diff --git a/compiled/TextEncoder.mlmodelc/analytics/coremldata.bin b/compiled/TextEncoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e93e6ceaa6e8e319e2ea8cdd6dcc1488ccc49d1f --- /dev/null +++ b/compiled/TextEncoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f732919fa370a1b7b09ec2b227539269b6543149a2b0dbae95cc4cf350e4b697 +size 207 diff --git a/compiled/TextEncoder.mlmodelc/coremldata.bin b/compiled/TextEncoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..bca87797b63a2002be08fcd99a522c5011db367c --- /dev/null +++ b/compiled/TextEncoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ff26866d8d8fbb4e53a0628f8aab5f7edf1b3ec763a96e6812c8f7fbf4c9827 +size 825 diff --git a/compiled/TextEncoder.mlmodelc/metadata.json b/compiled/TextEncoder.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..df8a5ad64ed7dccc831ee8dc734c7a263010eb6e --- /dev/null +++ b/compiled/TextEncoder.mlmodelc/metadata.json @@ -0,0 +1,82 @@ +[ + { + "shortDescription" : "Stable Diffusion generates images conditioned on text and\/or other images as input through the diffusion process. Please refer to https:\/\/arxiv.org\/abs\/2112.10752 for details.", + "metadataOutputVersion" : "3.0", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32)", + "shortDescription" : "Hidden states after the encoder layers", + "shape" : "[]", + "name" : "hidden_embeds", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32)", + "shortDescription" : "The version of the `last_hidden_state` output after pooling", + "shape" : "[]", + "name" : "pooled_outputs", + "type" : "MultiArray" + } + ], + "version" : "diffusers\/stable-diffusion-xl-base-1.0", + "modelParameters" : [ + + ], + "author" : "Please refer to the Model Card available at huggingface.co\/diffusers\/stable-diffusion-xl-base-1.0", + "specificationVersion" : 7, + "storagePrecision" : "Float16", + "license" : "OpenRAIL (https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license)", + "mlProgramOperationTypeHistogram" : { + "Ios16.cast" : 3, + "Ios16.mul" : 36, + "Ios16.layerNorm" : 25, + "Stack" : 1, + "Transpose" : 60, + "Ios16.sigmoid" : 12, + "Ios16.linear" : 72, + "Ios16.add" : 37, + "Ios16.matmul" : 24, + "Ios16.softmax" : 12, + "Ios16.gatherNd" : 1, + "Ios16.gather" : 1, + "Ios16.reshape" : 120, + "Ios16.reduceArgmax" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 77)", + "shortDescription" : "The token ids that represent the input text", + "shape" : "[1, 77]", + "name" : "input_ids", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.version" : "7.0b1", + "com.github.apple.coremltools.source" : "torch==2.0.1+cu117" + }, + "generatedClassName" : "Stable_Diffusion_version_diffusers_stable_diffusion_xl_base_1_0_text_encoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/compiled/TextEncoder.mlmodelc/model.mil b/compiled/TextEncoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..02bd1f912cc81bf538be2548551c918d9c4482b3 --- /dev/null +++ b/compiled/TextEncoder.mlmodelc/model.mil @@ -0,0 +1,896 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.33.4"}, {"coremlc-version", "1839.0.0"}, {"coremltools-component-torch", "2.0.1+cu117"}, {"coremltools-version", "7.0b1"}})] +{ + func main(tensor input_ids) { + tensor var_5 = const()[name = tensor("op_5"), val = tensor(-1)]; + tensor var_6 = const()[name = tensor("op_6"), val = tensor(false)]; + tensor cast_1_dtype_0 = const()[name = tensor("cast_1_dtype_0"), val = tensor("int32")]; + tensor inputs_embeds_axis_0 = const()[name = tensor("inputs_embeds_axis_0"), val = tensor(0)]; + tensor inputs_embeds_batch_dims_0 = const()[name = tensor("inputs_embeds_batch_dims_0"), val = tensor(0)]; + tensor text_encoder_text_model_embeddings_token_embedding_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_embeddings_token_embedding_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor cast_526 = cast(dtype = cast_1_dtype_0, x = input_ids)[name = tensor("cast_526")]; + tensor inputs_embeds_cast = gather(axis = inputs_embeds_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = cast_526, x = text_encoder_text_model_embeddings_token_embedding_weight_to_fp16)[name = tensor("inputs_embeds_cast")]; + tensor position_embeddings_to_fp16 = const()[name = tensor("position_embeddings_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75890816)))]; + tensor input_3_cast = add(x = inputs_embeds_cast, y = position_embeddings_to_fp16)[name = tensor("input_3_cast")]; + tensor hidden_states_1_axes_0 = const()[name = tensor("hidden_states_1_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76009152)))]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76010752)))]; + tensor var_12_to_fp16 = const()[name = tensor("op_12_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_1_cast = layer_norm(axes = hidden_states_1_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16, x = input_3_cast)[name = tensor("hidden_states_1_cast")]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76012352)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77192064)))]; + tensor var_86_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16, x = hidden_states_1_cast)[name = tensor("op_86_cast")]; + tensor var_87_to_fp16 = const()[name = tensor("op_87_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_5_cast = mul(x = var_86_cast, y = var_87_to_fp16)[name = tensor("tensor_5_cast")]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77193664)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78373376)))]; + tensor tensor_1_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16, x = hidden_states_1_cast)[name = tensor("tensor_1_cast")]; + tensor var_92 = const()[name = tensor("op_92"), val = tensor([1, -1, 12, 64])]; + tensor var_93_cast = reshape(shape = var_92, x = tensor_1_cast)[name = tensor("op_93_cast")]; + tensor var_94_perm_0 = const()[name = tensor("op_94_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78374976)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79554688)))]; + tensor tensor_3_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16, x = hidden_states_1_cast)[name = tensor("tensor_3_cast")]; + tensor var_99 = const()[name = tensor("op_99"), val = tensor([1, -1, 12, 64])]; + tensor var_100_cast = reshape(shape = var_99, x = tensor_3_cast)[name = tensor("op_100_cast")]; + tensor var_101_perm_0 = const()[name = tensor("op_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_108 = const()[name = tensor("op_108"), val = tensor([1, 77, 12, 64])]; + tensor var_109_cast = reshape(shape = var_108, x = tensor_5_cast)[name = tensor("op_109_cast")]; + tensor var_110_perm_0 = const()[name = tensor("op_110_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_112 = const()[name = tensor("op_112"), val = tensor([12, -1, 64])]; + tensor transpose_58 = transpose(perm = var_110_perm_0, x = var_109_cast)[name = tensor("transpose_58")]; + tensor query_states_1_cast = reshape(shape = var_112, x = transpose_58)[name = tensor("query_states_1_cast")]; + tensor var_114 = const()[name = tensor("op_114"), val = tensor([12, -1, 64])]; + tensor transpose_60 = transpose(perm = var_94_perm_0, x = var_93_cast)[name = tensor("transpose_60")]; + tensor key_states_3_cast = reshape(shape = var_114, x = transpose_60)[name = tensor("key_states_3_cast")]; + tensor var_116 = const()[name = tensor("op_116"), val = tensor([12, -1, 64])]; + tensor transpose_59 = transpose(perm = var_101_perm_0, x = var_100_cast)[name = tensor("transpose_59")]; + tensor value_states_3_cast = reshape(shape = var_116, x = transpose_59)[name = tensor("value_states_3_cast")]; + tensor var_119_perm_0 = const()[name = tensor("op_119_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor transpose_57 = transpose(perm = var_119_perm_0, x = key_states_3_cast)[name = tensor("transpose_57")]; + tensor attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = query_states_1_cast, y = transpose_57)[name = tensor("attn_weights_1_cast")]; + tensor var_121 = const()[name = tensor("op_121"), val = tensor([1, 12, 77, 77])]; + tensor var_122_cast = reshape(shape = var_121, x = attn_weights_1_cast)[name = tensor("op_122_cast")]; + tensor causal_attention_mask_to_fp16 = const()[name = tensor("causal_attention_mask_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79556288)))]; + tensor attn_weights_3_cast = add(x = var_122_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_3_cast")]; + tensor var_127 = const()[name = tensor("op_127"), val = tensor([12, 77, 77])]; + tensor input_5_cast = reshape(shape = var_127, x = attn_weights_3_cast)[name = tensor("input_5_cast")]; + tensor input_7_cast = softmax(axis = var_5, x = input_5_cast)[name = tensor("input_7_cast")]; + tensor attn_output_1_transpose_x_0 = const()[name = tensor("attn_output_1_transpose_x_0"), val = tensor(false)]; + tensor attn_output_1_transpose_y_0 = const()[name = tensor("attn_output_1_transpose_y_0"), val = tensor(false)]; + tensor attn_output_1_cast = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = input_7_cast, y = value_states_3_cast)[name = tensor("attn_output_1_cast")]; + tensor var_132 = const()[name = tensor("op_132"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_3_cast = reshape(shape = var_132, x = attn_output_1_cast)[name = tensor("attn_output_3_cast")]; + tensor attn_output_5_perm_0 = const()[name = tensor("attn_output_5_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_135 = const()[name = tensor("op_135"), val = tensor([1, 77, 768])]; + tensor transpose_56 = transpose(perm = attn_output_5_perm_0, x = attn_output_3_cast)[name = tensor("transpose_56")]; + tensor input_9_cast = reshape(shape = var_135, x = transpose_56)[name = tensor("input_9_cast")]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79568256)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80747968)))]; + tensor hidden_states_3_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16, x = input_9_cast)[name = tensor("hidden_states_3_cast")]; + tensor input_11_cast = add(x = input_3_cast, y = hidden_states_3_cast)[name = tensor("input_11_cast")]; + tensor input_13_axes_0 = const()[name = tensor("input_13_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80749568)))]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80751168)))]; + tensor input_13_cast = layer_norm(axes = input_13_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16, x = input_11_cast)[name = tensor("input_13_cast")]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80752768)))]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85471424)))]; + tensor input_15_cast = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16, x = input_13_cast)[name = tensor("input_15_cast")]; + tensor var_150_to_fp16 = const()[name = tensor("op_150_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_151_cast = mul(x = input_15_cast, y = var_150_to_fp16)[name = tensor("op_151_cast")]; + tensor var_152_cast = sigmoid(x = var_151_cast)[name = tensor("op_152_cast")]; + tensor input_17_cast = mul(x = input_15_cast, y = var_152_cast)[name = tensor("input_17_cast")]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85477632)))]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90196288)))]; + tensor hidden_states_5_cast = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16, x = input_17_cast)[name = tensor("hidden_states_5_cast")]; + tensor input_19_cast = add(x = input_11_cast, y = hidden_states_5_cast)[name = tensor("input_19_cast")]; + tensor hidden_states_7_axes_0 = const()[name = tensor("hidden_states_7_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90197888)))]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90199488)))]; + tensor hidden_states_7_cast = layer_norm(axes = hidden_states_7_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16, x = input_19_cast)[name = tensor("hidden_states_7_cast")]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90201088)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91380800)))]; + tensor var_176_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16, x = hidden_states_7_cast)[name = tensor("op_176_cast")]; + tensor var_177_to_fp16 = const()[name = tensor("op_177_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_11_cast = mul(x = var_176_cast, y = var_177_to_fp16)[name = tensor("tensor_11_cast")]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91382400)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92562112)))]; + tensor tensor_7_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16, x = hidden_states_7_cast)[name = tensor("tensor_7_cast")]; + tensor var_182 = const()[name = tensor("op_182"), val = tensor([1, -1, 12, 64])]; + tensor var_183_cast = reshape(shape = var_182, x = tensor_7_cast)[name = tensor("op_183_cast")]; + tensor var_184_perm_0 = const()[name = tensor("op_184_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92563712)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93743424)))]; + tensor tensor_9_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16, x = hidden_states_7_cast)[name = tensor("tensor_9_cast")]; + tensor var_189 = const()[name = tensor("op_189"), val = tensor([1, -1, 12, 64])]; + tensor var_190_cast = reshape(shape = var_189, x = tensor_9_cast)[name = tensor("op_190_cast")]; + tensor var_191_perm_0 = const()[name = tensor("op_191_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_198 = const()[name = tensor("op_198"), val = tensor([1, 77, 12, 64])]; + tensor var_199_cast = reshape(shape = var_198, x = tensor_11_cast)[name = tensor("op_199_cast")]; + tensor var_200_perm_0 = const()[name = tensor("op_200_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_202 = const()[name = tensor("op_202"), val = tensor([12, -1, 64])]; + tensor transpose_53 = transpose(perm = var_200_perm_0, x = var_199_cast)[name = tensor("transpose_53")]; + tensor query_states_3_cast = reshape(shape = var_202, x = transpose_53)[name = tensor("query_states_3_cast")]; + tensor var_204 = const()[name = tensor("op_204"), val = tensor([12, -1, 64])]; + tensor transpose_55 = transpose(perm = var_184_perm_0, x = var_183_cast)[name = tensor("transpose_55")]; + tensor key_states_7_cast = reshape(shape = var_204, x = transpose_55)[name = tensor("key_states_7_cast")]; + tensor var_206 = const()[name = tensor("op_206"), val = tensor([12, -1, 64])]; + tensor transpose_54 = transpose(perm = var_191_perm_0, x = var_190_cast)[name = tensor("transpose_54")]; + tensor value_states_7_cast = reshape(shape = var_206, x = transpose_54)[name = tensor("value_states_7_cast")]; + tensor var_209_perm_0 = const()[name = tensor("op_209_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_7_transpose_x_0 = const()[name = tensor("attn_weights_7_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_7_transpose_y_0 = const()[name = tensor("attn_weights_7_transpose_y_0"), val = tensor(false)]; + tensor transpose_52 = transpose(perm = var_209_perm_0, x = key_states_7_cast)[name = tensor("transpose_52")]; + tensor attn_weights_7_cast = matmul(transpose_x = attn_weights_7_transpose_x_0, transpose_y = attn_weights_7_transpose_y_0, x = query_states_3_cast, y = transpose_52)[name = tensor("attn_weights_7_cast")]; + tensor var_211 = const()[name = tensor("op_211"), val = tensor([1, 12, 77, 77])]; + tensor var_212_cast = reshape(shape = var_211, x = attn_weights_7_cast)[name = tensor("op_212_cast")]; + tensor attn_weights_9_cast = add(x = var_212_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_9_cast")]; + tensor var_217 = const()[name = tensor("op_217"), val = tensor([12, 77, 77])]; + tensor input_21_cast = reshape(shape = var_217, x = attn_weights_9_cast)[name = tensor("input_21_cast")]; + tensor input_23_cast = softmax(axis = var_5, x = input_21_cast)[name = tensor("input_23_cast")]; + tensor attn_output_7_transpose_x_0 = const()[name = tensor("attn_output_7_transpose_x_0"), val = tensor(false)]; + tensor attn_output_7_transpose_y_0 = const()[name = tensor("attn_output_7_transpose_y_0"), val = tensor(false)]; + tensor attn_output_7_cast = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = input_23_cast, y = value_states_7_cast)[name = tensor("attn_output_7_cast")]; + tensor var_222 = const()[name = tensor("op_222"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_9_cast = reshape(shape = var_222, x = attn_output_7_cast)[name = tensor("attn_output_9_cast")]; + tensor attn_output_11_perm_0 = const()[name = tensor("attn_output_11_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_225 = const()[name = tensor("op_225"), val = tensor([1, 77, 768])]; + tensor transpose_51 = transpose(perm = attn_output_11_perm_0, x = attn_output_9_cast)[name = tensor("transpose_51")]; + tensor input_25_cast = reshape(shape = var_225, x = transpose_51)[name = tensor("input_25_cast")]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93745024)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94924736)))]; + tensor hidden_states_9_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16, x = input_25_cast)[name = tensor("hidden_states_9_cast")]; + tensor input_27_cast = add(x = input_19_cast, y = hidden_states_9_cast)[name = tensor("input_27_cast")]; + tensor input_29_axes_0 = const()[name = tensor("input_29_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94926336)))]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94927936)))]; + tensor input_29_cast = layer_norm(axes = input_29_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16, x = input_27_cast)[name = tensor("input_29_cast")]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94929536)))]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99648192)))]; + tensor input_31_cast = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16, x = input_29_cast)[name = tensor("input_31_cast")]; + tensor var_240_to_fp16 = const()[name = tensor("op_240_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_241_cast = mul(x = input_31_cast, y = var_240_to_fp16)[name = tensor("op_241_cast")]; + tensor var_242_cast = sigmoid(x = var_241_cast)[name = tensor("op_242_cast")]; + tensor input_33_cast = mul(x = input_31_cast, y = var_242_cast)[name = tensor("input_33_cast")]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99654400)))]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104373056)))]; + tensor hidden_states_11_cast = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16, x = input_33_cast)[name = tensor("hidden_states_11_cast")]; + tensor input_35_cast = add(x = input_27_cast, y = hidden_states_11_cast)[name = tensor("input_35_cast")]; + tensor hidden_states_13_axes_0 = const()[name = tensor("hidden_states_13_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104374656)))]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104376256)))]; + tensor hidden_states_13_cast = layer_norm(axes = hidden_states_13_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16, x = input_35_cast)[name = tensor("hidden_states_13_cast")]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104377856)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105557568)))]; + tensor var_266_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16, x = hidden_states_13_cast)[name = tensor("op_266_cast")]; + tensor var_267_to_fp16 = const()[name = tensor("op_267_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_17_cast = mul(x = var_266_cast, y = var_267_to_fp16)[name = tensor("tensor_17_cast")]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105559168)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106738880)))]; + tensor tensor_13_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16, x = hidden_states_13_cast)[name = tensor("tensor_13_cast")]; + tensor var_272 = const()[name = tensor("op_272"), val = tensor([1, -1, 12, 64])]; + tensor var_273_cast = reshape(shape = var_272, x = tensor_13_cast)[name = tensor("op_273_cast")]; + tensor var_274_perm_0 = const()[name = tensor("op_274_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106740480)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107920192)))]; + tensor tensor_15_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16, x = hidden_states_13_cast)[name = tensor("tensor_15_cast")]; + tensor var_279 = const()[name = tensor("op_279"), val = tensor([1, -1, 12, 64])]; + tensor var_280_cast = reshape(shape = var_279, x = tensor_15_cast)[name = tensor("op_280_cast")]; + tensor var_281_perm_0 = const()[name = tensor("op_281_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_288 = const()[name = tensor("op_288"), val = tensor([1, 77, 12, 64])]; + tensor var_289_cast = reshape(shape = var_288, x = tensor_17_cast)[name = tensor("op_289_cast")]; + tensor var_290_perm_0 = const()[name = tensor("op_290_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_292 = const()[name = tensor("op_292"), val = tensor([12, -1, 64])]; + tensor transpose_48 = transpose(perm = var_290_perm_0, x = var_289_cast)[name = tensor("transpose_48")]; + tensor query_states_5_cast = reshape(shape = var_292, x = transpose_48)[name = tensor("query_states_5_cast")]; + tensor var_294 = const()[name = tensor("op_294"), val = tensor([12, -1, 64])]; + tensor transpose_50 = transpose(perm = var_274_perm_0, x = var_273_cast)[name = tensor("transpose_50")]; + tensor key_states_11_cast = reshape(shape = var_294, x = transpose_50)[name = tensor("key_states_11_cast")]; + tensor var_296 = const()[name = tensor("op_296"), val = tensor([12, -1, 64])]; + tensor transpose_49 = transpose(perm = var_281_perm_0, x = var_280_cast)[name = tensor("transpose_49")]; + tensor value_states_11_cast = reshape(shape = var_296, x = transpose_49)[name = tensor("value_states_11_cast")]; + tensor var_299_perm_0 = const()[name = tensor("op_299_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor transpose_47 = transpose(perm = var_299_perm_0, x = key_states_11_cast)[name = tensor("transpose_47")]; + tensor attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = query_states_5_cast, y = transpose_47)[name = tensor("attn_weights_13_cast")]; + tensor var_301 = const()[name = tensor("op_301"), val = tensor([1, 12, 77, 77])]; + tensor var_302_cast = reshape(shape = var_301, x = attn_weights_13_cast)[name = tensor("op_302_cast")]; + tensor attn_weights_15_cast = add(x = var_302_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_15_cast")]; + tensor var_307 = const()[name = tensor("op_307"), val = tensor([12, 77, 77])]; + tensor input_37_cast = reshape(shape = var_307, x = attn_weights_15_cast)[name = tensor("input_37_cast")]; + tensor input_39_cast = softmax(axis = var_5, x = input_37_cast)[name = tensor("input_39_cast")]; + tensor attn_output_13_transpose_x_0 = const()[name = tensor("attn_output_13_transpose_x_0"), val = tensor(false)]; + tensor attn_output_13_transpose_y_0 = const()[name = tensor("attn_output_13_transpose_y_0"), val = tensor(false)]; + tensor attn_output_13_cast = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = input_39_cast, y = value_states_11_cast)[name = tensor("attn_output_13_cast")]; + tensor var_312 = const()[name = tensor("op_312"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_15_cast = reshape(shape = var_312, x = attn_output_13_cast)[name = tensor("attn_output_15_cast")]; + tensor attn_output_17_perm_0 = const()[name = tensor("attn_output_17_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_315 = const()[name = tensor("op_315"), val = tensor([1, 77, 768])]; + tensor transpose_46 = transpose(perm = attn_output_17_perm_0, x = attn_output_15_cast)[name = tensor("transpose_46")]; + tensor input_41_cast = reshape(shape = var_315, x = transpose_46)[name = tensor("input_41_cast")]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107921792)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109101504)))]; + tensor hidden_states_15_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16, x = input_41_cast)[name = tensor("hidden_states_15_cast")]; + tensor input_43_cast = add(x = input_35_cast, y = hidden_states_15_cast)[name = tensor("input_43_cast")]; + tensor input_45_axes_0 = const()[name = tensor("input_45_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109103104)))]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109104704)))]; + tensor input_45_cast = layer_norm(axes = input_45_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16, x = input_43_cast)[name = tensor("input_45_cast")]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109106304)))]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113824960)))]; + tensor input_47_cast = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16, x = input_45_cast)[name = tensor("input_47_cast")]; + tensor var_330_to_fp16 = const()[name = tensor("op_330_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_331_cast = mul(x = input_47_cast, y = var_330_to_fp16)[name = tensor("op_331_cast")]; + tensor var_332_cast = sigmoid(x = var_331_cast)[name = tensor("op_332_cast")]; + tensor input_49_cast = mul(x = input_47_cast, y = var_332_cast)[name = tensor("input_49_cast")]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113831168)))]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118549824)))]; + tensor hidden_states_17_cast = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16, x = input_49_cast)[name = tensor("hidden_states_17_cast")]; + tensor input_51_cast = add(x = input_43_cast, y = hidden_states_17_cast)[name = tensor("input_51_cast")]; + tensor hidden_states_19_axes_0 = const()[name = tensor("hidden_states_19_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118551424)))]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118553024)))]; + tensor hidden_states_19_cast = layer_norm(axes = hidden_states_19_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16, x = input_51_cast)[name = tensor("hidden_states_19_cast")]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118554624)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119734336)))]; + tensor var_356_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16, x = hidden_states_19_cast)[name = tensor("op_356_cast")]; + tensor var_357_to_fp16 = const()[name = tensor("op_357_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_23_cast = mul(x = var_356_cast, y = var_357_to_fp16)[name = tensor("tensor_23_cast")]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119735936)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120915648)))]; + tensor tensor_19_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16, x = hidden_states_19_cast)[name = tensor("tensor_19_cast")]; + tensor var_362 = const()[name = tensor("op_362"), val = tensor([1, -1, 12, 64])]; + tensor var_363_cast = reshape(shape = var_362, x = tensor_19_cast)[name = tensor("op_363_cast")]; + tensor var_364_perm_0 = const()[name = tensor("op_364_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120917248)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122096960)))]; + tensor tensor_21_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16, x = hidden_states_19_cast)[name = tensor("tensor_21_cast")]; + tensor var_369 = const()[name = tensor("op_369"), val = tensor([1, -1, 12, 64])]; + tensor var_370_cast = reshape(shape = var_369, x = tensor_21_cast)[name = tensor("op_370_cast")]; + tensor var_371_perm_0 = const()[name = tensor("op_371_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_378 = const()[name = tensor("op_378"), val = tensor([1, 77, 12, 64])]; + tensor var_379_cast = reshape(shape = var_378, x = tensor_23_cast)[name = tensor("op_379_cast")]; + tensor var_380_perm_0 = const()[name = tensor("op_380_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_382 = const()[name = tensor("op_382"), val = tensor([12, -1, 64])]; + tensor transpose_43 = transpose(perm = var_380_perm_0, x = var_379_cast)[name = tensor("transpose_43")]; + tensor query_states_7_cast = reshape(shape = var_382, x = transpose_43)[name = tensor("query_states_7_cast")]; + tensor var_384 = const()[name = tensor("op_384"), val = tensor([12, -1, 64])]; + tensor transpose_45 = transpose(perm = var_364_perm_0, x = var_363_cast)[name = tensor("transpose_45")]; + tensor key_states_15_cast = reshape(shape = var_384, x = transpose_45)[name = tensor("key_states_15_cast")]; + tensor var_386 = const()[name = tensor("op_386"), val = tensor([12, -1, 64])]; + tensor transpose_44 = transpose(perm = var_371_perm_0, x = var_370_cast)[name = tensor("transpose_44")]; + tensor value_states_15_cast = reshape(shape = var_386, x = transpose_44)[name = tensor("value_states_15_cast")]; + tensor var_389_perm_0 = const()[name = tensor("op_389_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_19_transpose_x_0 = const()[name = tensor("attn_weights_19_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_19_transpose_y_0 = const()[name = tensor("attn_weights_19_transpose_y_0"), val = tensor(false)]; + tensor transpose_42 = transpose(perm = var_389_perm_0, x = key_states_15_cast)[name = tensor("transpose_42")]; + tensor attn_weights_19_cast = matmul(transpose_x = attn_weights_19_transpose_x_0, transpose_y = attn_weights_19_transpose_y_0, x = query_states_7_cast, y = transpose_42)[name = tensor("attn_weights_19_cast")]; + tensor var_391 = const()[name = tensor("op_391"), val = tensor([1, 12, 77, 77])]; + tensor var_392_cast = reshape(shape = var_391, x = attn_weights_19_cast)[name = tensor("op_392_cast")]; + tensor attn_weights_21_cast = add(x = var_392_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_21_cast")]; + tensor var_397 = const()[name = tensor("op_397"), val = tensor([12, 77, 77])]; + tensor input_53_cast = reshape(shape = var_397, x = attn_weights_21_cast)[name = tensor("input_53_cast")]; + tensor input_55_cast = softmax(axis = var_5, x = input_53_cast)[name = tensor("input_55_cast")]; + tensor attn_output_19_transpose_x_0 = const()[name = tensor("attn_output_19_transpose_x_0"), val = tensor(false)]; + tensor attn_output_19_transpose_y_0 = const()[name = tensor("attn_output_19_transpose_y_0"), val = tensor(false)]; + tensor attn_output_19_cast = matmul(transpose_x = attn_output_19_transpose_x_0, transpose_y = attn_output_19_transpose_y_0, x = input_55_cast, y = value_states_15_cast)[name = tensor("attn_output_19_cast")]; + tensor var_402 = const()[name = tensor("op_402"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_21_cast = reshape(shape = var_402, x = attn_output_19_cast)[name = tensor("attn_output_21_cast")]; + tensor attn_output_23_perm_0 = const()[name = tensor("attn_output_23_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_405 = const()[name = tensor("op_405"), val = tensor([1, 77, 768])]; + tensor transpose_41 = transpose(perm = attn_output_23_perm_0, x = attn_output_21_cast)[name = tensor("transpose_41")]; + tensor input_57_cast = reshape(shape = var_405, x = transpose_41)[name = tensor("input_57_cast")]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122098560)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123278272)))]; + tensor hidden_states_21_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16, x = input_57_cast)[name = tensor("hidden_states_21_cast")]; + tensor input_59_cast = add(x = input_51_cast, y = hidden_states_21_cast)[name = tensor("input_59_cast")]; + tensor input_61_axes_0 = const()[name = tensor("input_61_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123279872)))]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123281472)))]; + tensor input_61_cast = layer_norm(axes = input_61_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16, x = input_59_cast)[name = tensor("input_61_cast")]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123283072)))]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(128001728)))]; + tensor input_63_cast = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16, x = input_61_cast)[name = tensor("input_63_cast")]; + tensor var_420_to_fp16 = const()[name = tensor("op_420_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_421_cast = mul(x = input_63_cast, y = var_420_to_fp16)[name = tensor("op_421_cast")]; + tensor var_422_cast = sigmoid(x = var_421_cast)[name = tensor("op_422_cast")]; + tensor input_65_cast = mul(x = input_63_cast, y = var_422_cast)[name = tensor("input_65_cast")]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(128007936)))]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132726592)))]; + tensor hidden_states_23_cast = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16, x = input_65_cast)[name = tensor("hidden_states_23_cast")]; + tensor input_67_cast = add(x = input_59_cast, y = hidden_states_23_cast)[name = tensor("input_67_cast")]; + tensor hidden_states_25_axes_0 = const()[name = tensor("hidden_states_25_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132728192)))]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132729792)))]; + tensor hidden_states_25_cast = layer_norm(axes = hidden_states_25_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16, x = input_67_cast)[name = tensor("hidden_states_25_cast")]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132731392)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133911104)))]; + tensor var_446_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16, x = hidden_states_25_cast)[name = tensor("op_446_cast")]; + tensor var_447_to_fp16 = const()[name = tensor("op_447_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_29_cast = mul(x = var_446_cast, y = var_447_to_fp16)[name = tensor("tensor_29_cast")]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133912704)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135092416)))]; + tensor tensor_25_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16, x = hidden_states_25_cast)[name = tensor("tensor_25_cast")]; + tensor var_452 = const()[name = tensor("op_452"), val = tensor([1, -1, 12, 64])]; + tensor var_453_cast = reshape(shape = var_452, x = tensor_25_cast)[name = tensor("op_453_cast")]; + tensor var_454_perm_0 = const()[name = tensor("op_454_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135094016)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136273728)))]; + tensor tensor_27_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16, x = hidden_states_25_cast)[name = tensor("tensor_27_cast")]; + tensor var_459 = const()[name = tensor("op_459"), val = tensor([1, -1, 12, 64])]; + tensor var_460_cast = reshape(shape = var_459, x = tensor_27_cast)[name = tensor("op_460_cast")]; + tensor var_461_perm_0 = const()[name = tensor("op_461_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_468 = const()[name = tensor("op_468"), val = tensor([1, 77, 12, 64])]; + tensor var_469_cast = reshape(shape = var_468, x = tensor_29_cast)[name = tensor("op_469_cast")]; + tensor var_470_perm_0 = const()[name = tensor("op_470_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_472 = const()[name = tensor("op_472"), val = tensor([12, -1, 64])]; + tensor transpose_38 = transpose(perm = var_470_perm_0, x = var_469_cast)[name = tensor("transpose_38")]; + tensor query_states_9_cast = reshape(shape = var_472, x = transpose_38)[name = tensor("query_states_9_cast")]; + tensor var_474 = const()[name = tensor("op_474"), val = tensor([12, -1, 64])]; + tensor transpose_40 = transpose(perm = var_454_perm_0, x = var_453_cast)[name = tensor("transpose_40")]; + tensor key_states_19_cast = reshape(shape = var_474, x = transpose_40)[name = tensor("key_states_19_cast")]; + tensor var_476 = const()[name = tensor("op_476"), val = tensor([12, -1, 64])]; + tensor transpose_39 = transpose(perm = var_461_perm_0, x = var_460_cast)[name = tensor("transpose_39")]; + tensor value_states_19_cast = reshape(shape = var_476, x = transpose_39)[name = tensor("value_states_19_cast")]; + tensor var_479_perm_0 = const()[name = tensor("op_479_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor transpose_37 = transpose(perm = var_479_perm_0, x = key_states_19_cast)[name = tensor("transpose_37")]; + tensor attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = query_states_9_cast, y = transpose_37)[name = tensor("attn_weights_25_cast")]; + tensor var_481 = const()[name = tensor("op_481"), val = tensor([1, 12, 77, 77])]; + tensor var_482_cast = reshape(shape = var_481, x = attn_weights_25_cast)[name = tensor("op_482_cast")]; + tensor attn_weights_27_cast = add(x = var_482_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_27_cast")]; + tensor var_487 = const()[name = tensor("op_487"), val = tensor([12, 77, 77])]; + tensor input_69_cast = reshape(shape = var_487, x = attn_weights_27_cast)[name = tensor("input_69_cast")]; + tensor input_71_cast = softmax(axis = var_5, x = input_69_cast)[name = tensor("input_71_cast")]; + tensor attn_output_25_transpose_x_0 = const()[name = tensor("attn_output_25_transpose_x_0"), val = tensor(false)]; + tensor attn_output_25_transpose_y_0 = const()[name = tensor("attn_output_25_transpose_y_0"), val = tensor(false)]; + tensor attn_output_25_cast = matmul(transpose_x = attn_output_25_transpose_x_0, transpose_y = attn_output_25_transpose_y_0, x = input_71_cast, y = value_states_19_cast)[name = tensor("attn_output_25_cast")]; + tensor var_492 = const()[name = tensor("op_492"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_27_cast = reshape(shape = var_492, x = attn_output_25_cast)[name = tensor("attn_output_27_cast")]; + tensor attn_output_29_perm_0 = const()[name = tensor("attn_output_29_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_495 = const()[name = tensor("op_495"), val = tensor([1, 77, 768])]; + tensor transpose_36 = transpose(perm = attn_output_29_perm_0, x = attn_output_27_cast)[name = tensor("transpose_36")]; + tensor input_73_cast = reshape(shape = var_495, x = transpose_36)[name = tensor("input_73_cast")]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136275328)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137455040)))]; + tensor hidden_states_27_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16, x = input_73_cast)[name = tensor("hidden_states_27_cast")]; + tensor input_75_cast = add(x = input_67_cast, y = hidden_states_27_cast)[name = tensor("input_75_cast")]; + tensor input_77_axes_0 = const()[name = tensor("input_77_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137456640)))]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137458240)))]; + tensor input_77_cast = layer_norm(axes = input_77_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16, x = input_75_cast)[name = tensor("input_77_cast")]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137459840)))]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142178496)))]; + tensor input_79_cast = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16, x = input_77_cast)[name = tensor("input_79_cast")]; + tensor var_510_to_fp16 = const()[name = tensor("op_510_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_511_cast = mul(x = input_79_cast, y = var_510_to_fp16)[name = tensor("op_511_cast")]; + tensor var_512_cast = sigmoid(x = var_511_cast)[name = tensor("op_512_cast")]; + tensor input_81_cast = mul(x = input_79_cast, y = var_512_cast)[name = tensor("input_81_cast")]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142184704)))]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146903360)))]; + tensor hidden_states_29_cast = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16, x = input_81_cast)[name = tensor("hidden_states_29_cast")]; + tensor input_83_cast = add(x = input_75_cast, y = hidden_states_29_cast)[name = tensor("input_83_cast")]; + tensor hidden_states_31_axes_0 = const()[name = tensor("hidden_states_31_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146904960)))]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146906560)))]; + tensor hidden_states_31_cast = layer_norm(axes = hidden_states_31_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16, x = input_83_cast)[name = tensor("hidden_states_31_cast")]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(146908160)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148087872)))]; + tensor var_536_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16, x = hidden_states_31_cast)[name = tensor("op_536_cast")]; + tensor var_537_to_fp16 = const()[name = tensor("op_537_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_35_cast = mul(x = var_536_cast, y = var_537_to_fp16)[name = tensor("tensor_35_cast")]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(148089472)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149269184)))]; + tensor tensor_31_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16, x = hidden_states_31_cast)[name = tensor("tensor_31_cast")]; + tensor var_542 = const()[name = tensor("op_542"), val = tensor([1, -1, 12, 64])]; + tensor var_543_cast = reshape(shape = var_542, x = tensor_31_cast)[name = tensor("op_543_cast")]; + tensor var_544_perm_0 = const()[name = tensor("op_544_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149270784)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150450496)))]; + tensor tensor_33_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16, x = hidden_states_31_cast)[name = tensor("tensor_33_cast")]; + tensor var_549 = const()[name = tensor("op_549"), val = tensor([1, -1, 12, 64])]; + tensor var_550_cast = reshape(shape = var_549, x = tensor_33_cast)[name = tensor("op_550_cast")]; + tensor var_551_perm_0 = const()[name = tensor("op_551_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_558 = const()[name = tensor("op_558"), val = tensor([1, 77, 12, 64])]; + tensor var_559_cast = reshape(shape = var_558, x = tensor_35_cast)[name = tensor("op_559_cast")]; + tensor var_560_perm_0 = const()[name = tensor("op_560_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_562 = const()[name = tensor("op_562"), val = tensor([12, -1, 64])]; + tensor transpose_33 = transpose(perm = var_560_perm_0, x = var_559_cast)[name = tensor("transpose_33")]; + tensor query_states_11_cast = reshape(shape = var_562, x = transpose_33)[name = tensor("query_states_11_cast")]; + tensor var_564 = const()[name = tensor("op_564"), val = tensor([12, -1, 64])]; + tensor transpose_35 = transpose(perm = var_544_perm_0, x = var_543_cast)[name = tensor("transpose_35")]; + tensor key_states_23_cast = reshape(shape = var_564, x = transpose_35)[name = tensor("key_states_23_cast")]; + tensor var_566 = const()[name = tensor("op_566"), val = tensor([12, -1, 64])]; + tensor transpose_34 = transpose(perm = var_551_perm_0, x = var_550_cast)[name = tensor("transpose_34")]; + tensor value_states_23_cast = reshape(shape = var_566, x = transpose_34)[name = tensor("value_states_23_cast")]; + tensor var_569_perm_0 = const()[name = tensor("op_569_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_31_transpose_x_0 = const()[name = tensor("attn_weights_31_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_31_transpose_y_0 = const()[name = tensor("attn_weights_31_transpose_y_0"), val = tensor(false)]; + tensor transpose_32 = transpose(perm = var_569_perm_0, x = key_states_23_cast)[name = tensor("transpose_32")]; + tensor attn_weights_31_cast = matmul(transpose_x = attn_weights_31_transpose_x_0, transpose_y = attn_weights_31_transpose_y_0, x = query_states_11_cast, y = transpose_32)[name = tensor("attn_weights_31_cast")]; + tensor var_571 = const()[name = tensor("op_571"), val = tensor([1, 12, 77, 77])]; + tensor var_572_cast = reshape(shape = var_571, x = attn_weights_31_cast)[name = tensor("op_572_cast")]; + tensor attn_weights_33_cast = add(x = var_572_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_33_cast")]; + tensor var_577 = const()[name = tensor("op_577"), val = tensor([12, 77, 77])]; + tensor input_85_cast = reshape(shape = var_577, x = attn_weights_33_cast)[name = tensor("input_85_cast")]; + tensor input_87_cast = softmax(axis = var_5, x = input_85_cast)[name = tensor("input_87_cast")]; + tensor attn_output_31_transpose_x_0 = const()[name = tensor("attn_output_31_transpose_x_0"), val = tensor(false)]; + tensor attn_output_31_transpose_y_0 = const()[name = tensor("attn_output_31_transpose_y_0"), val = tensor(false)]; + tensor attn_output_31_cast = matmul(transpose_x = attn_output_31_transpose_x_0, transpose_y = attn_output_31_transpose_y_0, x = input_87_cast, y = value_states_23_cast)[name = tensor("attn_output_31_cast")]; + tensor var_582 = const()[name = tensor("op_582"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_33_cast = reshape(shape = var_582, x = attn_output_31_cast)[name = tensor("attn_output_33_cast")]; + tensor attn_output_35_perm_0 = const()[name = tensor("attn_output_35_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_585 = const()[name = tensor("op_585"), val = tensor([1, 77, 768])]; + tensor transpose_31 = transpose(perm = attn_output_35_perm_0, x = attn_output_33_cast)[name = tensor("transpose_31")]; + tensor input_89_cast = reshape(shape = var_585, x = transpose_31)[name = tensor("input_89_cast")]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150452096)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151631808)))]; + tensor hidden_states_33_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16, x = input_89_cast)[name = tensor("hidden_states_33_cast")]; + tensor input_91_cast = add(x = input_83_cast, y = hidden_states_33_cast)[name = tensor("input_91_cast")]; + tensor input_93_axes_0 = const()[name = tensor("input_93_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151633408)))]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151635008)))]; + tensor input_93_cast = layer_norm(axes = input_93_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16, x = input_91_cast)[name = tensor("input_93_cast")]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(151636608)))]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156355264)))]; + tensor input_95_cast = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16, x = input_93_cast)[name = tensor("input_95_cast")]; + tensor var_600_to_fp16 = const()[name = tensor("op_600_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_601_cast = mul(x = input_95_cast, y = var_600_to_fp16)[name = tensor("op_601_cast")]; + tensor var_602_cast = sigmoid(x = var_601_cast)[name = tensor("op_602_cast")]; + tensor input_97_cast = mul(x = input_95_cast, y = var_602_cast)[name = tensor("input_97_cast")]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(156361472)))]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161080128)))]; + tensor hidden_states_35_cast = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16, x = input_97_cast)[name = tensor("hidden_states_35_cast")]; + tensor input_99_cast = add(x = input_91_cast, y = hidden_states_35_cast)[name = tensor("input_99_cast")]; + tensor hidden_states_37_axes_0 = const()[name = tensor("hidden_states_37_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161081728)))]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161083328)))]; + tensor hidden_states_37_cast = layer_norm(axes = hidden_states_37_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16, x = input_99_cast)[name = tensor("hidden_states_37_cast")]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(161084928)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162264640)))]; + tensor var_626_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16, x = hidden_states_37_cast)[name = tensor("op_626_cast")]; + tensor var_627_to_fp16 = const()[name = tensor("op_627_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_41_cast = mul(x = var_626_cast, y = var_627_to_fp16)[name = tensor("tensor_41_cast")]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(162266240)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163445952)))]; + tensor tensor_37_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16, x = hidden_states_37_cast)[name = tensor("tensor_37_cast")]; + tensor var_632 = const()[name = tensor("op_632"), val = tensor([1, -1, 12, 64])]; + tensor var_633_cast = reshape(shape = var_632, x = tensor_37_cast)[name = tensor("op_633_cast")]; + tensor var_634_perm_0 = const()[name = tensor("op_634_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163447552)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164627264)))]; + tensor tensor_39_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16, x = hidden_states_37_cast)[name = tensor("tensor_39_cast")]; + tensor var_639 = const()[name = tensor("op_639"), val = tensor([1, -1, 12, 64])]; + tensor var_640_cast = reshape(shape = var_639, x = tensor_39_cast)[name = tensor("op_640_cast")]; + tensor var_641_perm_0 = const()[name = tensor("op_641_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_648 = const()[name = tensor("op_648"), val = tensor([1, 77, 12, 64])]; + tensor var_649_cast = reshape(shape = var_648, x = tensor_41_cast)[name = tensor("op_649_cast")]; + tensor var_650_perm_0 = const()[name = tensor("op_650_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_652 = const()[name = tensor("op_652"), val = tensor([12, -1, 64])]; + tensor transpose_28 = transpose(perm = var_650_perm_0, x = var_649_cast)[name = tensor("transpose_28")]; + tensor query_states_13_cast = reshape(shape = var_652, x = transpose_28)[name = tensor("query_states_13_cast")]; + tensor var_654 = const()[name = tensor("op_654"), val = tensor([12, -1, 64])]; + tensor transpose_30 = transpose(perm = var_634_perm_0, x = var_633_cast)[name = tensor("transpose_30")]; + tensor key_states_27_cast = reshape(shape = var_654, x = transpose_30)[name = tensor("key_states_27_cast")]; + tensor var_656 = const()[name = tensor("op_656"), val = tensor([12, -1, 64])]; + tensor transpose_29 = transpose(perm = var_641_perm_0, x = var_640_cast)[name = tensor("transpose_29")]; + tensor value_states_27_cast = reshape(shape = var_656, x = transpose_29)[name = tensor("value_states_27_cast")]; + tensor var_659_perm_0 = const()[name = tensor("op_659_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor transpose_27 = transpose(perm = var_659_perm_0, x = key_states_27_cast)[name = tensor("transpose_27")]; + tensor attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = query_states_13_cast, y = transpose_27)[name = tensor("attn_weights_37_cast")]; + tensor var_661 = const()[name = tensor("op_661"), val = tensor([1, 12, 77, 77])]; + tensor var_662_cast = reshape(shape = var_661, x = attn_weights_37_cast)[name = tensor("op_662_cast")]; + tensor attn_weights_39_cast = add(x = var_662_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_39_cast")]; + tensor var_667 = const()[name = tensor("op_667"), val = tensor([12, 77, 77])]; + tensor input_101_cast = reshape(shape = var_667, x = attn_weights_39_cast)[name = tensor("input_101_cast")]; + tensor input_103_cast = softmax(axis = var_5, x = input_101_cast)[name = tensor("input_103_cast")]; + tensor attn_output_37_transpose_x_0 = const()[name = tensor("attn_output_37_transpose_x_0"), val = tensor(false)]; + tensor attn_output_37_transpose_y_0 = const()[name = tensor("attn_output_37_transpose_y_0"), val = tensor(false)]; + tensor attn_output_37_cast = matmul(transpose_x = attn_output_37_transpose_x_0, transpose_y = attn_output_37_transpose_y_0, x = input_103_cast, y = value_states_27_cast)[name = tensor("attn_output_37_cast")]; + tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_39_cast = reshape(shape = var_672, x = attn_output_37_cast)[name = tensor("attn_output_39_cast")]; + tensor attn_output_41_perm_0 = const()[name = tensor("attn_output_41_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_675 = const()[name = tensor("op_675"), val = tensor([1, 77, 768])]; + tensor transpose_26 = transpose(perm = attn_output_41_perm_0, x = attn_output_39_cast)[name = tensor("transpose_26")]; + tensor input_105_cast = reshape(shape = var_675, x = transpose_26)[name = tensor("input_105_cast")]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164628864)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165808576)))]; + tensor hidden_states_39_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16, x = input_105_cast)[name = tensor("hidden_states_39_cast")]; + tensor input_107_cast = add(x = input_99_cast, y = hidden_states_39_cast)[name = tensor("input_107_cast")]; + tensor input_109_axes_0 = const()[name = tensor("input_109_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165810176)))]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165811776)))]; + tensor input_109_cast = layer_norm(axes = input_109_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16, x = input_107_cast)[name = tensor("input_109_cast")]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165813376)))]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170532032)))]; + tensor input_111_cast = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16, x = input_109_cast)[name = tensor("input_111_cast")]; + tensor var_690_to_fp16 = const()[name = tensor("op_690_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_691_cast = mul(x = input_111_cast, y = var_690_to_fp16)[name = tensor("op_691_cast")]; + tensor var_692_cast = sigmoid(x = var_691_cast)[name = tensor("op_692_cast")]; + tensor input_113_cast = mul(x = input_111_cast, y = var_692_cast)[name = tensor("input_113_cast")]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170538240)))]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175256896)))]; + tensor hidden_states_41_cast = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16, x = input_113_cast)[name = tensor("hidden_states_41_cast")]; + tensor input_115_cast = add(x = input_107_cast, y = hidden_states_41_cast)[name = tensor("input_115_cast")]; + tensor hidden_states_43_axes_0 = const()[name = tensor("hidden_states_43_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175258496)))]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175260096)))]; + tensor hidden_states_43_cast = layer_norm(axes = hidden_states_43_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16, x = input_115_cast)[name = tensor("hidden_states_43_cast")]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175261696)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176441408)))]; + tensor var_716_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16, x = hidden_states_43_cast)[name = tensor("op_716_cast")]; + tensor var_717_to_fp16 = const()[name = tensor("op_717_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_47_cast = mul(x = var_716_cast, y = var_717_to_fp16)[name = tensor("tensor_47_cast")]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176443008)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177622720)))]; + tensor tensor_43_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16, x = hidden_states_43_cast)[name = tensor("tensor_43_cast")]; + tensor var_722 = const()[name = tensor("op_722"), val = tensor([1, -1, 12, 64])]; + tensor var_723_cast = reshape(shape = var_722, x = tensor_43_cast)[name = tensor("op_723_cast")]; + tensor var_724_perm_0 = const()[name = tensor("op_724_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177624320)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178804032)))]; + tensor tensor_45_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16, x = hidden_states_43_cast)[name = tensor("tensor_45_cast")]; + tensor