]; + int32 x_33_groups_0 = const()[name = string("x_33_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_2_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4494912))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5232256))))[name = string("module_adapter_blocks_2_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233088)))]; + tensor x_33_cast_fp16 = conv(bias = module_adapter_blocks_2_net_4_bias_to_fp16, dilations = x_33_dilations_0, groups = x_33_groups_0, pad = x_33_pad_0, pad_type = x_33_pad_type_0, strides = x_33_strides_0, weight = module_adapter_blocks_2_net_4_weight_to_fp16_quantized, x = input_43_cast_fp16)[name = string("x_33_cast_fp16")]; + tensor input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor work_11_axes_0 = const()[name = string("work_11_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_2_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5233920)))]; + tensor module_adapter_blocks_2_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5234752)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_33_cast_fp16)[name = string("transpose_44")]; + tensor work_11_cast_fp16 = layer_norm(axes = work_11_axes_0, beta = module_adapter_blocks_2_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_2_net_5_weight_to_fp16, x = input_45_cast_fp16)[name = string("work_11_cast_fp16")]; + tensor h_15_perm_0 = const()[name = string("h_15_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5235584))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383104))))[name = string("module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_2_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_2_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5383936)))]; + tensor linear_5_cast_fp16 = linear(bias = module_adapter_blocks_2_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_2_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_5_cast_fp16")]; + tensor var_293_axes_0 = const()[name = string("op_293_axes_0"), val = tensor([-1])]; + tensor var_293_cast_fp16 = expand_dims(axes = var_293_axes_0, x = linear_5_cast_fp16)[name = string("op_293_cast_fp16")]; + tensor h_15_cast_fp16 = transpose(perm = h_15_perm_0, x = work_11_cast_fp16)[name = string("transpose_43")]; + tensor x_35_cast_fp16 = add(x = h_15_cast_fp16, y = var_293_cast_fp16)[name = string("x_35_cast_fp16")]; + tensor h_17_cast_fp16 = mul(x = x_35_cast_fp16, y = var_98_to_fp16)[name = string("h_17_cast_fp16")]; + tensor input_47_cast_fp16 = add(x = input_35_cast_fp16, y = h_17_cast_fp16)[name = string("input_47_cast_fp16")]; + string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("EXACT")]; + tensor x_37_cast_fp16 = gelu(mode = x_37_mode_0, x = input_47_cast_fp16)[name = string("x_37_cast_fp16")]; + tensor x_39_cast_fp16 = mul(x = x_37_cast_fp16, y = var_98_to_fp16)[name = string("x_39_cast_fp16")]; + tensor input_49_cast_fp16 = mul(x = x_39_cast_fp16, y = var_98_to_fp16)[name = string("input_49_cast_fp16")]; + string x_41_pad_type_0 = const()[name = string("x_41_pad_type_0"), val = string("custom")]; + tensor x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor([2, 2])]; + tensor x_41_strides_0 = const()[name = string("x_41_strides_0"), val = tensor([1])]; + tensor x_41_dilations_0 = const()[name = string("x_41_dilations_0"), val = tensor([1])]; + int32 x_41_groups_0 = const()[name = string("x_41_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5384768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122112))))[name = string("module_adapter_blocks_3_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6122944)))]; + tensor x_41_cast_fp16 = conv(bias = module_adapter_blocks_3_net_0_bias_to_fp16, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = module_adapter_blocks_3_net_0_weight_to_fp16_quantized, x = input_49_cast_fp16)[name = string("x_41_cast_fp16")]; + tensor input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor([0, 2, 1])]; + tensor work_13_axes_0 = const()[name = string("work_13_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6123776)))]; + tensor module_adapter_blocks_3_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6124608)))]; + tensor input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = x_41_cast_fp16)[name = string("transpose_42")]; + tensor work_13_cast_fp16 = layer_norm(axes = work_13_axes_0, beta = module_adapter_blocks_3_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_1_weight_to_fp16, x = input_51_cast_fp16)[name = string("work_13_cast_fp16")]; + tensor h_19_perm_0 = const()[name = string("h_19_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6125440))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6272960))))[name = string("module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6273792)))]; + tensor linear_6_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_6_cast_fp16")]; + tensor var_338_axes_0 = const()[name = string("op_338_axes_0"), val = tensor([-1])]; + tensor var_338_cast_fp16 = expand_dims(axes = var_338_axes_0, x = linear_6_cast_fp16)[name = string("op_338_cast_fp16")]; + tensor h_19_cast_fp16 = transpose(perm = h_19_perm_0, x = work_13_cast_fp16)[name = string("transpose_41")]; + tensor input_53_cast_fp16 = add(x = h_19_cast_fp16, y = var_338_cast_fp16)[name = string("input_53_cast_fp16")]; + string input_55_mode_0 = const()[name = string("input_55_mode_0"), val = string("EXACT")]; + tensor input_55_cast_fp16 = gelu(mode = input_55_mode_0, x = input_53_cast_fp16)[name = string("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = mul(x = input_55_cast_fp16, y = var_98_to_fp16)[name = string("input_57_cast_fp16")]; + string x_45_pad_type_0 = const()[name = string("x_45_pad_type_0"), val = string("custom")]; + tensor x_45_pad_0 = const()[name = string("x_45_pad_0"), val = tensor([2, 2])]; + tensor x_45_strides_0 = const()[name = string("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = string("x_45_dilations_0"), val = tensor([1])]; + int32 x_45_groups_0 = const()[name = string("x_45_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_3_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6274624))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7011968))))[name = string("module_adapter_blocks_3_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7012800)))]; + tensor x_45_cast_fp16 = conv(bias = module_adapter_blocks_3_net_4_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = module_adapter_blocks_3_net_4_weight_to_fp16_quantized, x = input_57_cast_fp16)[name = string("x_45_cast_fp16")]; + tensor input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor([0, 2, 1])]; + tensor work_15_axes_0 = const()[name = string("work_15_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_3_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7013632)))]; + tensor module_adapter_blocks_3_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7014464)))]; + tensor input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_45_cast_fp16)[name = string("transpose_40")]; + tensor work_15_cast_fp16 = layer_norm(axes = work_15_axes_0, beta = module_adapter_blocks_3_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_3_net_5_weight_to_fp16, x = input_59_cast_fp16)[name = string("work_15_cast_fp16")]; + tensor h_21_perm_0 = const()[name = string("h_21_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7015296))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7162816))))[name = string("module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_3_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_3_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7163648)))]; + tensor linear_7_cast_fp16 = linear(bias = module_adapter_blocks_3_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_3_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_7_cast_fp16")]; + tensor var_362_axes_0 = const()[name = string("op_362_axes_0"), val = tensor([-1])]; + tensor var_362_cast_fp16 = expand_dims(axes = var_362_axes_0, x = linear_7_cast_fp16)[name = string("op_362_cast_fp16")]; + tensor h_21_cast_fp16 = transpose(perm = h_21_perm_0, x = work_15_cast_fp16)[name = string("transpose_39")]; + tensor x_47_cast_fp16 = add(x = h_21_cast_fp16, y = var_362_cast_fp16)[name = string("x_47_cast_fp16")]; + tensor h_23_cast_fp16 = mul(x = x_47_cast_fp16, y = var_98_to_fp16)[name = string("h_23_cast_fp16")]; + tensor input_61_cast_fp16 = add(x = input_49_cast_fp16, y = h_23_cast_fp16)[name = string("input_61_cast_fp16")]; + string x_49_mode_0 = const()[name = string("x_49_mode_0"), val = string("EXACT")]; + tensor x_49_cast_fp16 = gelu(mode = x_49_mode_0, x = input_61_cast_fp16)[name = string("x_49_cast_fp16")]; + tensor x_51_cast_fp16 = mul(x = x_49_cast_fp16, y = var_98_to_fp16)[name = string("x_51_cast_fp16")]; + tensor input_63_cast_fp16 = mul(x = x_51_cast_fp16, y = var_98_to_fp16)[name = string("input_63_cast_fp16")]; + string x_53_pad_type_0 = const()[name = string("x_53_pad_type_0"), val = string("custom")]; + tensor x_53_pad_0 = const()[name = string("x_53_pad_0"), val = tensor([2, 2])]; + tensor x_53_strides_0 = const()[name = string("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = string("x_53_dilations_0"), val = tensor([1])]; + int32 x_53_groups_0 = const()[name = string("x_53_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7164480))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7901824))))[name = string("module_adapter_blocks_4_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7902656)))]; + tensor x_53_cast_fp16 = conv(bias = module_adapter_blocks_4_net_0_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = module_adapter_blocks_4_net_0_weight_to_fp16_quantized, x = input_63_cast_fp16)[name = string("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = string("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor work_17_axes_0 = const()[name = string("work_17_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7903488)))]; + tensor module_adapter_blocks_4_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7904320)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = string("transpose_38")]; + tensor work_17_cast_fp16 = layer_norm(axes = work_17_axes_0, beta = module_adapter_blocks_4_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_1_weight_to_fp16, x = input_65_cast_fp16)[name = string("work_17_cast_fp16")]; + tensor h_25_perm_0 = const()[name = string("h_25_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7905152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8052672))))[name = string("module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8053504)))]; + tensor linear_8_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_8_cast_fp16")]; + tensor var_407_axes_0 = const()[name = string("op_407_axes_0"), val = tensor([-1])]; + tensor var_407_cast_fp16 = expand_dims(axes = var_407_axes_0, x = linear_8_cast_fp16)[name = string("op_407_cast_fp16")]; + tensor h_25_cast_fp16 = transpose(perm = h_25_perm_0, x = work_17_cast_fp16)[name = string("transpose_37")]; + tensor input_67_cast_fp16 = add(x = h_25_cast_fp16, y = var_407_cast_fp16)[name = string("input_67_cast_fp16")]; + string input_69_mode_0 = const()[name = string("input_69_mode_0"), val = string("EXACT")]; + tensor input_69_cast_fp16 = gelu(mode = input_69_mode_0, x = input_67_cast_fp16)[name = string("input_69_cast_fp16")]; + tensor input_71_cast_fp16 = mul(x = input_69_cast_fp16, y = var_98_to_fp16)[name = string("input_71_cast_fp16")]; + string x_57_pad_type_0 = const()[name = string("x_57_pad_type_0"), val = string("custom")]; + tensor x_57_pad_0 = const()[name = string("x_57_pad_0"), val = tensor([2, 2])]; + tensor x_57_strides_0 = const()[name = string("x_57_strides_0"), val = tensor([1])]; + tensor x_57_dilations_0 = const()[name = string("x_57_dilations_0"), val = tensor([1])]; + int32 x_57_groups_0 = const()[name = string("x_57_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_4_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8054336))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8791680))))[name = string("module_adapter_blocks_4_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8792512)))]; + tensor x_57_cast_fp16 = conv(bias = module_adapter_blocks_4_net_4_bias_to_fp16, dilations = x_57_dilations_0, groups = x_57_groups_0, pad = x_57_pad_0, pad_type = x_57_pad_type_0, strides = x_57_strides_0, weight = module_adapter_blocks_4_net_4_weight_to_fp16_quantized, x = input_71_cast_fp16)[name = string("x_57_cast_fp16")]; + tensor input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor([0, 2, 1])]; + tensor work_19_axes_0 = const()[name = string("work_19_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_4_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8793344)))]; + tensor module_adapter_blocks_4_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8794176)))]; + tensor input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_57_cast_fp16)[name = string("transpose_36")]; + tensor work_19_cast_fp16 = layer_norm(axes = work_19_axes_0, beta = module_adapter_blocks_4_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_4_net_5_weight_to_fp16, x = input_73_cast_fp16)[name = string("work_19_cast_fp16")]; + tensor h_27_perm_0 = const()[name = string("h_27_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8795008))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8942528))))[name = string("module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_4_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_4_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8943360)))]; + tensor linear_9_cast_fp16 = linear(bias = module_adapter_blocks_4_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_4_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_9_cast_fp16")]; + tensor var_431_axes_0 = const()[name = string("op_431_axes_0"), val = tensor([-1])]; + tensor var_431_cast_fp16 = expand_dims(axes = var_431_axes_0, x = linear_9_cast_fp16)[name = string("op_431_cast_fp16")]; + tensor h_27_cast_fp16 = transpose(perm = h_27_perm_0, x = work_19_cast_fp16)[name = string("transpose_35")]; + tensor x_59_cast_fp16 = add(x = h_27_cast_fp16, y = var_431_cast_fp16)[name = string("x_59_cast_fp16")]; + tensor h_29_cast_fp16 = mul(x = x_59_cast_fp16, y = var_98_to_fp16)[name = string("h_29_cast_fp16")]; + tensor input_75_cast_fp16 = add(x = input_63_cast_fp16, y = h_29_cast_fp16)[name = string("input_75_cast_fp16")]; + string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("EXACT")]; + tensor x_61_cast_fp16 = gelu(mode = x_61_mode_0, x = input_75_cast_fp16)[name = string("x_61_cast_fp16")]; + tensor x_63_cast_fp16 = mul(x = x_61_cast_fp16, y = var_98_to_fp16)[name = string("x_63_cast_fp16")]; + tensor input_77_cast_fp16 = mul(x = x_63_cast_fp16, y = var_98_to_fp16)[name = string("input_77_cast_fp16")]; + string x_65_pad_type_0 = const()[name = string("x_65_pad_type_0"), val = string("custom")]; + tensor x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor([2, 2])]; + tensor x_65_strides_0 = const()[name = string("x_65_strides_0"), val = tensor([1])]; + tensor x_65_dilations_0 = const()[name = string("x_65_dilations_0"), val = tensor([1])]; + int32 x_65_groups_0 = const()[name = string("x_65_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8944192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9681536))))[name = string("module_adapter_blocks_5_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_0_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9682368)))]; + tensor x_65_cast_fp16 = conv(bias = module_adapter_blocks_5_net_0_bias_to_fp16, dilations = x_65_dilations_0, groups = x_65_groups_0, pad = x_65_pad_0, pad_type = x_65_pad_type_0, strides = x_65_strides_0, weight = module_adapter_blocks_5_net_0_weight_to_fp16_quantized, x = input_77_cast_fp16)[name = string("x_65_cast_fp16")]; + tensor input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor([0, 2, 1])]; + tensor work_21_axes_0 = const()[name = string("work_21_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_1_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9683200)))]; + tensor module_adapter_blocks_5_net_1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684032)))]; + tensor input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_65_cast_fp16)[name = string("transpose_34")]; + tensor work_21_cast_fp16 = layer_norm(axes = work_21_axes_0, beta = module_adapter_blocks_5_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_1_weight_to_fp16, x = input_79_cast_fp16)[name = string("work_21_cast_fp16")]; + tensor h_31_perm_0 = const()[name = string("h_31_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9684864))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9832384))))[name = string("module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9833216)))]; + tensor linear_10_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj1_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_10_cast_fp16")]; + tensor var_476_axes_0 = const()[name = string("op_476_axes_0"), val = tensor([-1])]; + tensor var_476_cast_fp16 = expand_dims(axes = var_476_axes_0, x = linear_10_cast_fp16)[name = string("op_476_cast_fp16")]; + tensor h_31_cast_fp16 = transpose(perm = h_31_perm_0, x = work_21_cast_fp16)[name = string("transpose_33")]; + tensor input_81_cast_fp16 = add(x = h_31_cast_fp16, y = var_476_cast_fp16)[name = string("input_81_cast_fp16")]; + string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")]; + tensor input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = input_81_cast_fp16)[name = string("input_83_cast_fp16")]; + tensor input_85_cast_fp16 = mul(x = input_83_cast_fp16, y = var_98_to_fp16)[name = string("input_85_cast_fp16")]; + string x_69_pad_type_0 = const()[name = string("x_69_pad_type_0"), val = string("custom")]; + tensor x_69_pad_0 = const()[name = string("x_69_pad_0"), val = tensor([2, 2])]; + tensor x_69_strides_0 = const()[name = string("x_69_strides_0"), val = tensor([1])]; + tensor x_69_dilations_0 = const()[name = string("x_69_dilations_0"), val = tensor([1])]; + int32 x_69_groups_0 = const()[name = string("x_69_groups_0"), val = int32(1)]; + tensor module_adapter_blocks_5_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9834048))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10571392))))[name = string("module_adapter_blocks_5_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_net_4_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10572224)))]; + tensor x_69_cast_fp16 = conv(bias = module_adapter_blocks_5_net_4_bias_to_fp16, dilations = x_69_dilations_0, groups = x_69_groups_0, pad = x_69_pad_0, pad_type = x_69_pad_type_0, strides = x_69_strides_0, weight = module_adapter_blocks_5_net_4_weight_to_fp16_quantized, x = input_85_cast_fp16)[name = string("x_69_cast_fp16")]; + tensor input_87_perm_0 = const()[name = string("input_87_perm_0"), val = tensor([0, 2, 1])]; + tensor work_23_axes_0 = const()[name = string("work_23_axes_0"), val = tensor([-1])]; + tensor module_adapter_blocks_5_net_5_weight_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573056)))]; + tensor module_adapter_blocks_5_net_5_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10573888)))]; + tensor input_87_cast_fp16 = transpose(perm = input_87_perm_0, x = x_69_cast_fp16)[name = string("transpose_32")]; + tensor work_23_cast_fp16 = layer_norm(axes = work_23_axes_0, beta = module_adapter_blocks_5_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_blocks_5_net_5_weight_to_fp16, x = input_87_cast_fp16)[name = string("work_23_cast_fp16")]; + tensor h_33_perm_0 = const()[name = string("h_33_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10574720))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10722240))))[name = string("module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_blocks_5_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_blocks_5_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723072)))]; + tensor linear_11_cast_fp16 = linear(bias = module_adapter_blocks_5_cond_proj2_bias_to_fp16, weight = module_adapter_blocks_5_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_11_cast_fp16")]; + tensor var_500_axes_0 = const()[name = string("op_500_axes_0"), val = tensor([-1])]; + tensor var_500_cast_fp16 = expand_dims(axes = var_500_axes_0, x = linear_11_cast_fp16)[name = string("op_500_cast_fp16")]; + tensor h_33_cast_fp16 = transpose(perm = h_33_perm_0, x = work_23_cast_fp16)[name = string("transpose_31")]; + tensor x_71_cast_fp16 = add(x = h_33_cast_fp16, y = var_500_cast_fp16)[name = string("x_71_cast_fp16")]; + tensor h_35_cast_fp16 = mul(x = x_71_cast_fp16, y = var_98_to_fp16)[name = string("h_35_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = input_77_cast_fp16, y = h_35_cast_fp16)[name = string("input_89_cast_fp16")]; + string x_73_mode_0 = const()[name = string("x_73_mode_0"), val = string("EXACT")]; + tensor x_73_cast_fp16 = gelu(mode = x_73_mode_0, x = input_89_cast_fp16)[name = string("x_73_cast_fp16")]; + tensor input_91_cast_fp16 = mul(x = x_73_cast_fp16, y = var_98_to_fp16)[name = string("input_91_cast_fp16")]; + string upsampled_pad_type_0 = const()[name = string("upsampled_pad_type_0"), val = string("custom")]; + tensor upsampled_pad_0 = const()[name = string("upsampled_pad_0"), val = tensor([1, 1])]; + tensor upsampled_strides_0 = const()[name = string("upsampled_strides_0"), val = tensor([2])]; + tensor upsampled_dilations_0 = const()[name = string("upsampled_dilations_0"), val = tensor([1])]; + int32 upsampled_groups_0 = const()[name = string("upsampled_groups_0"), val = int32(1)]; + tensor upsampled_has_output_shape_output_shape_0 = const()[name = string("upsampled_has_output_shape_output_shape_0"), val = tensor([1, 384, 1536])]; + tensor module_adapter_upsample_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10723904))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11313792))))[name = string("module_adapter_upsample_weight_to_fp16_quantized")]; + tensor module_adapter_upsample_bias_to_fp16 = const()[name = string("module_adapter_upsample_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11314624)))]; + tensor upsampled_has_output_shape_cast_fp16 = conv_transpose(bias = module_adapter_upsample_bias_to_fp16, dilations = upsampled_dilations_0, groups = upsampled_groups_0, output_shape = upsampled_has_output_shape_output_shape_0, pad = upsampled_pad_0, pad_type = upsampled_pad_type_0, strides = upsampled_strides_0, weight = module_adapter_upsample_weight_to_fp16_quantized, x = input_91_cast_fp16)[name = string("upsampled_has_output_shape_cast_fp16")]; + tensor transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor([1, 0])]; + tensor tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor([2, 1])]; + tensor transpose_0 = transpose(perm = transpose_0_perm_0, x = latent_mask_1)[name = string("transpose_30")]; + tensor tile_0 = tile(reps = tile_0_reps_0, x = transpose_0)[name = string("tile_0")]; + tensor concat_0 = const()[name = string("concat_0"), val = tensor([2, 768, 1])]; + tensor reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")]; + tensor transpose_1_perm_0 = const()[name = string("transpose_1_perm_0"), val = tensor([1, 0, 2])]; + tensor concat_1 = const()[name = string("concat_1"), val = tensor([-1, 1])]; + tensor transpose_1 = transpose(perm = transpose_1_perm_0, x = reshape_0)[name = string("transpose_29")]; + tensor reshape_1 = reshape(shape = concat_1, x = transpose_1)[name = string("reshape_1")]; + tensor mask_53_perm_0 = const()[name = string("mask_53_perm_0"), val = tensor([1, 0])]; + tensor var_524_axes_0 = const()[name = string("op_524_axes_0"), val = tensor([1])]; + tensor mask_53 = transpose(perm = mask_53_perm_0, x = reshape_1)[name = string("transpose_28")]; + tensor var_524 = expand_dims(axes = var_524_axes_0, x = mask_53)[name = string("op_524")]; + string var_525_to_fp16_dtype_0 = const()[name = string("op_525_to_fp16_dtype_0"), val = string("fp16")]; + tensor var_524_to_fp16 = cast(dtype = var_525_to_fp16_dtype_0, x = var_524)[name = string("cast_2")]; + tensor x_75_cast_fp16 = mul(x = upsampled_has_output_shape_cast_fp16, y = var_524_to_fp16)[name = string("x_75_cast_fp16")]; + tensor input_93_cast_fp16 = mul(x = x_75_cast_fp16, y = var_524_to_fp16)[name = string("input_93_cast_fp16")]; + string x_77_pad_type_0 = const()[name = string("x_77_pad_type_0"), val = string("custom")]; + tensor x_77_pad_0 = const()[name = string("x_77_pad_0"), val = tensor([2, 2])]; + tensor x_77_strides_0 = const()[name = string("x_77_strides_0"), val = tensor([1])]; + tensor x_77_dilations_0 = const()[name = string("x_77_dilations_0"), val = tensor([1])]; + int32 x_77_groups_0 = const()[name = string("x_77_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_0_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11315456))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12052800))))[name = string("module_adapter_post_0_net_0_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_0_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_0_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12053632)))]; + tensor x_77_cast_fp16 = conv(bias = module_adapter_post_0_net_0_bias_to_fp16, dilations = x_77_dilations_0, groups = x_77_groups_0, pad = x_77_pad_0, pad_type = x_77_pad_type_0, strides = x_77_strides_0, weight = module_adapter_post_0_net_0_weight_to_fp16_quantized, x = input_93_cast_fp16)[name = string("x_77_cast_fp16")]; + tensor input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor([0, 2, 1])]; + tensor work_25_axes_0 = const()[name = string("work_25_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_1_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_1_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12054464)))]; + tensor module_adapter_post_0_net_1_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12055296)))]; + tensor input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_77_cast_fp16)[name = string("transpose_27")]; + tensor work_25_cast_fp16 = layer_norm(axes = work_25_axes_0, beta = module_adapter_post_0_net_1_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_1_weight_to_fp16, x = input_95_cast_fp16)[name = string("work_25_cast_fp16")]; + tensor h_37_perm_0 = const()[name = string("h_37_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12056128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12203648))))[name = string("module_adapter_post_0_cond_proj1_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj1_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12204480)))]; + tensor linear_12_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj1_bias_to_fp16, weight = module_adapter_post_0_cond_proj1_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_12_cast_fp16")]; + tensor var_557_axes_0 = const()[name = string("op_557_axes_0"), val = tensor([-1])]; + tensor var_557_cast_fp16 = expand_dims(axes = var_557_axes_0, x = linear_12_cast_fp16)[name = string("op_557_cast_fp16")]; + tensor h_37_cast_fp16 = transpose(perm = h_37_perm_0, x = work_25_cast_fp16)[name = string("transpose_26")]; + tensor input_97_cast_fp16 = add(x = h_37_cast_fp16, y = var_557_cast_fp16)[name = string("input_97_cast_fp16")]; + string input_99_mode_0 = const()[name = string("input_99_mode_0"), val = string("EXACT")]; + tensor input_99_cast_fp16 = gelu(mode = input_99_mode_0, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")]; + tensor input_101_cast_fp16 = mul(x = input_99_cast_fp16, y = var_524_to_fp16)[name = string("input_101_cast_fp16")]; + string x_81_pad_type_0 = const()[name = string("x_81_pad_type_0"), val = string("custom")]; + tensor x_81_pad_0 = const()[name = string("x_81_pad_0"), val = tensor([2, 2])]; + tensor x_81_strides_0 = const()[name = string("x_81_strides_0"), val = tensor([1])]; + tensor x_81_dilations_0 = const()[name = string("x_81_dilations_0"), val = tensor([1])]; + int32 x_81_groups_0 = const()[name = string("x_81_groups_0"), val = int32(1)]; + tensor module_adapter_post_0_net_4_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12205312))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12942656))))[name = string("module_adapter_post_0_net_4_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_net_4_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_4_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12943488)))]; + tensor x_81_cast_fp16 = conv(bias = module_adapter_post_0_net_4_bias_to_fp16, dilations = x_81_dilations_0, groups = x_81_groups_0, pad = x_81_pad_0, pad_type = x_81_pad_type_0, strides = x_81_strides_0, weight = module_adapter_post_0_net_4_weight_to_fp16_quantized, x = input_101_cast_fp16)[name = string("x_81_cast_fp16")]; + tensor input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor([0, 2, 1])]; + tensor work_axes_0 = const()[name = string("work_axes_0"), val = tensor([-1])]; + tensor module_adapter_post_0_net_5_weight_to_fp16 = const()[name = string("module_adapter_post_0_net_5_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12944320)))]; + tensor module_adapter_post_0_net_5_bias_to_fp16 = const()[name = string("module_adapter_post_0_net_5_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945152)))]; + tensor input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_81_cast_fp16)[name = string("transpose_25")]; + tensor work_cast_fp16 = layer_norm(axes = work_axes_0, beta = module_adapter_post_0_net_5_bias_to_fp16, epsilon = var_48_to_fp16, gamma = module_adapter_post_0_net_5_weight_to_fp16, x = input_103_cast_fp16)[name = string("work_cast_fp16")]; + tensor h_39_perm_0 = const()[name = string("h_39_perm_0"), val = tensor([0, 2, 1])]; + tensor module_adapter_post_0_cond_proj2_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12945984))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13093504))))[name = string("module_adapter_post_0_cond_proj2_weight_to_fp16_quantized")]; + tensor module_adapter_post_0_cond_proj2_bias_to_fp16 = const()[name = string("module_adapter_post_0_cond_proj2_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13094336)))]; + tensor linear_13_cast_fp16 = linear(bias = module_adapter_post_0_cond_proj2_bias_to_fp16, weight = module_adapter_post_0_cond_proj2_weight_to_fp16_quantized, x = cond_cast_fp16)[name = string("linear_13_cast_fp16")]; + tensor var_581_axes_0 = const()[name = string("op_581_axes_0"), val = tensor([-1])]; + tensor var_581_cast_fp16 = expand_dims(axes = var_581_axes_0, x = linear_13_cast_fp16)[name = string("op_581_cast_fp16")]; + tensor h_39_cast_fp16 = transpose(perm = h_39_perm_0, x = work_cast_fp16)[name = string("transpose_24")]; + tensor x_83_cast_fp16 = add(x = h_39_cast_fp16, y = var_581_cast_fp16)[name = string("x_83_cast_fp16")]; + tensor h_cast_fp16 = mul(x = x_83_cast_fp16, y = var_524_to_fp16)[name = string("h_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = input_93_cast_fp16, y = h_cast_fp16)[name = string("input_105_cast_fp16")]; + string x_85_mode_0 = const()[name = string("x_85_mode_0"), val = string("EXACT")]; + tensor x_85_cast_fp16 = gelu(mode = x_85_mode_0, x = input_105_cast_fp16)[name = string("x_85_cast_fp16")]; + tensor input_107_cast_fp16 = mul(x = x_85_cast_fp16, y = var_524_to_fp16)[name = string("input_107_cast_fp16")]; + string x_87_pad_type_0 = const()[name = string("x_87_pad_type_0"), val = string("custom")]; + tensor x_87_pad_0 = const()[name = string("x_87_pad_0"), val = tensor([1, 1])]; + tensor x_87_strides_0 = const()[name = string("x_87_strides_0"), val = tensor([1])]; + tensor x_87_dilations_0 = const()[name = string("x_87_dilations_0"), val = tensor([1])]; + int32 x_87_groups_0 = const()[name = string("x_87_groups_0"), val = int32(1)]; + tensor module_adapter_post_1_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13095168))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210432))))[name = string("module_adapter_post_1_weight_to_fp16_quantized")]; + tensor module_adapter_post_1_bias_to_fp16 = const()[name = string("module_adapter_post_1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13210752)))]; + tensor x_87_cast_fp16 = conv(bias = module_adapter_post_1_bias_to_fp16, dilations = x_87_dilations_0, groups = x_87_groups_0, pad = x_87_pad_0, pad_type = x_87_pad_type_0, strides = x_87_strides_0, weight = module_adapter_post_1_weight_to_fp16_quantized, x = input_107_cast_fp16)[name = string("x_87_cast_fp16")]; + tensor input_109_cast_fp16 = mul(x = x_87_cast_fp16, y = var_524_to_fp16)[name = string("input_109_cast_fp16")]; + string x_89_pad_type_0 = const()[name = string("x_89_pad_type_0"), val = string("custom")]; + tensor x_89_pad_0 = const()[name = string("x_89_pad_0"), val = tensor([3, 3])]; + tensor x_89_strides_0 = const()[name = string("x_89_strides_0"), val = tensor([1])]; + tensor x_89_dilations_0 = const()[name = string("x_89_dilations_0"), val = tensor([1])]; + int32 x_89_groups_0 = const()[name = string("x_89_groups_0"), val = int32(1)]; + tensor module_backbone_embed_weight_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13211072))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13569536))))[name = string("module_backbone_embed_weight_to_fp16_quantized")]; + tensor module_backbone_embed_bias_to_fp16 = const()[name = string("module_backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13570624)))]; + tensor x_89_cast_fp16 = conv(bias = module_backbone_embed_bias_to_fp16, dilations = x_89_dilations_0, groups = x_89_groups_0, pad = x_89_pad_0, pad_type = x_89_pad_type_0, strides = x_89_strides_0, weight = module_backbone_embed_weight_to_fp16_quantized, x = input_109_cast_fp16)[name = string("x_89_cast_fp16")]; + tensor input_111_perm_0 = const()[name = string("input_111_perm_0"), val = tensor([0, 2, 1])]; + tensor var_637_axes_0 = const()[name = string("op_637_axes_0"), val = tensor([-1])]; + tensor module_backbone_norm_weight_to_fp16 = const()[name = string("module_backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13571712)))]; + tensor module_backbone_norm_bias_to_fp16 = const()[name = string("module_backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13572800)))]; + fp16 var_36_to_fp16 = const()[name = string("op_36_to_fp16"), val = fp16(0x1.1p-20)]; + tensor input_111_cast_fp16 = transpose(perm = input_111_perm_0, x = x_89_cast_fp16)[name = string("transpose_23")]; + tensor var_637_cast_fp16 = layer_norm(axes = var_637_axes_0, beta = module_backbone_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_norm_weight_to_fp16, x = input_111_cast_fp16)[name = string("op_637_cast_fp16")]; + tensor input_113_perm_0 = const()[name = string("input_113_perm_0"), val = tensor([0, 2, 1])]; + string x_91_pad_type_0 = const()[name = string("x_91_pad_type_0"), val = string("custom")]; + tensor x_91_pad_0 = const()[name = string("x_91_pad_0"), val = tensor([3, 3])]; + int32 x_91_groups_0 = const()[name = string("x_91_groups_0"), val = int32(512)]; + tensor x_91_strides_0 = const()[name = string("x_91_strides_0"), val = tensor([1])]; + tensor x_91_dilations_0 = const()[name = string("x_91_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13573888)))]; + tensor module_backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13581120)))]; + tensor input_113_cast_fp16 = transpose(perm = input_113_perm_0, x = var_637_cast_fp16)[name = string("transpose_22")]; + tensor x_91_cast_fp16 = conv(bias = module_backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_91_dilations_0, groups = x_91_groups_0, pad = x_91_pad_0, pad_type = x_91_pad_type_0, strides = x_91_strides_0, weight = module_backbone_convnext_0_dwconv_weight_to_fp16, x = input_113_cast_fp16)[name = string("x_91_cast_fp16")]; + tensor input_115_perm_0 = const()[name = string("input_115_perm_0"), val = tensor([0, 2, 1])]; + tensor var_661_axes_0 = const()[name = string("op_661_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_0_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13582208)))]; + tensor module_backbone_convnext_0_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13583296)))]; + tensor input_115_cast_fp16 = transpose(perm = input_115_perm_0, x = x_91_cast_fp16)[name = string("transpose_21")]; + tensor var_661_cast_fp16 = layer_norm(axes = var_661_axes_0, beta = module_backbone_convnext_0_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_0_norm_weight_to_fp16, x = input_115_cast_fp16)[name = string("op_661_cast_fp16")]; + tensor x_93_perm_0 = const()[name = string("x_93_perm_0"), val = tensor([0, 2, 1])]; + string input_117_pad_type_0 = const()[name = string("input_117_pad_type_0"), val = string("valid")]; + tensor input_117_strides_0 = const()[name = string("input_117_strides_0"), val = tensor([1])]; + tensor input_117_pad_0 = const()[name = string("input_117_pad_0"), val = tensor([0, 0])]; + tensor input_117_dilations_0 = const()[name = string("input_117_dilations_0"), val = tensor([1])]; + int32 input_117_groups_0 = const()[name = string("input_117_groups_0"), val = int32(1)]; + tensor op_663_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13584384))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14370880))))[name = string("op_663_to_fp16_quantized")]; + tensor module_backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14374016)))]; + tensor x_93_cast_fp16 = transpose(perm = x_93_perm_0, x = var_661_cast_fp16)[name = string("transpose_20")]; + tensor input_117_cast_fp16 = conv(bias = module_backbone_convnext_0_pwconv1_bias_to_fp16, dilations = input_117_dilations_0, groups = input_117_groups_0, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = input_117_strides_0, weight = op_663_to_fp16_quantized, x = x_93_cast_fp16)[name = string("input_117_cast_fp16")]; + string x_95_mode_0 = const()[name = string("x_95_mode_0"), val = string("EXACT")]; + tensor x_95_cast_fp16 = gelu(mode = x_95_mode_0, x = input_117_cast_fp16)[name = string("x_95_cast_fp16")]; + string x_97_pad_type_0 = const()[name = string("x_97_pad_type_0"), val = string("valid")]; + tensor x_97_strides_0 = const()[name = string("x_97_strides_0"), val = tensor([1])]; + tensor x_97_pad_0 = const()[name = string("x_97_pad_0"), val = tensor([0, 0])]; + tensor x_97_dilations_0 = const()[name = string("x_97_dilations_0"), val = tensor([1])]; + int32 x_97_groups_0 = const()[name = string("x_97_groups_0"), val = int32(1)]; + tensor x_99_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14377152))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15163648))))[name = string("x_99_weight_0_to_fp16_quantized")]; + tensor x_99_bias_0_to_fp16 = const()[name = string("x_99_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15164736)))]; + tensor x_99_cast_fp16 = conv(bias = x_99_bias_0_to_fp16, dilations = x_97_dilations_0, groups = x_97_groups_0, pad = x_97_pad_0, pad_type = x_97_pad_type_0, strides = x_97_strides_0, weight = x_99_weight_0_to_fp16_quantized, x = x_95_cast_fp16)[name = string("x_99_cast_fp16")]; + tensor input_119_cast_fp16 = add(x = input_113_cast_fp16, y = x_99_cast_fp16)[name = string("input_119_cast_fp16")]; + string x_101_pad_type_0 = const()[name = string("x_101_pad_type_0"), val = string("custom")]; + tensor x_101_pad_0 = const()[name = string("x_101_pad_0"), val = tensor([3, 3])]; + int32 x_101_groups_0 = const()[name = string("x_101_groups_0"), val = int32(512)]; + tensor x_101_strides_0 = const()[name = string("x_101_strides_0"), val = tensor([1])]; + tensor x_101_dilations_0 = const()[name = string("x_101_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15165824)))]; + tensor module_backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15173056)))]; + tensor x_101_cast_fp16 = conv(bias = module_backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_101_dilations_0, groups = x_101_groups_0, pad = x_101_pad_0, pad_type = x_101_pad_type_0, strides = x_101_strides_0, weight = module_backbone_convnext_1_dwconv_weight_to_fp16, x = input_119_cast_fp16)[name = string("x_101_cast_fp16")]; + tensor input_121_perm_0 = const()[name = string("input_121_perm_0"), val = tensor([0, 2, 1])]; + tensor var_702_axes_0 = const()[name = string("op_702_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_1_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15174144)))]; + tensor module_backbone_convnext_1_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15175232)))]; + tensor input_121_cast_fp16 = transpose(perm = input_121_perm_0, x = x_101_cast_fp16)[name = string("transpose_19")]; + tensor var_702_cast_fp16 = layer_norm(axes = var_702_axes_0, beta = module_backbone_convnext_1_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_1_norm_weight_to_fp16, x = input_121_cast_fp16)[name = string("op_702_cast_fp16")]; + tensor x_103_perm_0 = const()[name = string("x_103_perm_0"), val = tensor([0, 2, 1])]; + string input_123_pad_type_0 = const()[name = string("input_123_pad_type_0"), val = string("valid")]; + tensor input_123_strides_0 = const()[name = string("input_123_strides_0"), val = tensor([1])]; + tensor input_123_pad_0 = const()[name = string("input_123_pad_0"), val = tensor([0, 0])]; + tensor input_123_dilations_0 = const()[name = string("input_123_dilations_0"), val = tensor([1])]; + int32 input_123_groups_0 = const()[name = string("input_123_groups_0"), val = int32(1)]; + tensor op_704_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15176320))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15962816))))[name = string("op_704_to_fp16_quantized")]; + tensor module_backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15965952)))]; + tensor x_103_cast_fp16 = transpose(perm = x_103_perm_0, x = var_702_cast_fp16)[name = string("transpose_18")]; + tensor input_123_cast_fp16 = conv(bias = module_backbone_convnext_1_pwconv1_bias_to_fp16, dilations = input_123_dilations_0, groups = input_123_groups_0, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = input_123_strides_0, weight = op_704_to_fp16_quantized, x = x_103_cast_fp16)[name = string("input_123_cast_fp16")]; + string x_105_mode_0 = const()[name = string("x_105_mode_0"), val = string("EXACT")]; + tensor x_105_cast_fp16 = gelu(mode = x_105_mode_0, x = input_123_cast_fp16)[name = string("x_105_cast_fp16")]; + string x_107_pad_type_0 = const()[name = string("x_107_pad_type_0"), val = string("valid")]; + tensor x_107_strides_0 = const()[name = string("x_107_strides_0"), val = tensor([1])]; + tensor x_107_pad_0 = const()[name = string("x_107_pad_0"), val = tensor([0, 0])]; + tensor x_107_dilations_0 = const()[name = string("x_107_dilations_0"), val = tensor([1])]; + int32 x_107_groups_0 = const()[name = string("x_107_groups_0"), val = int32(1)]; + tensor x_109_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(15969088))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16755584))))[name = string("x_109_weight_0_to_fp16_quantized")]; + tensor x_109_bias_0_to_fp16 = const()[name = string("x_109_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16756672)))]; + tensor x_109_cast_fp16 = conv(bias = x_109_bias_0_to_fp16, dilations = x_107_dilations_0, groups = x_107_groups_0, pad = x_107_pad_0, pad_type = x_107_pad_type_0, strides = x_107_strides_0, weight = x_109_weight_0_to_fp16_quantized, x = x_105_cast_fp16)[name = string("x_109_cast_fp16")]; + tensor input_125_cast_fp16 = add(x = input_119_cast_fp16, y = x_109_cast_fp16)[name = string("input_125_cast_fp16")]; + string x_111_pad_type_0 = const()[name = string("x_111_pad_type_0"), val = string("custom")]; + tensor x_111_pad_0 = const()[name = string("x_111_pad_0"), val = tensor([3, 3])]; + int32 x_111_groups_0 = const()[name = string("x_111_groups_0"), val = int32(512)]; + tensor x_111_strides_0 = const()[name = string("x_111_strides_0"), val = tensor([1])]; + tensor x_111_dilations_0 = const()[name = string("x_111_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16757760)))]; + tensor module_backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16764992)))]; + tensor x_111_cast_fp16 = conv(bias = module_backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_111_dilations_0, groups = x_111_groups_0, pad = x_111_pad_0, pad_type = x_111_pad_type_0, strides = x_111_strides_0, weight = module_backbone_convnext_2_dwconv_weight_to_fp16, x = input_125_cast_fp16)[name = string("x_111_cast_fp16")]; + tensor input_127_perm_0 = const()[name = string("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor var_743_axes_0 = const()[name = string("op_743_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_2_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16766080)))]; + tensor module_backbone_convnext_2_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16767168)))]; + tensor input_127_cast_fp16 = transpose(perm = input_127_perm_0, x = x_111_cast_fp16)[name = string("transpose_17")]; + tensor var_743_cast_fp16 = layer_norm(axes = var_743_axes_0, beta = module_backbone_convnext_2_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_2_norm_weight_to_fp16, x = input_127_cast_fp16)[name = string("op_743_cast_fp16")]; + tensor x_113_perm_0 = const()[name = string("x_113_perm_0"), val = tensor([0, 2, 1])]; + string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("valid")]; + tensor input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor([1])]; + tensor input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor([0, 0])]; + tensor input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor([1])]; + int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; + tensor op_745_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16768256))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17554752))))[name = string("op_745_to_fp16_quantized")]; + tensor module_backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17557888)))]; + tensor x_113_cast_fp16 = transpose(perm = x_113_perm_0, x = var_743_cast_fp16)[name = string("transpose_16")]; + tensor input_129_cast_fp16 = conv(bias = module_backbone_convnext_2_pwconv1_bias_to_fp16, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = op_745_to_fp16_quantized, x = x_113_cast_fp16)[name = string("input_129_cast_fp16")]; + string x_115_mode_0 = const()[name = string("x_115_mode_0"), val = string("EXACT")]; + tensor x_115_cast_fp16 = gelu(mode = x_115_mode_0, x = input_129_cast_fp16)[name = string("x_115_cast_fp16")]; + string x_117_pad_type_0 = const()[name = string("x_117_pad_type_0"), val = string("valid")]; + tensor x_117_strides_0 = const()[name = string("x_117_strides_0"), val = tensor([1])]; + tensor x_117_pad_0 = const()[name = string("x_117_pad_0"), val = tensor([0, 0])]; + tensor x_117_dilations_0 = const()[name = string("x_117_dilations_0"), val = tensor([1])]; + int32 x_117_groups_0 = const()[name = string("x_117_groups_0"), val = int32(1)]; + tensor x_119_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17561024))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18347520))))[name = string("x_119_weight_0_to_fp16_quantized")]; + tensor x_119_bias_0_to_fp16 = const()[name = string("x_119_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18348608)))]; + tensor x_119_cast_fp16 = conv(bias = x_119_bias_0_to_fp16, dilations = x_117_dilations_0, groups = x_117_groups_0, pad = x_117_pad_0, pad_type = x_117_pad_type_0, strides = x_117_strides_0, weight = x_119_weight_0_to_fp16_quantized, x = x_115_cast_fp16)[name = string("x_119_cast_fp16")]; + tensor input_131_cast_fp16 = add(x = input_125_cast_fp16, y = x_119_cast_fp16)[name = string("input_131_cast_fp16")]; + string x_121_pad_type_0 = const()[name = string("x_121_pad_type_0"), val = string("custom")]; + tensor x_121_pad_0 = const()[name = string("x_121_pad_0"), val = tensor([3, 3])]; + int32 x_121_groups_0 = const()[name = string("x_121_groups_0"), val = int32(512)]; + tensor x_121_strides_0 = const()[name = string("x_121_strides_0"), val = tensor([1])]; + tensor x_121_dilations_0 = const()[name = string("x_121_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18349696)))]; + tensor module_backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18356928)))]; + tensor x_121_cast_fp16 = conv(bias = module_backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_121_dilations_0, groups = x_121_groups_0, pad = x_121_pad_0, pad_type = x_121_pad_type_0, strides = x_121_strides_0, weight = module_backbone_convnext_3_dwconv_weight_to_fp16, x = input_131_cast_fp16)[name = string("x_121_cast_fp16")]; + tensor input_133_perm_0 = const()[name = string("input_133_perm_0"), val = tensor([0, 2, 1])]; + tensor var_784_axes_0 = const()[name = string("op_784_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_3_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18358016)))]; + tensor module_backbone_convnext_3_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18359104)))]; + tensor input_133_cast_fp16 = transpose(perm = input_133_perm_0, x = x_121_cast_fp16)[name = string("transpose_15")]; + tensor var_784_cast_fp16 = layer_norm(axes = var_784_axes_0, beta = module_backbone_convnext_3_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_3_norm_weight_to_fp16, x = input_133_cast_fp16)[name = string("op_784_cast_fp16")]; + tensor x_123_perm_0 = const()[name = string("x_123_perm_0"), val = tensor([0, 2, 1])]; + string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("valid")]; + tensor input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor([1])]; + tensor input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor([0, 0])]; + tensor input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor([1])]; + int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; + tensor op_786_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18360192))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19146688))))[name = string("op_786_to_fp16_quantized")]; + tensor module_backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19149824)))]; + tensor x_123_cast_fp16 = transpose(perm = x_123_perm_0, x = var_784_cast_fp16)[name = string("transpose_14")]; + tensor input_135_cast_fp16 = conv(bias = module_backbone_convnext_3_pwconv1_bias_to_fp16, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = op_786_to_fp16_quantized, x = x_123_cast_fp16)[name = string("input_135_cast_fp16")]; + string x_125_mode_0 = const()[name = string("x_125_mode_0"), val = string("EXACT")]; + tensor x_125_cast_fp16 = gelu(mode = x_125_mode_0, x = input_135_cast_fp16)[name = string("x_125_cast_fp16")]; + string x_127_pad_type_0 = const()[name = string("x_127_pad_type_0"), val = string("valid")]; + tensor x_127_strides_0 = const()[name = string("x_127_strides_0"), val = tensor([1])]; + tensor x_127_pad_0 = const()[name = string("x_127_pad_0"), val = tensor([0, 0])]; + tensor x_127_dilations_0 = const()[name = string("x_127_dilations_0"), val = tensor([1])]; + int32 x_127_groups_0 = const()[name = string("x_127_groups_0"), val = int32(1)]; + tensor x_129_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19152960))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19939456))))[name = string("x_129_weight_0_to_fp16_quantized")]; + tensor x_129_bias_0_to_fp16 = const()[name = string("x_129_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19940544)))]; + tensor x_129_cast_fp16 = conv(bias = x_129_bias_0_to_fp16, dilations = x_127_dilations_0, groups = x_127_groups_0, pad = x_127_pad_0, pad_type = x_127_pad_type_0, strides = x_127_strides_0, weight = x_129_weight_0_to_fp16_quantized, x = x_125_cast_fp16)[name = string("x_129_cast_fp16")]; + tensor input_137_cast_fp16 = add(x = input_131_cast_fp16, y = x_129_cast_fp16)[name = string("input_137_cast_fp16")]; + string x_131_pad_type_0 = const()[name = string("x_131_pad_type_0"), val = string("custom")]; + tensor x_131_pad_0 = const()[name = string("x_131_pad_0"), val = tensor([3, 3])]; + int32 x_131_groups_0 = const()[name = string("x_131_groups_0"), val = int32(512)]; + tensor x_131_strides_0 = const()[name = string("x_131_strides_0"), val = tensor([1])]; + tensor x_131_dilations_0 = const()[name = string("x_131_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19941632)))]; + tensor module_backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19948864)))]; + tensor x_131_cast_fp16 = conv(bias = module_backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_131_dilations_0, groups = x_131_groups_0, pad = x_131_pad_0, pad_type = x_131_pad_type_0, strides = x_131_strides_0, weight = module_backbone_convnext_4_dwconv_weight_to_fp16, x = input_137_cast_fp16)[name = string("x_131_cast_fp16")]; + tensor input_139_perm_0 = const()[name = string("input_139_perm_0"), val = tensor([0, 2, 1])]; + tensor var_825_axes_0 = const()[name = string("op_825_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_4_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19949952)))]; + tensor module_backbone_convnext_4_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19951040)))]; + tensor input_139_cast_fp16 = transpose(perm = input_139_perm_0, x = x_131_cast_fp16)[name = string("transpose_13")]; + tensor var_825_cast_fp16 = layer_norm(axes = var_825_axes_0, beta = module_backbone_convnext_4_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_4_norm_weight_to_fp16, x = input_139_cast_fp16)[name = string("op_825_cast_fp16")]; + tensor x_133_perm_0 = const()[name = string("x_133_perm_0"), val = tensor([0, 2, 1])]; + string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("valid")]; + tensor input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor([1])]; + tensor input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor([0, 0])]; + tensor input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor([1])]; + int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)]; + tensor