var_729 = const()[name = tensor("op_729"), val = tensor([1, -1, 12, 64])]; + tensor var_730_cast = reshape(shape = var_729, x = tensor_45_cast)[name = tensor("op_730_cast")]; + tensor var_731_perm_0 = const()[name = tensor("op_731_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_738 = const()[name = tensor("op_738"), val = tensor([1, 77, 12, 64])]; + tensor var_739_cast = reshape(shape = var_738, x = tensor_47_cast)[name = tensor("op_739_cast")]; + tensor var_740_perm_0 = const()[name = tensor("op_740_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_742 = const()[name = tensor("op_742"), val = tensor([12, -1, 64])]; + tensor transpose_23 = transpose(perm = var_740_perm_0, x = var_739_cast)[name = tensor("transpose_23")]; + tensor query_states_15_cast = reshape(shape = var_742, x = transpose_23)[name = tensor("query_states_15_cast")]; + tensor var_744 = const()[name = tensor("op_744"), val = tensor([12, -1, 64])]; + tensor transpose_25 = transpose(perm = var_724_perm_0, x = var_723_cast)[name = tensor("transpose_25")]; + tensor key_states_31_cast = reshape(shape = var_744, x = transpose_25)[name = tensor("key_states_31_cast")]; + tensor var_746 = const()[name = tensor("op_746"), val = tensor([12, -1, 64])]; + tensor transpose_24 = transpose(perm = var_731_perm_0, x = var_730_cast)[name = tensor("transpose_24")]; + tensor value_states_31_cast = reshape(shape = var_746, x = transpose_24)[name = tensor("value_states_31_cast")]; + tensor var_749_perm_0 = const()[name = tensor("op_749_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_43_transpose_x_0 = const()[name = tensor("attn_weights_43_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_43_transpose_y_0 = const()[name = tensor("attn_weights_43_transpose_y_0"), val = tensor(false)]; + tensor transpose_22 = transpose(perm = var_749_perm_0, x = key_states_31_cast)[name = tensor("transpose_22")]; + tensor attn_weights_43_cast = matmul(transpose_x = attn_weights_43_transpose_x_0, transpose_y = attn_weights_43_transpose_y_0, x = query_states_15_cast, y = transpose_22)[name = tensor("attn_weights_43_cast")]; + tensor var_751 = const()[name = tensor("op_751"), val = tensor([1, 12, 77, 77])]; + tensor var_752_cast = reshape(shape = var_751, x = attn_weights_43_cast)[name = tensor("op_752_cast")]; + tensor attn_weights_45_cast = add(x = var_752_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_45_cast")]; + tensor var_757 = const()[name = tensor("op_757"), val = tensor([12, 77, 77])]; + tensor input_117_cast = reshape(shape = var_757, x = attn_weights_45_cast)[name = tensor("input_117_cast")]; + tensor input_119_cast = softmax(axis = var_5, x = input_117_cast)[name = tensor("input_119_cast")]; + tensor attn_output_43_transpose_x_0 = const()[name = tensor("attn_output_43_transpose_x_0"), val = tensor(false)]; + tensor attn_output_43_transpose_y_0 = const()[name = tensor("attn_output_43_transpose_y_0"), val = tensor(false)]; + tensor attn_output_43_cast = matmul(transpose_x = attn_output_43_transpose_x_0, transpose_y = attn_output_43_transpose_y_0, x = input_119_cast, y = value_states_31_cast)[name = tensor("attn_output_43_cast")]; + tensor var_762 = const()[name = tensor("op_762"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_45_cast = reshape(shape = var_762, x = attn_output_43_cast)[name = tensor("attn_output_45_cast")]; + tensor attn_output_47_perm_0 = const()[name = tensor("attn_output_47_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_765 = const()[name = tensor("op_765"), val = tensor([1, 77, 768])]; + tensor transpose_21 = transpose(perm = attn_output_47_perm_0, x = attn_output_45_cast)[name = tensor("transpose_21")]; + tensor input_121_cast = reshape(shape = var_765, x = transpose_21)[name = tensor("input_121_cast")]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178805632)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179985344)))]; + tensor hidden_states_45_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16, x = input_121_cast)[name = tensor("hidden_states_45_cast")]; + tensor input_123_cast = add(x = input_115_cast, y = hidden_states_45_cast)[name = tensor("input_123_cast")]; + tensor input_125_axes_0 = const()[name = tensor("input_125_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179986944)))]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179988544)))]; + tensor input_125_cast = layer_norm(axes = input_125_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16, x = input_123_cast)[name = tensor("input_125_cast")]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179990144)))]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184708800)))]; + tensor input_127_cast = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16, x = input_125_cast)[name = tensor("input_127_cast")]; + tensor var_780_to_fp16 = const()[name = tensor("op_780_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_781_cast = mul(x = input_127_cast, y = var_780_to_fp16)[name = tensor("op_781_cast")]; + tensor var_782_cast = sigmoid(x = var_781_cast)[name = tensor("op_782_cast")]; + tensor input_129_cast = mul(x = input_127_cast, y = var_782_cast)[name = tensor("input_129_cast")]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184715008)))]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(189433664)))]; + tensor hidden_states_47_cast = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16, x = input_129_cast)[name = tensor("hidden_states_47_cast")]; + tensor input_131_cast = add(x = input_123_cast, y = hidden_states_47_cast)[name = tensor("input_131_cast")]; + tensor hidden_states_49_axes_0 = const()[name = tensor("hidden_states_49_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(189435264)))]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(189436864)))]; + tensor hidden_states_49_cast = layer_norm(axes = hidden_states_49_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16, x = input_131_cast)[name = tensor("hidden_states_49_cast")]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(189438464)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190618176)))]; + tensor var_806_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16, x = hidden_states_49_cast)[name = tensor("op_806_cast")]; + tensor var_807_to_fp16 = const()[name = tensor("op_807_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_53_cast = mul(x = var_806_cast, y = var_807_to_fp16)[name = tensor("tensor_53_cast")]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(190619776)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191799488)))]; + tensor tensor_49_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16, x = hidden_states_49_cast)[name = tensor("tensor_49_cast")]; + tensor var_812 = const()[name = tensor("op_812"), val = tensor([1, -1, 12, 64])]; + tensor var_813_cast = reshape(shape = var_812, x = tensor_49_cast)[name = tensor("op_813_cast")]; + tensor var_814_perm_0 = const()[name = tensor("op_814_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191801088)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192980800)))]; + tensor tensor_51_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16, x = hidden_states_49_cast)[name = tensor("tensor_51_cast")]; + tensor var_819 = const()[name = tensor("op_819"), val = tensor([1, -1, 12, 64])]; + tensor var_820_cast = reshape(shape = var_819, x = tensor_51_cast)[name = tensor("op_820_cast")]; + tensor var_821_perm_0 = const()[name = tensor("op_821_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_828 = const()[name = tensor("op_828"), val = tensor([1, 77, 12, 64])]; + tensor var_829_cast = reshape(shape = var_828, x = tensor_53_cast)[name = tensor("op_829_cast")]; + tensor var_830_perm_0 = const()[name = tensor("op_830_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_832 = const()[name = tensor("op_832"), val = tensor([12, -1, 64])]; + tensor transpose_18 = transpose(perm = var_830_perm_0, x = var_829_cast)[name = tensor("transpose_18")]; + tensor query_states_17_cast = reshape(shape = var_832, x = transpose_18)[name = tensor("query_states_17_cast")]; + tensor var_834 = const()[name = tensor("op_834"), val = tensor([12, -1, 64])]; + tensor transpose_20 = transpose(perm = var_814_perm_0, x = var_813_cast)[name = tensor("transpose_20")]; + tensor key_states_35_cast = reshape(shape = var_834, x = transpose_20)[name = tensor("key_states_35_cast")]; + tensor var_836 = const()[name = tensor("op_836"), val = tensor([12, -1, 64])]; + tensor transpose_19 = transpose(perm = var_821_perm_0, x = var_820_cast)[name = tensor("transpose_19")]; + tensor value_states_35_cast = reshape(shape = var_836, x = transpose_19)[name = tensor("value_states_35_cast")]; + tensor var_839_perm_0 = const()[name = tensor("op_839_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor transpose_17 = transpose(perm = var_839_perm_0, x = key_states_35_cast)[name = tensor("transpose_17")]; + tensor attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = query_states_17_cast, y = transpose_17)[name = tensor("attn_weights_49_cast")]; + tensor var_841 = const()[name = tensor("op_841"), val = tensor([1, 12, 77, 77])]; + tensor var_842_cast = reshape(shape = var_841, x = attn_weights_49_cast)[name = tensor("op_842_cast")]; + tensor attn_weights_51_cast = add(x = var_842_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_51_cast")]; + tensor var_847 = const()[name = tensor("op_847"), val = tensor([12, 77, 77])]; + tensor input_133_cast = reshape(shape = var_847, x = attn_weights_51_cast)[name = tensor("input_133_cast")]; + tensor input_135_cast = softmax(axis = var_5, x = input_133_cast)[name = tensor("input_135_cast")]; + tensor attn_output_49_transpose_x_0 = const()[name = tensor("attn_output_49_transpose_x_0"), val = tensor(false)]; + tensor attn_output_49_transpose_y_0 = const()[name = tensor("attn_output_49_transpose_y_0"), val = tensor(false)]; + tensor attn_output_49_cast = matmul(transpose_x = attn_output_49_transpose_x_0, transpose_y = attn_output_49_transpose_y_0, x = input_135_cast, y = value_states_35_cast)[name = tensor("attn_output_49_cast")]; + tensor var_852 = const()[name = tensor("op_852"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_51_cast = reshape(shape = var_852, x = attn_output_49_cast)[name = tensor("attn_output_51_cast")]; + tensor attn_output_53_perm_0 = const()[name = tensor("attn_output_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_855 = const()[name = tensor("op_855"), val = tensor([1, 77, 768])]; + tensor transpose_16 = transpose(perm = attn_output_53_perm_0, x = attn_output_51_cast)[name = tensor("transpose_16")]; + tensor input_137_cast = reshape(shape = var_855, x = transpose_16)[name = tensor("input_137_cast")]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192982400)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194162112)))]; + tensor hidden_states_51_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16, x = input_137_cast)[name = tensor("hidden_states_51_cast")]; + tensor input_139_cast = add(x = input_131_cast, y = hidden_states_51_cast)[name = tensor("input_139_cast")]; + tensor input_141_axes_0 = const()[name = tensor("input_141_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194163712)))]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194165312)))]; + tensor input_141_cast = layer_norm(axes = input_141_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16, x = input_139_cast)[name = tensor("input_141_cast")]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194166912)))]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198885568)))]; + tensor input_143_cast = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16, x = input_141_cast)[name = tensor("input_143_cast")]; + tensor var_870_to_fp16 = const()[name = tensor("op_870_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_871_cast = mul(x = input_143_cast, y = var_870_to_fp16)[name = tensor("op_871_cast")]; + tensor var_872_cast = sigmoid(x = var_871_cast)[name = tensor("op_872_cast")]; + tensor input_145_cast = mul(x = input_143_cast, y = var_872_cast)[name = tensor("input_145_cast")]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198891776)))]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203610432)))]; + tensor hidden_states_53_cast = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16, x = input_145_cast)[name = tensor("hidden_states_53_cast")]; + tensor input_147_cast = add(x = input_139_cast, y = hidden_states_53_cast)[name = tensor("input_147_cast")]; + tensor hidden_states_55_axes_0 = const()[name = tensor("hidden_states_55_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203612032)))]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203613632)))]; + tensor hidden_states_55_cast = layer_norm(axes = hidden_states_55_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16, x = input_147_cast)[name = tensor("hidden_states_55_cast")]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203615232)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204794944)))]; + tensor var_896_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16, x = hidden_states_55_cast)[name = tensor("op_896_cast")]; + tensor var_897_to_fp16 = const()[name = tensor("op_897_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_59_cast = mul(x = var_896_cast, y = var_897_to_fp16)[name = tensor("tensor_59_cast")]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204796544)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205976256)))]; + tensor tensor_55_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16, x = hidden_states_55_cast)[name = tensor("tensor_55_cast")]; + tensor var_902 = const()[name = tensor("op_902"), val = tensor([1, -1, 12, 64])]; + tensor var_903_cast = reshape(shape = var_902, x = tensor_55_cast)[name = tensor("op_903_cast")]; + tensor var_904_perm_0 = const()[name = tensor("op_904_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205977856)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207157568)))]; + tensor tensor_57_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16, x = hidden_states_55_cast)[name = tensor("tensor_57_cast")]; + tensor var_909 = const()[name = tensor("op_909"), val = tensor([1, -1, 12, 64])]; + tensor var_910_cast = reshape(shape = var_909, x = tensor_57_cast)[name = tensor("op_910_cast")]; + tensor var_911_perm_0 = const()[name = tensor("op_911_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_918 = const()[name = tensor("op_918"), val = tensor([1, 77, 12, 64])]; + tensor var_919_cast = reshape(shape = var_918, x = tensor_59_cast)[name = tensor("op_919_cast")]; + tensor var_920_perm_0 = const()[name = tensor("op_920_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_922 = const()[name = tensor("op_922"), val = tensor([12, -1, 64])]; + tensor transpose_13 = transpose(perm = var_920_perm_0, x = var_919_cast)[name = tensor("transpose_13")]; + tensor query_states_19_cast = reshape(shape = var_922, x = transpose_13)[name = tensor("query_states_19_cast")]; + tensor var_924 = const()[name = tensor("op_924"), val = tensor([12, -1, 64])]; + tensor transpose_15 = transpose(perm = var_904_perm_0, x = var_903_cast)[name = tensor("transpose_15")]; + tensor key_states_39_cast = reshape(shape = var_924, x = transpose_15)[name = tensor("key_states_39_cast")]; + tensor var_926 = const()[name = tensor("op_926"), val = tensor([12, -1, 64])]; + tensor transpose_14 = transpose(perm = var_911_perm_0, x = var_910_cast)[name = tensor("transpose_14")]; + tensor value_states_39_cast = reshape(shape = var_926, x = transpose_14)[name = tensor("value_states_39_cast")]; + tensor var_929_perm_0 = const()[name = tensor("op_929_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_55_transpose_x_0 = const()[name = tensor("attn_weights_55_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_55_transpose_y_0 = const()[name = tensor("attn_weights_55_transpose_y_0"), val = tensor(false)]; + tensor transpose_12 = transpose(perm = var_929_perm_0, x = key_states_39_cast)[name = tensor("transpose_12")]; + tensor attn_weights_55_cast = matmul(transpose_x = attn_weights_55_transpose_x_0, transpose_y = attn_weights_55_transpose_y_0, x = query_states_19_cast, y = transpose_12)[name = tensor("attn_weights_55_cast")]; + tensor var_931 = const()[name = tensor("op_931"), val = tensor([1, 12, 77, 77])]; + tensor var_932_cast = reshape(shape = var_931, x = attn_weights_55_cast)[name = tensor("op_932_cast")]; + tensor attn_weights_57_cast = add(x = var_932_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_57_cast")]; + tensor var_937 = const()[name = tensor("op_937"), val = tensor([12, 77, 77])]; + tensor input_149_cast = reshape(shape = var_937, x = attn_weights_57_cast)[name = tensor("input_149_cast")]; + tensor input_151_cast = softmax(axis = var_5, x = input_149_cast)[name = tensor("input_151_cast")]; + tensor attn_output_55_transpose_x_0 = const()[name = tensor("attn_output_55_transpose_x_0"), val = tensor(false)]; + tensor attn_output_55_transpose_y_0 = const()[name = tensor("attn_output_55_transpose_y_0"), val = tensor(false)]; + tensor attn_output_55_cast = matmul(transpose_x = attn_output_55_transpose_x_0, transpose_y = attn_output_55_transpose_y_0, x = input_151_cast, y = value_states_39_cast)[name = tensor("attn_output_55_cast")]; + tensor var_942 = const()[name = tensor("op_942"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_57_cast = reshape(shape = var_942, x = attn_output_55_cast)[name = tensor("attn_output_57_cast")]; + tensor attn_output_59_perm_0 = const()[name = tensor("attn_output_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_945 = const()[name = tensor("op_945"), val = tensor([1, 77, 768])]; + tensor transpose_11 = transpose(perm = attn_output_59_perm_0, x = attn_output_57_cast)[name = tensor("transpose_11")]; + tensor input_153_cast = reshape(shape = var_945, x = transpose_11)[name = tensor("input_153_cast")]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(207159168)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208338880)))]; + tensor hidden_states_57_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16, x = input_153_cast)[name = tensor("hidden_states_57_cast")]; + tensor input_155_cast = add(x = input_147_cast, y = hidden_states_57_cast)[name = tensor("input_155_cast")]; + tensor input_157_axes_0 = const()[name = tensor("input_157_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208340480)))]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208342080)))]; + tensor input_157_cast = layer_norm(axes = input_157_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16, x = input_155_cast)[name = tensor("input_157_cast")]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208343680)))]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213062336)))]; + tensor input_159_cast = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16, x = input_157_cast)[name = tensor("input_159_cast")]; + tensor var_960_to_fp16 = const()[name = tensor("op_960_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_961_cast = mul(x = input_159_cast, y = var_960_to_fp16)[name = tensor("op_961_cast")]; + tensor var_962_cast = sigmoid(x = var_961_cast)[name = tensor("op_962_cast")]; + tensor input_161_cast = mul(x = input_159_cast, y = var_962_cast)[name = tensor("input_161_cast")]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213068544)))]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217787200)))]; + tensor hidden_states_59_cast = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16, x = input_161_cast)[name = tensor("hidden_states_59_cast")]; + tensor input_163_cast = add(x = input_155_cast, y = hidden_states_59_cast)[name = tensor("input_163_cast")]; + tensor hidden_states_61_axes_0 = const()[name = tensor("hidden_states_61_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217788800)))]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217790400)))]; + tensor hidden_states_61_cast = layer_norm(axes = hidden_states_61_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16, x = input_163_cast)[name = tensor("hidden_states_61_cast")]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217792000)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218971712)))]; + tensor var_986_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16, x = hidden_states_61_cast)[name = tensor("op_986_cast")]; + tensor var_987_to_fp16 = const()[name = tensor("op_987_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_65_cast = mul(x = var_986_cast, y = var_987_to_fp16)[name = tensor("tensor_65_cast")]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218973312)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220153024)))]; + tensor tensor_61_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16, x = hidden_states_61_cast)[name = tensor("tensor_61_cast")]; + tensor var_992 = const()[name = tensor("op_992"), val = tensor([1, -1, 12, 64])]; + tensor var_993_cast = reshape(shape = var_992, x = tensor_61_cast)[name = tensor("op_993_cast")]; + tensor var_994_perm_0 = const()[name = tensor("op_994_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220154624)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(221334336)))]; + tensor tensor_63_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16, x = hidden_states_61_cast)[name = tensor("tensor_63_cast")]; + tensor var_999 = const()[name = tensor("op_999"), val = tensor([1, -1, 12, 64])]; + tensor var_1000_cast = reshape(shape = var_999, x = tensor_63_cast)[name = tensor("op_1000_cast")]; + tensor var_1001_perm_0 = const()[name = tensor("op_1001_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1008 = const()[name = tensor("op_1008"), val = tensor([1, 77, 12, 64])]; + tensor var_1009_cast = reshape(shape = var_1008, x = tensor_65_cast)[name = tensor("op_1009_cast")]; + tensor var_1010_perm_0 = const()[name = tensor("op_1010_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1012 = const()[name = tensor("op_1012"), val = tensor([12, -1, 64])]; + tensor transpose_8 = transpose(perm = var_1010_perm_0, x = var_1009_cast)[name = tensor("transpose_8")]; + tensor query_states_21_cast = reshape(shape = var_1012, x = transpose_8)[name = tensor("query_states_21_cast")]; + tensor var_1014 = const()[name = tensor("op_1014"), val = tensor([12, -1, 64])]; + tensor transpose_10 = transpose(perm = var_994_perm_0, x = var_993_cast)[name = tensor("transpose_10")]; + tensor key_states_43_cast = reshape(shape = var_1014, x = transpose_10)[name = tensor("key_states_43_cast")]; + tensor var_1016 = const()[name = tensor("op_1016"), val = tensor([12, -1, 64])]; + tensor transpose_9 = transpose(perm = var_1001_perm_0, x = var_1000_cast)[name = tensor("transpose_9")]; + tensor value_states_43_cast = reshape(shape = var_1016, x = transpose_9)[name = tensor("value_states_43_cast")]; + tensor var_1019_perm_0 = const()[name = tensor("op_1019_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor transpose_7 = transpose(perm = var_1019_perm_0, x = key_states_43_cast)[name = tensor("transpose_7")]; + tensor attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = query_states_21_cast, y = transpose_7)[name = tensor("attn_weights_61_cast")]; + tensor var_1021 = const()[name = tensor("op_1021"), val = tensor([1, 12, 77, 77])]; + tensor var_1022_cast = reshape(shape = var_1021, x = attn_weights_61_cast)[name = tensor("op_1022_cast")]; + tensor attn_weights_63_cast = add(x = var_1022_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_63_cast")]; + tensor var_1027 = const()[name = tensor("op_1027"), val = tensor([12, 77, 77])]; + tensor input_165_cast = reshape(shape = var_1027, x = attn_weights_63_cast)[name = tensor("input_165_cast")]; + tensor input_167_cast = softmax(axis = var_5, x = input_165_cast)[name = tensor("input_167_cast")]; + tensor attn_output_61_transpose_x_0 = const()[name = tensor("attn_output_61_transpose_x_0"), val = tensor(false)]; + tensor attn_output_61_transpose_y_0 = const()[name = tensor("attn_output_61_transpose_y_0"), val = tensor(false)]; + tensor attn_output_61_cast = matmul(transpose_x = attn_output_61_transpose_x_0, transpose_y = attn_output_61_transpose_y_0, x = input_167_cast, y = value_states_43_cast)[name = tensor("attn_output_61_cast")]; + tensor var_1032 = const()[name = tensor("op_1032"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_63_cast = reshape(shape = var_1032, x = attn_output_61_cast)[name = tensor("attn_output_63_cast")]; + tensor attn_output_65_perm_0 = const()[name = tensor("attn_output_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1035 = const()[name = tensor("op_1035"), val = tensor([1, 77, 768])]; + tensor transpose_6 = transpose(perm = attn_output_65_perm_0, x = attn_output_63_cast)[name = tensor("transpose_6")]; + tensor input_169_cast = reshape(shape = var_1035, x = transpose_6)[name = tensor("input_169_cast")]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(221335936)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222515648)))]; + tensor hidden_states_63_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16, x = input_169_cast)[name = tensor("hidden_states_63_cast")]; + tensor input_171_cast = add(x = input_163_cast, y = hidden_states_63_cast)[name = tensor("input_171_cast")]; + tensor input_173_axes_0 = const()[name = tensor("input_173_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222517248)))]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222518848)))]; + tensor input_173_cast = layer_norm(axes = input_173_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16, x = input_171_cast)[name = tensor("input_173_cast")]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222520448)))]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227239104)))]; + tensor input_175_cast = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16, x = input_173_cast)[name = tensor("input_175_cast")]; + tensor var_1050_to_fp16 = const()[name = tensor("op_1050_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_1051_cast = mul(x = input_175_cast, y = var_1050_to_fp16)[name = tensor("op_1051_cast")]; + tensor var_1052_cast = sigmoid(x = var_1051_cast)[name = tensor("op_1052_cast")]; + tensor input_177_cast = mul(x = input_175_cast, y = var_1052_cast)[name = tensor("input_177_cast")]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(227245312)))]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231963968)))]; + tensor hidden_states_65_cast = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16, x = input_177_cast)[name = tensor("hidden_states_65_cast")]; + tensor input_179_cast = add(x = input_171_cast, y = hidden_states_65_cast)[name = tensor("input_179_cast")]; + tensor input_179_cast_to_fp32_dtype_0 = const()[name = tensor("input_179_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor hidden_states_67_axes_0 = const()[name = tensor("hidden_states_67_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231965568)))]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231967168)))]; + tensor hidden_states_67_cast = layer_norm(axes = hidden_states_67_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16, x = input_179_cast)[name = tensor("hidden_states_67_cast")]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231968768)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233148480)))]; + tensor var_1076_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16, x = hidden_states_67_cast)[name = tensor("op_1076_cast")]; + tensor var_1077_to_fp16 = const()[name = tensor("op_1077_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_cast = mul(x = var_1076_cast, y = var_1077_to_fp16)[name = tensor("tensor_cast")]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(233150080)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234329792)))]; + tensor tensor_67_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16, x = hidden_states_67_cast)[name = tensor("tensor_67_cast")]; + tensor var_1082 = const()[name = tensor("op_1082"), val = tensor([1, -1, 12, 64])]; + tensor var_1083_cast = reshape(shape = var_1082, x = tensor_67_cast)[name = tensor("op_1083_cast")]; + tensor var_1084_perm_0 = const()[name = tensor("op_1084_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234331392)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235511104)))]; + tensor tensor_69_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16, x = hidden_states_67_cast)[name = tensor("tensor_69_cast")]; + tensor var_1089 = const()[name = tensor("op_1089"), val = tensor([1, -1, 12, 64])]; + tensor var_1090_cast = reshape(shape = var_1089, x = tensor_69_cast)[name = tensor("op_1090_cast")]; + tensor var_1091_perm_0 = const()[name = tensor("op_1091_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1098 = const()[name = tensor("op_1098"), val = tensor([1, 77, 12, 64])]; + tensor var_1099_cast = reshape(shape = var_1098, x = tensor_cast)[name = tensor("op_1099_cast")]; + tensor var_1100_perm_0 = const()[name = tensor("op_1100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1102 = const()[name = tensor("op_1102"), val = tensor([12, -1, 64])]; + tensor transpose_3 = transpose(perm = var_1100_perm_0, x = var_1099_cast)[name = tensor("transpose_3")]; + tensor query_states_cast = reshape(shape = var_1102, x = transpose_3)[name = tensor("query_states_cast")]; + tensor var_1104 = const()[name = tensor("op_1104"), val = tensor([12, -1, 64])]; + tensor transpose_5 = transpose(perm = var_1084_perm_0, x = var_1083_cast)[name = tensor("transpose_5")]; + tensor key_states_cast = reshape(shape = var_1104, x = transpose_5)[name = tensor("key_states_cast")]; + tensor var_1106 = const()[name = tensor("op_1106"), val = tensor([12, -1, 64])]; + tensor transpose_4 = transpose(perm = var_1091_perm_0, x = var_1090_cast)[name = tensor("transpose_4")]; + tensor value_states_cast = reshape(shape = var_1106, x = transpose_4)[name = tensor("value_states_cast")]; + tensor var_1109_perm_0 = const()[name = tensor("op_1109_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_67_transpose_x_0 = const()[name = tensor("attn_weights_67_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_67_transpose_y_0 = const()[name = tensor("attn_weights_67_transpose_y_0"), val = tensor(false)]; + tensor transpose_2 = transpose(perm = var_1109_perm_0, x = key_states_cast)[name = tensor("transpose_2")]; + tensor attn_weights_67_cast = matmul(transpose_x = attn_weights_67_transpose_x_0, transpose_y = attn_weights_67_transpose_y_0, x = query_states_cast, y = transpose_2)[name = tensor("attn_weights_67_cast")]; + tensor var_1111 = const()[name = tensor("op_1111"), val = tensor([1, 12, 77, 77])]; + tensor var_1112_cast = reshape(shape = var_1111, x = attn_weights_67_cast)[name = tensor("op_1112_cast")]; + tensor attn_weights_69_cast = add(x = var_1112_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_69_cast")]; + tensor var_1117 = const()[name = tensor("op_1117"), val = tensor([12, 77, 77])]; + tensor input_181_cast = reshape(shape = var_1117, x = attn_weights_69_cast)[name = tensor("input_181_cast")]; + tensor input_183_cast = softmax(axis = var_5, x = input_181_cast)[name = tensor("input_183_cast")]; + tensor attn_output_67_transpose_x_0 = const()[name = tensor("attn_output_67_transpose_x_0"), val = tensor(false)]; + tensor attn_output_67_transpose_y_0 = const()[name = tensor("attn_output_67_transpose_y_0"), val = tensor(false)]; + tensor attn_output_67_cast = matmul(transpose_x = attn_output_67_transpose_x_0, transpose_y = attn_output_67_transpose_y_0, x = input_183_cast, y = value_states_cast)[name = tensor("attn_output_67_cast")]; + tensor var_1122 = const()[name = tensor("op_1122"), val = tensor([1, 12, 77, 64])]; + tensor attn_output_69_cast = reshape(shape = var_1122, x = attn_output_67_cast)[name = tensor("attn_output_69_cast")]; + tensor attn_output_perm_0 = const()[name = tensor("attn_output_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1125 = const()[name = tensor("op_1125"), val = tensor([1, 77, 768])]; + tensor transpose_1 = transpose(perm = attn_output_perm_0, x = attn_output_69_cast)[name = tensor("transpose_1")]; + tensor input_185_cast = reshape(shape = var_1125, x = transpose_1)[name = tensor("input_185_cast")]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235512704)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236692416)))]; + tensor hidden_states_69_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16, x = input_185_cast)[name = tensor("hidden_states_69_cast")]; + tensor input_187_cast = add(x = input_179_cast, y = hidden_states_69_cast)[name = tensor("input_187_cast")]; + tensor input_189_axes_0 = const()[name = tensor("input_189_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236694016)))]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236695616)))]; + tensor input_189_cast = layer_norm(axes = input_189_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16, x = input_187_cast)[name = tensor("input_189_cast")]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236697216)))]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(241415872)))]; + tensor input_191_cast = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16, x = input_189_cast)[name = tensor("input_191_cast")]; + tensor var_1140_to_fp16 = const()[name = tensor("op_1140_to_fp16"), val = tensor(0x1.b3cp+0)]; + tensor var_1141_cast = mul(x = input_191_cast, y = var_1140_to_fp16)[name = tensor("op_1141_cast")]; + tensor var_1142_cast = sigmoid(x = var_1141_cast)[name = tensor("op_1142_cast")]; + tensor input_193_cast = mul(x = input_191_cast, y = var_1142_cast)[name = tensor("input_193_cast")]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(241422080)))]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(246140736)))]; + tensor hidden_states_cast = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16, x = input_193_cast)[name = tensor("hidden_states_cast")]; + tensor input_cast = add(x = input_187_cast, y = hidden_states_cast)[name = tensor("input_cast")]; + tensor last_hidden_state_axes_0 = const()[name = tensor("last_hidden_state_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_final_layer_norm_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(246142336)))]; + tensor text_encoder_text_model_final_layer_norm_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(246143936)))]; + tensor last_hidden_state_cast = layer_norm(axes = last_hidden_state_axes_0, beta = text_encoder_text_model_final_layer_norm_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_final_layer_norm_weight_to_fp16, x = input_cast)[name = tensor("last_hidden_state_cast")]; + tensor var_1156 = const()[name = tensor("op_1156"), val = tensor([0])]; + tensor var_1158 = reduce_argmax(axis = var_5, keep_dims = var_6, x = cast_526)[name = tensor("op_1158")]; + tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(1)]; + tensor stack_0 = stack(axis = stack_0_axis_0, values = (var_1156, var_1158))[name = tensor("stack_0")]; + tensor var_1160_transpose_batch_dims_0 = const()[name = tensor("op_1160_transpose_batch_dims_0"), val = tensor(0)]; + tensor var_1160_transpose_cast = gather_nd(batch_dims = var_1160_transpose_batch_dims_0, indices = stack_0, x = last_hidden_state_cast)[name = tensor("op_1160_transpose_cast")]; + tensor var_1160_cast_to_fp32_dtype_0 = const()[name = tensor("op_1160_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor pooled_outputs = cast(dtype = var_1160_cast_to_fp32_dtype_0, x = var_1160_transpose_cast)[name = tensor("cast_125")]; + tensor hidden_embeds = cast(dtype = input_179_cast_to_fp32_dtype_0, x = input_179_cast)[name = tensor("cast_161")]; + } -> (hidden_embeds, pooled_outputs); +} \ No newline at end of file diff --git a/compiled/TextEncoder.mlmodelc/weights/weight.bin b/compiled/TextEncoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..3cd2ebb4765b40c98cb09d0e35c908e9a2e229ef --- /dev/null +++ b/compiled/TextEncoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a86533724aadf50c8f5539592d440887a484f60002d7967505c69c7faf4d7797 +size 246145536 diff --git a/compiled/TextEncoder2.mlmodelc/analytics/coremldata.bin b/compiled/TextEncoder2.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..ab0c2019dd8664c30fb272fd43f2f559a5fbd2b8 --- /dev/null +++ b/compiled/TextEncoder2.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eef66b480388714bb62f2f0f2f97a8953e44acbb00b25f9a9fd63c759f4f0e83 +size 207 diff --git a/compiled/TextEncoder2.mlmodelc/coremldata.bin b/compiled/TextEncoder2.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..bca87797b63a2002be08fcd99a522c5011db367c --- /dev/null +++ b/compiled/TextEncoder2.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9ff26866d8d8fbb4e53a0628f8aab5f7edf1b3ec763a96e6812c8f7fbf4c9827 +size 825 diff --git a/compiled/TextEncoder2.mlmodelc/metadata.json b/compiled/TextEncoder2.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..14166b3db2c377b93a3f3b936bc215a6bf082ec1 --- /dev/null +++ b/compiled/TextEncoder2.mlmodelc/metadata.json @@ -0,0 +1,82 @@ +[ + { + "shortDescription" : "Stable Diffusion generates images conditioned on text and\/or other images as input through the diffusion process. Please refer to https:\/\/arxiv.org\/abs\/2112.10752 for details.", + "metadataOutputVersion" : "3.0", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32)", + "shortDescription" : "Hidden states after the encoder layers", + "shape" : "[]", + "name" : "hidden_embeds", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32)", + "shortDescription" : "The version of the `last_hidden_state` output after pooling", + "shape" : "[]", + "name" : "pooled_outputs", + "type" : "MultiArray" + } + ], + "version" : "diffusers\/stable-diffusion-xl-base-1.0", + "modelParameters" : [ + + ], + "author" : "Please refer to the Model Card available at huggingface.co\/diffusers\/stable-diffusion-xl-base-1.0", + "specificationVersion" : 7, + "storagePrecision" : "Float16", + "license" : "OpenRAIL (https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license)", + "mlProgramOperationTypeHistogram" : { + "Ios16.cast" : 3, + "Ios16.mul" : 32, + "Ios16.layerNorm" : 65, + "Stack" : 1, + "Transpose" : 160, + "Ios16.linear" : 193, + "Ios16.add" : 97, + "Ios16.matmul" : 64, + "Ios16.gelu" : 32, + "Ios16.softmax" : 32, + "Ios16.gatherNd" : 1, + "Ios16.gather" : 1, + "Ios16.reshape" : 320, + "Ios16.reduceArgmax" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 77)", + "shortDescription" : "The token ids that represent the input text", + "shape" : "[1, 77]", + "name" : "input_ids", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.version" : "7.0b1", + "com.github.apple.coremltools.source" : "torch==2.0.1+cu117" + }, + "generatedClassName" : "Stable_Diffusion_version_diffusers_stable_diffusion_xl_base_1_0_text_encoder_2", + "method" : "predict" + } +] \ No newline at end of file diff --git a/compiled/TextEncoder2.mlmodelc/model.mil b/compiled/TextEncoder2.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..62c9f845ab22560659be573a0c15fcd970d3ec60 --- /dev/null +++ b/compiled/TextEncoder2.mlmodelc/model.mil @@ -0,0 +1,2275 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.33.4"}, {"coremlc-version", "1839.0.0"}, {"coremltools-component-torch", "2.0.1+cu117"}, {"coremltools-version", "7.0b1"}})] +{ + func main(tensor input_ids) { + tensor var_5 = const()[name = tensor("op_5"), val = tensor(-1)]; + tensor var_6 = const()[name = tensor("op_6"), val = tensor(false)]; + tensor cast_1_dtype_0 = const()[name = tensor("cast_1_dtype_0"), val = tensor("int32")]; + tensor inputs_embeds_axis_0 = const()[name = tensor("inputs_embeds_axis_0"), val = tensor(0)]; + tensor inputs_embeds_batch_dims_0 = const()[name = tensor("inputs_embeds_batch_dims_0"), val = tensor(0)]; + tensor text_encoder_text_model_embeddings_token_embedding_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_embeddings_token_embedding_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor cast_1322 = cast(dtype = cast_1_dtype_0, x = input_ids)[name = tensor("cast_1322")]; + tensor inputs_embeds_cast = gather(axis = inputs_embeds_axis_0, batch_dims = inputs_embeds_batch_dims_0, indices = cast_1322, x = text_encoder_text_model_embeddings_token_embedding_weight_to_fp16)[name = tensor("inputs_embeds_cast")]; + tensor position_embeddings_to_fp16 = const()[name = tensor("position_embeddings_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126484608)))]; + tensor input_3_cast = add(x = inputs_embeds_cast, y = position_embeddings_to_fp16)[name = tensor("input_3_cast")]; + tensor hidden_states_1_axes_0 = const()[name = tensor("hidden_states_1_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126681792)))]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126684416)))]; + tensor var_12_to_fp16 = const()[name = tensor("op_12_to_fp16"), val = tensor(0x1.5p-17)]; + tensor hidden_states_1_cast = layer_norm(axes = hidden_states_1_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm1_weight_to_fp16, x = input_3_cast)[name = tensor("hidden_states_1_cast")]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126687040)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129963904)))]; + tensor var_128_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_q_proj_weight_to_fp16, x = hidden_states_1_cast)[name = tensor("op_128_cast")]; + tensor