op_827_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19952128))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20738624))))[name = string("op_827_to_fp16_quantized")]; + tensor module_backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20741760)))]; + tensor x_133_cast_fp16 = transpose(perm = x_133_perm_0, x = var_825_cast_fp16)[name = string("transpose_12")]; + tensor input_141_cast_fp16 = conv(bias = module_backbone_convnext_4_pwconv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = op_827_to_fp16_quantized, x = x_133_cast_fp16)[name = string("input_141_cast_fp16")]; + string x_135_mode_0 = const()[name = string("x_135_mode_0"), val = string("EXACT")]; + tensor x_135_cast_fp16 = gelu(mode = x_135_mode_0, x = input_141_cast_fp16)[name = string("x_135_cast_fp16")]; + string x_137_pad_type_0 = const()[name = string("x_137_pad_type_0"), val = string("valid")]; + tensor x_137_strides_0 = const()[name = string("x_137_strides_0"), val = tensor([1])]; + tensor x_137_pad_0 = const()[name = string("x_137_pad_0"), val = tensor([0, 0])]; + tensor x_137_dilations_0 = const()[name = string("x_137_dilations_0"), val = tensor([1])]; + int32 x_137_groups_0 = const()[name = string("x_137_groups_0"), val = int32(1)]; + tensor x_139_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20744896))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21531392))))[name = string("x_139_weight_0_to_fp16_quantized")]; + tensor x_139_bias_0_to_fp16 = const()[name = string("x_139_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21532480)))]; + tensor x_139_cast_fp16 = conv(bias = x_139_bias_0_to_fp16, dilations = x_137_dilations_0, groups = x_137_groups_0, pad = x_137_pad_0, pad_type = x_137_pad_type_0, strides = x_137_strides_0, weight = x_139_weight_0_to_fp16_quantized, x = x_135_cast_fp16)[name = string("x_139_cast_fp16")]; + tensor input_143_cast_fp16 = add(x = input_137_cast_fp16, y = x_139_cast_fp16)[name = string("input_143_cast_fp16")]; + string x_141_pad_type_0 = const()[name = string("x_141_pad_type_0"), val = string("custom")]; + tensor x_141_pad_0 = const()[name = string("x_141_pad_0"), val = tensor([3, 3])]; + int32 x_141_groups_0 = const()[name = string("x_141_groups_0"), val = int32(512)]; + tensor x_141_strides_0 = const()[name = string("x_141_strides_0"), val = tensor([1])]; + tensor x_141_dilations_0 = const()[name = string("x_141_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21533568)))]; + tensor module_backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21540800)))]; + tensor x_141_cast_fp16 = conv(bias = module_backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_141_dilations_0, groups = x_141_groups_0, pad = x_141_pad_0, pad_type = x_141_pad_type_0, strides = x_141_strides_0, weight = module_backbone_convnext_5_dwconv_weight_to_fp16, x = input_143_cast_fp16)[name = string("x_141_cast_fp16")]; + tensor input_145_perm_0 = const()[name = string("input_145_perm_0"), val = tensor([0, 2, 1])]; + tensor var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_5_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21541888)))]; + tensor module_backbone_convnext_5_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21542976)))]; + tensor input_145_cast_fp16 = transpose(perm = input_145_perm_0, x = x_141_cast_fp16)[name = string("transpose_11")]; + tensor var_866_cast_fp16 = layer_norm(axes = var_866_axes_0, beta = module_backbone_convnext_5_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_5_norm_weight_to_fp16, x = input_145_cast_fp16)[name = string("op_866_cast_fp16")]; + tensor x_143_perm_0 = const()[name = string("x_143_perm_0"), val = tensor([0, 2, 1])]; + string input_147_pad_type_0 = const()[name = string("input_147_pad_type_0"), val = string("valid")]; + tensor input_147_strides_0 = const()[name = string("input_147_strides_0"), val = tensor([1])]; + tensor input_147_pad_0 = const()[name = string("input_147_pad_0"), val = tensor([0, 0])]; + tensor input_147_dilations_0 = const()[name = string("input_147_dilations_0"), val = tensor([1])]; + int32 input_147_groups_0 = const()[name = string("input_147_groups_0"), val = int32(1)]; + tensor op_868_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21544064))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22330560))))[name = string("op_868_to_fp16_quantized")]; + tensor module_backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22333696)))]; + tensor x_143_cast_fp16 = transpose(perm = x_143_perm_0, x = var_866_cast_fp16)[name = string("transpose_10")]; + tensor input_147_cast_fp16 = conv(bias = module_backbone_convnext_5_pwconv1_bias_to_fp16, dilations = input_147_dilations_0, groups = input_147_groups_0, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = input_147_strides_0, weight = op_868_to_fp16_quantized, x = x_143_cast_fp16)[name = string("input_147_cast_fp16")]; + string x_145_mode_0 = const()[name = string("x_145_mode_0"), val = string("EXACT")]; + tensor x_145_cast_fp16 = gelu(mode = x_145_mode_0, x = input_147_cast_fp16)[name = string("x_145_cast_fp16")]; + string x_147_pad_type_0 = const()[name = string("x_147_pad_type_0"), val = string("valid")]; + tensor x_147_strides_0 = const()[name = string("x_147_strides_0"), val = tensor([1])]; + tensor x_147_pad_0 = const()[name = string("x_147_pad_0"), val = tensor([0, 0])]; + tensor x_147_dilations_0 = const()[name = string("x_147_dilations_0"), val = tensor([1])]; + int32 x_147_groups_0 = const()[name = string("x_147_groups_0"), val = int32(1)]; + tensor x_149_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22336832))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23123328))))[name = string("x_149_weight_0_to_fp16_quantized")]; + tensor x_149_bias_0_to_fp16 = const()[name = string("x_149_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23124416)))]; + tensor x_149_cast_fp16 = conv(bias = x_149_bias_0_to_fp16, dilations = x_147_dilations_0, groups = x_147_groups_0, pad = x_147_pad_0, pad_type = x_147_pad_type_0, strides = x_147_strides_0, weight = x_149_weight_0_to_fp16_quantized, x = x_145_cast_fp16)[name = string("x_149_cast_fp16")]; + tensor input_149_cast_fp16 = add(x = input_143_cast_fp16, y = x_149_cast_fp16)[name = string("input_149_cast_fp16")]; + string x_151_pad_type_0 = const()[name = string("x_151_pad_type_0"), val = string("custom")]; + tensor x_151_pad_0 = const()[name = string("x_151_pad_0"), val = tensor([3, 3])]; + int32 x_151_groups_0 = const()[name = string("x_151_groups_0"), val = int32(512)]; + tensor x_151_strides_0 = const()[name = string("x_151_strides_0"), val = tensor([1])]; + tensor x_151_dilations_0 = const()[name = string("x_151_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23125504)))]; + tensor module_backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23132736)))]; + tensor x_151_cast_fp16 = conv(bias = module_backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_151_dilations_0, groups = x_151_groups_0, pad = x_151_pad_0, pad_type = x_151_pad_type_0, strides = x_151_strides_0, weight = module_backbone_convnext_6_dwconv_weight_to_fp16, x = input_149_cast_fp16)[name = string("x_151_cast_fp16")]; + tensor input_151_perm_0 = const()[name = string("input_151_perm_0"), val = tensor([0, 2, 1])]; + tensor var_907_axes_0 = const()[name = string("op_907_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_6_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23133824)))]; + tensor module_backbone_convnext_6_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23134912)))]; + tensor input_151_cast_fp16 = transpose(perm = input_151_perm_0, x = x_151_cast_fp16)[name = string("transpose_9")]; + tensor var_907_cast_fp16 = layer_norm(axes = var_907_axes_0, beta = module_backbone_convnext_6_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_6_norm_weight_to_fp16, x = input_151_cast_fp16)[name = string("op_907_cast_fp16")]; + tensor x_153_perm_0 = const()[name = string("x_153_perm_0"), val = tensor([0, 2, 1])]; + string input_153_pad_type_0 = const()[name = string("input_153_pad_type_0"), val = string("valid")]; + tensor input_153_strides_0 = const()[name = string("input_153_strides_0"), val = tensor([1])]; + tensor input_153_pad_0 = const()[name = string("input_153_pad_0"), val = tensor([0, 0])]; + tensor input_153_dilations_0 = const()[name = string("input_153_dilations_0"), val = tensor([1])]; + int32 input_153_groups_0 = const()[name = string("input_153_groups_0"), val = int32(1)]; + tensor op_909_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23136000))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23922496))))[name = string("op_909_to_fp16_quantized")]; + tensor module_backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23925632)))]; + tensor x_153_cast_fp16 = transpose(perm = x_153_perm_0, x = var_907_cast_fp16)[name = string("transpose_8")]; + tensor input_153_cast_fp16 = conv(bias = module_backbone_convnext_6_pwconv1_bias_to_fp16, dilations = input_153_dilations_0, groups = input_153_groups_0, pad = input_153_pad_0, pad_type = input_153_pad_type_0, strides = input_153_strides_0, weight = op_909_to_fp16_quantized, x = x_153_cast_fp16)[name = string("input_153_cast_fp16")]; + string x_155_mode_0 = const()[name = string("x_155_mode_0"), val = string("EXACT")]; + tensor x_155_cast_fp16 = gelu(mode = x_155_mode_0, x = input_153_cast_fp16)[name = string("x_155_cast_fp16")]; + string x_157_pad_type_0 = const()[name = string("x_157_pad_type_0"), val = string("valid")]; + tensor x_157_strides_0 = const()[name = string("x_157_strides_0"), val = tensor([1])]; + tensor x_157_pad_0 = const()[name = string("x_157_pad_0"), val = tensor([0, 0])]; + tensor x_157_dilations_0 = const()[name = string("x_157_dilations_0"), val = tensor([1])]; + int32 x_157_groups_0 = const()[name = string("x_157_groups_0"), val = int32(1)]; + tensor x_159_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23928768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24715264))))[name = string("x_159_weight_0_to_fp16_quantized")]; + tensor x_159_bias_0_to_fp16 = const()[name = string("x_159_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24716352)))]; + tensor x_159_cast_fp16 = conv(bias = x_159_bias_0_to_fp16, dilations = x_157_dilations_0, groups = x_157_groups_0, pad = x_157_pad_0, pad_type = x_157_pad_type_0, strides = x_157_strides_0, weight = x_159_weight_0_to_fp16_quantized, x = x_155_cast_fp16)[name = string("x_159_cast_fp16")]; + tensor input_155_cast_fp16 = add(x = input_149_cast_fp16, y = x_159_cast_fp16)[name = string("input_155_cast_fp16")]; + string x_161_pad_type_0 = const()[name = string("x_161_pad_type_0"), val = string("custom")]; + tensor x_161_pad_0 = const()[name = string("x_161_pad_0"), val = tensor([3, 3])]; + int32 x_161_groups_0 = const()[name = string("x_161_groups_0"), val = int32(512)]; + tensor x_161_strides_0 = const()[name = string("x_161_strides_0"), val = tensor([1])]; + tensor x_161_dilations_0 = const()[name = string("x_161_dilations_0"), val = tensor([1])]; + tensor module_backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24717440)))]; + tensor module_backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24724672)))]; + tensor x_161_cast_fp16 = conv(bias = module_backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_161_dilations_0, groups = x_161_groups_0, pad = x_161_pad_0, pad_type = x_161_pad_type_0, strides = x_161_strides_0, weight = module_backbone_convnext_7_dwconv_weight_to_fp16, x = input_155_cast_fp16)[name = string("x_161_cast_fp16")]; + tensor input_157_perm_0 = const()[name = string("input_157_perm_0"), val = tensor([0, 2, 1])]; + tensor var_948_axes_0 = const()[name = string("op_948_axes_0"), val = tensor([-1])]; + tensor module_backbone_convnext_7_norm_weight_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24725760)))]; + tensor module_backbone_convnext_7_norm_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24726848)))]; + tensor input_157_cast_fp16 = transpose(perm = input_157_perm_0, x = x_161_cast_fp16)[name = string("transpose_7")]; + tensor var_948_cast_fp16 = layer_norm(axes = var_948_axes_0, beta = module_backbone_convnext_7_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_convnext_7_norm_weight_to_fp16, x = input_157_cast_fp16)[name = string("op_948_cast_fp16")]; + tensor x_163_perm_0 = const()[name = string("x_163_perm_0"), val = tensor([0, 2, 1])]; + string input_159_pad_type_0 = const()[name = string("input_159_pad_type_0"), val = string("valid")]; + tensor input_159_strides_0 = const()[name = string("input_159_strides_0"), val = tensor([1])]; + tensor input_159_pad_0 = const()[name = string("input_159_pad_0"), val = tensor([0, 0])]; + tensor input_159_dilations_0 = const()[name = string("input_159_dilations_0"), val = tensor([1])]; + int32 input_159_groups_0 = const()[name = string("input_159_groups_0"), val = int32(1)]; + tensor op_950_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24727936))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25514432))))[name = string("op_950_to_fp16_quantized")]; + tensor module_backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = string("module_backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25517568)))]; + tensor x_163_cast_fp16 = transpose(perm = x_163_perm_0, x = var_948_cast_fp16)[name = string("transpose_6")]; + tensor input_159_cast_fp16 = conv(bias = module_backbone_convnext_7_pwconv1_bias_to_fp16, dilations = input_159_dilations_0, groups = input_159_groups_0, pad = input_159_pad_0, pad_type = input_159_pad_type_0, strides = input_159_strides_0, weight = op_950_to_fp16_quantized, x = x_163_cast_fp16)[name = string("input_159_cast_fp16")]; + string x_165_mode_0 = const()[name = string("x_165_mode_0"), val = string("EXACT")]; + tensor x_165_cast_fp16 = gelu(mode = x_165_mode_0, x = input_159_cast_fp16)[name = string("x_165_cast_fp16")]; + string x_167_pad_type_0 = const()[name = string("x_167_pad_type_0"), val = string("valid")]; + tensor x_167_strides_0 = const()[name = string("x_167_strides_0"), val = tensor([1])]; + tensor x_167_pad_0 = const()[name = string("x_167_pad_0"), val = tensor([0, 0])]; + tensor x_167_dilations_0 = const()[name = string("x_167_dilations_0"), val = tensor([1])]; + int32 x_167_groups_0 = const()[name = string("x_167_groups_0"), val = int32(1)]; + tensor x_169_weight_0_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25520704))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26307200))))[name = string("x_169_weight_0_to_fp16_quantized")]; + tensor x_169_bias_0_to_fp16 = const()[name = string("x_169_bias_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26308288)))]; + tensor x_169_cast_fp16 = conv(bias = x_169_bias_0_to_fp16, dilations = x_167_dilations_0, groups = x_167_groups_0, pad = x_167_pad_0, pad_type = x_167_pad_type_0, strides = x_167_strides_0, weight = x_169_weight_0_to_fp16_quantized, x = x_165_cast_fp16)[name = string("x_169_cast_fp16")]; + tensor x_cast_fp16 = add(x = input_155_cast_fp16, y = x_169_cast_fp16)[name = string("x_cast_fp16")]; + tensor input_161_perm_0 = const()[name = string("input_161_perm_0"), val = tensor([0, 2, 1])]; + tensor hidden_axes_0 = const()[name = string("hidden_axes_0"), val = tensor([-1])]; + tensor module_backbone_final_layer_norm_weight_to_fp16 = const()[name = string("module_backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26309376)))]; + tensor module_backbone_final_layer_norm_bias_to_fp16 = const()[name = string("module_backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26310464)))]; + tensor input_161_cast_fp16 = transpose(perm = input_161_perm_0, x = x_cast_fp16)[name = string("transpose_5")]; + tensor hidden_cast_fp16 = layer_norm(axes = hidden_axes_0, beta = module_backbone_final_layer_norm_bias_to_fp16, epsilon = var_36_to_fp16, gamma = module_backbone_final_layer_norm_weight_to_fp16, x = input_161_cast_fp16)[name = string("hidden_cast_fp16")]; + tensor var_977_perm_0 = const()[name = string("op_977_perm_0"), val = tensor([0, 2, 1])]; + string values_pad_type_0 = const()[name = string("values_pad_type_0"), val = string("valid")]; + tensor values_strides_0 = const()[name = string("values_strides_0"), val = tensor([1])]; + tensor values_pad_0 = const()[name = string("values_pad_0"), val = tensor([0, 0])]; + tensor values_dilations_0 = const()[name = string("values_dilations_0"), val = tensor([1])]; + int32 values_groups_0 = const()[name = string("values_groups_0"), val = int32(1)]; + tensor op_978_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26311552))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26836928))))[name = string("op_978_to_fp16_quantized")]; + tensor module_head_out_bias_to_fp16 = const()[name = string("module_head_out_bias_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26839104)))]; + tensor var_977_cast_fp16 = transpose(perm = var_977_perm_0, x = hidden_cast_fp16)[name = string("transpose_4")]; + tensor values_cast_fp16 = conv(bias = module_head_out_bias_to_fp16, dilations = values_dilations_0, groups = values_groups_0, pad = values_pad_0, pad_type = values_pad_type_0, strides = values_strides_0, weight = op_978_to_fp16_quantized, x = var_977_cast_fp16)[name = string("values_cast_fp16")]; + tensor var_984_split_sizes_0 = const()[name = string("op_984_split_sizes_0"), val = tensor([513, 513])]; + int32 var_984_axis_0 = const()[name = string("op_984_axis_0"), val = int32(1)]; + tensor var_984_cast_fp16_0, tensor var_984_cast_fp16_1 = split(axis = var_984_axis_0, split_sizes = var_984_split_sizes_0, x = values_cast_fp16)[name = string("op_984_cast_fp16")]; + tensor var_986_cast_fp16 = exp(x = var_984_cast_fp16_0)[name = string("op_986_cast_fp16")]; + fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(-inf)]; + fp16 var_26_to_fp16 = const()[name = string("op_26_to_fp16"), val = fp16(0x1.9p+6)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_26_to_fp16, x = var_986_cast_fp16)[name = string("clip_0_cast_fp16")]; + tensor var_988_cast_fp16 = cos(x = var_984_cast_fp16_1)[name = string("op_988_cast_fp16")]; + tensor real_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_988_cast_fp16)[name = string("real_cast_fp16")]; + tensor var_990_cast_fp16 = sin(x = var_984_cast_fp16_1)[name = string("op_990_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = clip_0_cast_fp16, y = var_990_cast_fp16)[name = string("imag_cast_fp16")]; + string frames_1_pad_type_0 = const()[name = string("frames_1_pad_type_0"), val = string("valid")]; + tensor frames_1_strides_0 = const()[name = string("frames_1_strides_0"), val = tensor([1])]; + tensor frames_1_pad_0 = const()[name = string("frames_1_pad_0"), val = tensor([0, 0])]; + tensor frames_1_dilations_0 = const()[name = string("frames_1_dilations_0"), val = tensor([1])]; + int32 frames_1_groups_0 = const()[name = string("frames_1_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_real_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26841280))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27366656))))[name = string("module_head_istft_basis_real_conv_to_fp16_quantized")]; + tensor frames_1_cast_fp16 = conv(dilations = frames_1_dilations_0, groups = frames_1_groups_0, pad = frames_1_pad_0, pad_type = frames_1_pad_type_0, strides = frames_1_strides_0, weight = module_head_istft_basis_real_conv_to_fp16_quantized, x = real_cast_fp16)[name = string("frames_1_cast_fp16")]; + string var_1005_pad_type_0 = const()[name = string("op_1005_pad_type_0"), val = string("valid")]; + tensor var_1005_strides_0 = const()[name = string("op_1005_strides_0"), val = tensor([1])]; + tensor var_1005_pad_0 = const()[name = string("op_1005_pad_0"), val = tensor([0, 0])]; + tensor var_1005_dilations_0 = const()[name = string("op_1005_dilations_0"), val = tensor([1])]; + int32 var_1005_groups_0 = const()[name = string("op_1005_groups_0"), val = int32(1)]; + tensor module_head_istft_basis_imag_conv_to_fp16_quantized = constexpr_blockwise_shift_scale(data = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27368768))), scale = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27894144))))[name = string("module_head_istft_basis_imag_conv_to_fp16_quantized")]; + tensor var_1005_cast_fp16 = conv(dilations = var_1005_dilations_0, groups = var_1005_groups_0, pad = var_1005_pad_0, pad_type = var_1005_pad_type_0, strides = var_1005_strides_0, weight = module_head_istft_basis_imag_conv_to_fp16_quantized, x = imag_cast_fp16)[name = string("op_1005_cast_fp16")]; + tensor frames_3_cast_fp16 = add(x = frames_1_cast_fp16, y = var_1005_cast_fp16)[name = string("frames_3_cast_fp16")]; + tensor var_1009_to_fp16 = const()[name = string("op_1009_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27896256)))]; + tensor frames_5_cast_fp16 = mul(x = frames_3_cast_fp16, y = var_1009_to_fp16)[name = string("frames_5_cast_fp16")]; + tensor var_1012 = const()[name = string("op_1012"), val = tensor([-1, 4, 256, 1536])]; + tensor pieces_cast_fp16 = reshape(shape = var_1012, x = frames_5_cast_fp16)[name = string("pieces_cast_fp16")]; + tensor var_1015_begin_0 = const()[name = string("op_1015_begin_0"), val = tensor([0, 0, 0, 0])]; + tensor var_1015_end_0 = const()[name = string("op_1015_end_0"), val = tensor([1, 1, 256, 1536])]; + tensor var_1015_end_mask_0 = const()[name = string("op_1015_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1015_squeeze_mask_0 = const()[name = string("op_1015_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1015_cast_fp16 = slice_by_index(begin = var_1015_begin_0, end = var_1015_end_0, end_mask = var_1015_end_mask_0, squeeze_mask = var_1015_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1015_cast_fp16")]; + tensor var_1018_perm_0 = const()[name = string("op_1018_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1019 = const()[name = string("op_1019"), val = tensor([-1, 393216])]; + tensor var_1018_cast_fp16 = transpose(perm = var_1018_perm_0, x = var_1015_cast_fp16)[name = string("transpose_3")]; + tensor stream_1_cast_fp16 = reshape(shape = var_1019, x = var_1018_cast_fp16)[name = string("stream_1_cast_fp16")]; + tensor input_163_axes_0 = const()[name = string("input_163_axes_0"), val = tensor([1])]; + tensor input_163_cast_fp16 = expand_dims(axes = input_163_axes_0, x = stream_1_cast_fp16)[name = string("input_163_cast_fp16")]; + tensor var_1023_pad_0 = const()[name = string("op_1023_pad_0"), val = tensor([0, 0, 0, 0, 0, 768])]; + string var_1023_mode_0 = const()[name = string("op_1023_mode_0"), val = string("constant")]; + fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1023_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = var_1023_mode_0, pad = var_1023_pad_0, x = input_163_cast_fp16)[name = string("op_1023_cast_fp16")]; + tensor audio_1_axes_0 = const()[name = string("audio_1_axes_0"), val = tensor([1])]; + tensor audio_1_cast_fp16 = squeeze(axes = audio_1_axes_0, x = var_1023_cast_fp16)[name = string("audio_1_cast_fp16")]; + tensor var_1026_begin_0 = const()[name = string("op_1026_begin_0"), val = tensor([0, 1, 0, 0])]; + tensor var_1026_end_0 = const()[name = string("op_1026_end_0"), val = tensor([1, 2, 256, 1536])]; + tensor var_1026_end_mask_0 = const()[name = string("op_1026_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1026_squeeze_mask_0 = const()[name = string("op_1026_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1026_cast_fp16 = slice_by_index(begin = var_1026_begin_0, end = var_1026_end_0, end_mask = var_1026_end_mask_0, squeeze_mask = var_1026_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1026_cast_fp16")]; + tensor var_1029_perm_0 = const()[name = string("op_1029_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1030 = const()[name = string("op_1030"), val = tensor([-1, 393216])]; + tensor var_1029_cast_fp16 = transpose(perm = var_1029_perm_0, x = var_1026_cast_fp16)[name = string("transpose_2")]; + tensor stream_3_cast_fp16 = reshape(shape = var_1030, x = var_1029_cast_fp16)[name = string("stream_3_cast_fp16")]; + tensor input_165_axes_0 = const()[name = string("input_165_axes_0"), val = tensor([1])]; + tensor input_165_cast_fp16 = expand_dims(axes = input_165_axes_0, x = stream_3_cast_fp16)[name = string("input_165_cast_fp16")]; + tensor var_1034_pad_0 = const()[name = string("op_1034_pad_0"), val = tensor([0, 0, 0, 0, 256, 512])]; + string var_1034_mode_0 = const()[name = string("op_1034_mode_0"), val = string("constant")]; + fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1034_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = var_1034_mode_0, pad = var_1034_pad_0, x = input_165_cast_fp16)[name = string("op_1034_cast_fp16")]; + tensor stream_5_axes_0 = const()[name = string("stream_5_axes_0"), val = tensor([1])]; + tensor stream_5_cast_fp16 = squeeze(axes = stream_5_axes_0, x = var_1034_cast_fp16)[name = string("stream_5_cast_fp16")]; + tensor audio_3_cast_fp16 = add(x = audio_1_cast_fp16, y = stream_5_cast_fp16)[name = string("audio_3_cast_fp16")]; + tensor var_1038_begin_0 = const()[name = string("op_1038_begin_0"), val = tensor([0, 2, 0, 0])]; + tensor var_1038_end_0 = const()[name = string("op_1038_end_0"), val = tensor([1, 3, 256, 1536])]; + tensor var_1038_end_mask_0 = const()[name = string("op_1038_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1038_squeeze_mask_0 = const()[name = string("op_1038_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1038_cast_fp16 = slice_by_index(begin = var_1038_begin_0, end = var_1038_end_0, end_mask = var_1038_end_mask_0, squeeze_mask = var_1038_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1038_cast_fp16")]; + tensor var_1041_perm_0 = const()[name = string("op_1041_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1042 = const()[name = string("op_1042"), val = tensor([-1, 393216])]; + tensor var_1041_cast_fp16 = transpose(perm = var_1041_perm_0, x = var_1038_cast_fp16)[name = string("transpose_1")]; + tensor stream_7_cast_fp16 = reshape(shape = var_1042, x = var_1041_cast_fp16)[name = string("stream_7_cast_fp16")]; + tensor input_167_axes_0 = const()[name = string("input_167_axes_0"), val = tensor([1])]; + tensor input_167_cast_fp16 = expand_dims(axes = input_167_axes_0, x = stream_7_cast_fp16)[name = string("input_167_cast_fp16")]; + tensor var_1046_pad_0 = const()[name = string("op_1046_pad_0"), val = tensor([0, 0, 0, 0, 512, 256])]; + string var_1046_mode_0 = const()[name = string("op_1046_mode_0"), val = string("constant")]; + fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1046_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = var_1046_mode_0, pad = var_1046_pad_0, x = input_167_cast_fp16)[name = string("op_1046_cast_fp16")]; + tensor stream_9_axes_0 = const()[name = string("stream_9_axes_0"), val = tensor([1])]; + tensor stream_9_cast_fp16 = squeeze(axes = stream_9_axes_0, x = var_1046_cast_fp16)[name = string("stream_9_cast_fp16")]; + tensor audio_5_cast_fp16 = add(x = audio_3_cast_fp16, y = stream_9_cast_fp16)[name = string("audio_5_cast_fp16")]; + tensor var_1050_begin_0 = const()[name = string("op_1050_begin_0"), val = tensor([0, 3, 0, 0])]; + tensor var_1050_end_0 = const()[name = string("op_1050_end_0"), val = tensor([1, 4, 256, 1536])]; + tensor var_1050_end_mask_0 = const()[name = string("op_1050_end_mask_0"), val = tensor([true, false, true, true])]; + tensor var_1050_squeeze_mask_0 = const()[name = string("op_1050_squeeze_mask_0"), val = tensor([false, true, false, false])]; + tensor var_1050_cast_fp16 = slice_by_index(begin = var_1050_begin_0, end = var_1050_end_0, end_mask = var_1050_end_mask_0, squeeze_mask = var_1050_squeeze_mask_0, x = pieces_cast_fp16)[name = string("op_1050_cast_fp16")]; + tensor var_1053_perm_0 = const()[name = string("op_1053_perm_0"), val = tensor([0, 2, 1])]; + tensor var_1054 = const()[name = string("op_1054"), val = tensor([-1, 393216])]; + tensor var_1053_cast_fp16 = transpose(perm = var_1053_perm_0, x = var_1050_cast_fp16)[name = string("transpose_0")]; + tensor stream_11_cast_fp16 = reshape(shape = var_1054, x = var_1053_cast_fp16)[name = string("stream_11_cast_fp16")]; + tensor input_axes_0 = const()[name = string("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = stream_11_cast_fp16)[name = string("input_cast_fp16")]; + tensor var_1058_pad_0 = const()[name = string("op_1058_pad_0"), val = tensor([0, 0, 0, 0, 768, 0])]; + string var_1058_mode_0 = const()[name = string("op_1058_mode_0"), val = string("constant")]; + fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)]; + tensor var_1058_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = var_1058_mode_0, pad = var_1058_pad_0, x = input_cast_fp16)[name = string("op_1058_cast_fp16")]; + tensor stream_axes_0 = const()[name = string("stream_axes_0"), val = tensor([1])]; + tensor stream_cast_fp16 = squeeze(axes = stream_axes_0, x = var_1058_cast_fp16)[name = string("stream_cast_fp16")]; + tensor audio_7_cast_fp16 = add(x = audio_5_cast_fp16, y = stream_cast_fp16)[name = string("audio_7_cast_fp16")]; + tensor audio_begin_0 = const()[name = string("audio_begin_0"), val = tensor([0, 512])]; + tensor audio_end_0 = const()[name = string("audio_end_0"), val = tensor([1, 393472])]; + tensor audio_end_mask_0 = const()[name = string("audio_end_mask_0"), val = tensor([true, false])]; + tensor audio_cast_fp16 = slice_by_index(begin = audio_begin_0, end = audio_end_0, end_mask = audio_end_mask_0, x = audio_7_cast_fp16)[name = string("audio_cast_fp16")]; + tensor _inversed_1063_y_0_to_fp16 = const()[name = string("_inversed_1063_y_0_to_fp16"), val = tensor(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29207296)))]; + tensor _inversed_1063_cast_fp16 = mul(x = audio_cast_fp16, y = _inversed_1063_y_0_to_fp16)[name = string("_inversed_1063_cast_fp16")]; + fp16 var_25_to_fp16 = const()[name = string("op_25_to_fp16"), val = fp16(-0x1p+0)]; + fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1p+0)]; + tensor clip_1_cast_fp16 = clip(alpha = var_25_to_fp16, beta = var_24_to_fp16, x = _inversed_1063_cast_fp16)[name = string("clip_1_cast_fp16")]; + string clip_1_cast_fp16_to_fp32_dtype_0 = const()[name = string("clip_1_cast_fp16_to_fp32_dtype_0"), val = string("fp32")]; + tensor audio = cast(dtype = clip_1_cast_fp16_to_fp32_dtype_0, x = clip_1_cast_fp16)[name = string("cast_1")]; + } -> (audio); +} \ No newline at end of file diff --git a/coreml/coreml/vocoder.mlmodelc/weights/weight.bin b/coreml/coreml/vocoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..d06a87eaaeebb1533daf67b38cbeb9f07fe75a30 --- /dev/null +++ b/coreml/coreml/vocoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f787f1a85ba0b25deb1baa8144e3bb8cf4095a5c16690e2e0700cc53402dcf5 +size 31041408 diff --git a/coreml/manifest.json b/coreml/manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..60a08ea5c5cda05b4e3ca4f0ff090813b4ed480c --- /dev/null +++ b/coreml/manifest.json @@ -0,0 +1,1477 @@ +{ + "architecture": "scyllasband-duration-flow", + "assets": { + "fixed_delivery_references": "assets/fixed_references.json", + "g2p_config": "assets/g2p/export_config.json", + "g2p_language_map": "assets/g2p/language_map.json", + "g2p_normalization": "assets/g2p/normalization.json", + "g2p_phoneme_dict": "assets/g2p/phoneme_dict.json", + "g2p_pronunciation_overrides": "assets/g2p/pronunciation_overrides.json", + "g2p_tokenizer": "assets/g2p/tokenizer.json", + "language_index": "assets/languages.json", + "phone_vocab": "assets/phone_vocab.json", + "voice_index": "assets/voices.json" + }, + "audio": { + "hop_length": 256, + "latent_dim": 24, + "latent_hop_length": 512, + "n_mels": 100, + "sample_rate": 24000 + }, + "components": { + "g2p_64": { + "artifacts": { + "coreml": { + "format": "coreml", + "path": "coreml/g2p.mlmodelc", + "function": "g2p_64" + } + }, + "inputs": [ + "text" + ], + "outputs": [ + "logits" + ], + "required": false + }, + "g2p_128": { + "artifacts": { + "coreml": { + "format": "coreml", + "path": "coreml/g2p.mlmodelc", + "function": "g2p_128" + } + }, + "inputs": [ + "text" + ], + "outputs": [ + "logits" + ], + "required": false + }, + 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