var_129_to_fp16 = const()[name = tensor("op_129_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_5_cast = mul(x = var_128_cast, y = var_129_to_fp16)[name = tensor("tensor_5_cast")]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129966528)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133243392)))]; + tensor tensor_1_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_k_proj_weight_to_fp16, x = hidden_states_1_cast)[name = tensor("tensor_1_cast")]; + tensor var_134 = const()[name = tensor("op_134"), val = tensor([1, -1, 20, 64])]; + tensor var_135_cast = reshape(shape = var_134, x = tensor_1_cast)[name = tensor("op_135_cast")]; + tensor var_136_perm_0 = const()[name = tensor("op_136_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133246016)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136522880)))]; + tensor tensor_3_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_v_proj_weight_to_fp16, x = hidden_states_1_cast)[name = tensor("tensor_3_cast")]; + tensor var_141 = const()[name = tensor("op_141"), val = tensor([1, -1, 20, 64])]; + tensor var_142_cast = reshape(shape = var_141, x = tensor_3_cast)[name = tensor("op_142_cast")]; + tensor var_143_perm_0 = const()[name = tensor("op_143_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_150 = const()[name = tensor("op_150"), val = tensor([1, 77, 20, 64])]; + tensor var_151_cast = reshape(shape = var_150, x = tensor_5_cast)[name = tensor("op_151_cast")]; + tensor var_152_perm_0 = const()[name = tensor("op_152_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_154 = const()[name = tensor("op_154"), val = tensor([20, -1, 64])]; + tensor transpose_158 = transpose(perm = var_152_perm_0, x = var_151_cast)[name = tensor("transpose_158")]; + tensor query_states_1_cast = reshape(shape = var_154, x = transpose_158)[name = tensor("query_states_1_cast")]; + tensor var_156 = const()[name = tensor("op_156"), val = tensor([20, -1, 64])]; + tensor transpose_160 = transpose(perm = var_136_perm_0, x = var_135_cast)[name = tensor("transpose_160")]; + tensor key_states_3_cast = reshape(shape = var_156, x = transpose_160)[name = tensor("key_states_3_cast")]; + tensor var_158 = const()[name = tensor("op_158"), val = tensor([20, -1, 64])]; + tensor transpose_159 = transpose(perm = var_143_perm_0, x = var_142_cast)[name = tensor("transpose_159")]; + tensor value_states_3_cast = reshape(shape = var_158, x = transpose_159)[name = tensor("value_states_3_cast")]; + tensor var_161_perm_0 = const()[name = tensor("op_161_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor transpose_157 = transpose(perm = var_161_perm_0, x = key_states_3_cast)[name = tensor("transpose_157")]; + tensor attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = query_states_1_cast, y = transpose_157)[name = tensor("attn_weights_1_cast")]; + tensor var_163 = const()[name = tensor("op_163"), val = tensor([1, 20, 77, 77])]; + tensor var_164_cast = reshape(shape = var_163, x = attn_weights_1_cast)[name = tensor("op_164_cast")]; + tensor causal_attention_mask_to_fp16 = const()[name = tensor("causal_attention_mask_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136525504)))]; + tensor attn_weights_3_cast = add(x = var_164_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_3_cast")]; + tensor var_169 = const()[name = tensor("op_169"), val = tensor([20, 77, 77])]; + tensor input_5_cast = reshape(shape = var_169, x = attn_weights_3_cast)[name = tensor("input_5_cast")]; + tensor input_7_cast = softmax(axis = var_5, x = input_5_cast)[name = tensor("input_7_cast")]; + tensor attn_output_1_transpose_x_0 = const()[name = tensor("attn_output_1_transpose_x_0"), val = tensor(false)]; + tensor attn_output_1_transpose_y_0 = const()[name = tensor("attn_output_1_transpose_y_0"), val = tensor(false)]; + tensor attn_output_1_cast = matmul(transpose_x = attn_output_1_transpose_x_0, transpose_y = attn_output_1_transpose_y_0, x = input_7_cast, y = value_states_3_cast)[name = tensor("attn_output_1_cast")]; + tensor var_174 = const()[name = tensor("op_174"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_3_cast = reshape(shape = var_174, x = attn_output_1_cast)[name = tensor("attn_output_3_cast")]; + tensor attn_output_5_perm_0 = const()[name = tensor("attn_output_5_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_177 = const()[name = tensor("op_177"), val = tensor([1, 77, 1280])]; + tensor transpose_156 = transpose(perm = attn_output_5_perm_0, x = attn_output_3_cast)[name = tensor("transpose_156")]; + tensor input_9_cast = reshape(shape = var_177, x = transpose_156)[name = tensor("input_9_cast")]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136537472)))]; + tensor text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139814336)))]; + tensor hidden_states_3_cast = linear(bias = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_self_attn_out_proj_weight_to_fp16, x = input_9_cast)[name = tensor("hidden_states_3_cast")]; + tensor input_11_cast = add(x = input_3_cast, y = hidden_states_3_cast)[name = tensor("input_11_cast")]; + tensor input_13_axes_0 = const()[name = tensor("input_13_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139816960)))]; + tensor text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139819584)))]; + tensor input_13_cast = layer_norm(axes = input_13_axes_0, beta = text_encoder_text_model_encoder_layers_0_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_0_layer_norm2_weight_to_fp16, x = input_11_cast)[name = tensor("input_13_cast")]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139822208)))]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152929472)))]; + tensor input_15_cast = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_mlp_fc1_weight_to_fp16, x = input_13_cast)[name = tensor("input_15_cast")]; + tensor input_17_mode_0 = const()[name = tensor("input_17_mode_0"), val = tensor("EXACT")]; + tensor input_17_cast = gelu(mode = input_17_mode_0, x = input_15_cast)[name = tensor("input_17_cast")]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152939776)))]; + tensor text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166047040)))]; + tensor hidden_states_5_cast = linear(bias = text_encoder_text_model_encoder_layers_0_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_0_mlp_fc2_weight_to_fp16, x = input_17_cast)[name = tensor("hidden_states_5_cast")]; + tensor input_19_cast = add(x = input_11_cast, y = hidden_states_5_cast)[name = tensor("input_19_cast")]; + tensor hidden_states_7_axes_0 = const()[name = tensor("hidden_states_7_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166049664)))]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166052288)))]; + tensor hidden_states_7_cast = layer_norm(axes = hidden_states_7_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm1_weight_to_fp16, x = input_19_cast)[name = tensor("hidden_states_7_cast")]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(166054912)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169331776)))]; + tensor var_215_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_q_proj_weight_to_fp16, x = hidden_states_7_cast)[name = tensor("op_215_cast")]; + tensor var_216_to_fp16 = const()[name = tensor("op_216_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_11_cast = mul(x = var_215_cast, y = var_216_to_fp16)[name = tensor("tensor_11_cast")]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169334400)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172611264)))]; + tensor tensor_7_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_k_proj_weight_to_fp16, x = hidden_states_7_cast)[name = tensor("tensor_7_cast")]; + tensor var_221 = const()[name = tensor("op_221"), val = tensor([1, -1, 20, 64])]; + tensor var_222_cast = reshape(shape = var_221, x = tensor_7_cast)[name = tensor("op_222_cast")]; + tensor var_223_perm_0 = const()[name = tensor("op_223_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(172613888)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175890752)))]; + tensor tensor_9_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_v_proj_weight_to_fp16, x = hidden_states_7_cast)[name = tensor("tensor_9_cast")]; + tensor var_228 = const()[name = tensor("op_228"), val = tensor([1, -1, 20, 64])]; + tensor var_229_cast = reshape(shape = var_228, x = tensor_9_cast)[name = tensor("op_229_cast")]; + tensor var_230_perm_0 = const()[name = tensor("op_230_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_237 = const()[name = tensor("op_237"), val = tensor([1, 77, 20, 64])]; + tensor var_238_cast = reshape(shape = var_237, x = tensor_11_cast)[name = tensor("op_238_cast")]; + tensor var_239_perm_0 = const()[name = tensor("op_239_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_241 = const()[name = tensor("op_241"), val = tensor([20, -1, 64])]; + tensor transpose_153 = transpose(perm = var_239_perm_0, x = var_238_cast)[name = tensor("transpose_153")]; + tensor query_states_3_cast = reshape(shape = var_241, x = transpose_153)[name = tensor("query_states_3_cast")]; + tensor var_243 = const()[name = tensor("op_243"), val = tensor([20, -1, 64])]; + tensor transpose_155 = transpose(perm = var_223_perm_0, x = var_222_cast)[name = tensor("transpose_155")]; + tensor key_states_7_cast = reshape(shape = var_243, x = transpose_155)[name = tensor("key_states_7_cast")]; + tensor var_245 = const()[name = tensor("op_245"), val = tensor([20, -1, 64])]; + tensor transpose_154 = transpose(perm = var_230_perm_0, x = var_229_cast)[name = tensor("transpose_154")]; + tensor value_states_7_cast = reshape(shape = var_245, x = transpose_154)[name = tensor("value_states_7_cast")]; + tensor var_248_perm_0 = const()[name = tensor("op_248_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_7_transpose_x_0 = const()[name = tensor("attn_weights_7_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_7_transpose_y_0 = const()[name = tensor("attn_weights_7_transpose_y_0"), val = tensor(false)]; + tensor transpose_152 = transpose(perm = var_248_perm_0, x = key_states_7_cast)[name = tensor("transpose_152")]; + tensor attn_weights_7_cast = matmul(transpose_x = attn_weights_7_transpose_x_0, transpose_y = attn_weights_7_transpose_y_0, x = query_states_3_cast, y = transpose_152)[name = tensor("attn_weights_7_cast")]; + tensor var_250 = const()[name = tensor("op_250"), val = tensor([1, 20, 77, 77])]; + tensor var_251_cast = reshape(shape = var_250, x = attn_weights_7_cast)[name = tensor("op_251_cast")]; + tensor attn_weights_9_cast = add(x = var_251_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_9_cast")]; + tensor var_256 = const()[name = tensor("op_256"), val = tensor([20, 77, 77])]; + tensor input_21_cast = reshape(shape = var_256, x = attn_weights_9_cast)[name = tensor("input_21_cast")]; + tensor input_23_cast = softmax(axis = var_5, x = input_21_cast)[name = tensor("input_23_cast")]; + tensor attn_output_7_transpose_x_0 = const()[name = tensor("attn_output_7_transpose_x_0"), val = tensor(false)]; + tensor attn_output_7_transpose_y_0 = const()[name = tensor("attn_output_7_transpose_y_0"), val = tensor(false)]; + tensor attn_output_7_cast = matmul(transpose_x = attn_output_7_transpose_x_0, transpose_y = attn_output_7_transpose_y_0, x = input_23_cast, y = value_states_7_cast)[name = tensor("attn_output_7_cast")]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_9_cast = reshape(shape = var_261, x = attn_output_7_cast)[name = tensor("attn_output_9_cast")]; + tensor attn_output_11_perm_0 = const()[name = tensor("attn_output_11_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_264 = const()[name = tensor("op_264"), val = tensor([1, 77, 1280])]; + tensor transpose_151 = transpose(perm = attn_output_11_perm_0, x = attn_output_9_cast)[name = tensor("transpose_151")]; + tensor input_25_cast = reshape(shape = var_264, x = transpose_151)[name = tensor("input_25_cast")]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175893376)))]; + tensor text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179170240)))]; + tensor hidden_states_9_cast = linear(bias = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_self_attn_out_proj_weight_to_fp16, x = input_25_cast)[name = tensor("hidden_states_9_cast")]; + tensor input_27_cast = add(x = input_19_cast, y = hidden_states_9_cast)[name = tensor("input_27_cast")]; + tensor input_29_axes_0 = const()[name = tensor("input_29_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179172864)))]; + tensor text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179175488)))]; + tensor input_29_cast = layer_norm(axes = input_29_axes_0, beta = text_encoder_text_model_encoder_layers_1_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_1_layer_norm2_weight_to_fp16, x = input_27_cast)[name = tensor("input_29_cast")]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(179178112)))]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192285376)))]; + tensor input_31_cast = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_mlp_fc1_weight_to_fp16, x = input_29_cast)[name = tensor("input_31_cast")]; + tensor input_33_mode_0 = const()[name = tensor("input_33_mode_0"), val = tensor("EXACT")]; + tensor input_33_cast = gelu(mode = input_33_mode_0, x = input_31_cast)[name = tensor("input_33_cast")]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192295680)))]; + tensor text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205402944)))]; + tensor hidden_states_11_cast = linear(bias = text_encoder_text_model_encoder_layers_1_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_1_mlp_fc2_weight_to_fp16, x = input_33_cast)[name = tensor("hidden_states_11_cast")]; + tensor input_35_cast = add(x = input_27_cast, y = hidden_states_11_cast)[name = tensor("input_35_cast")]; + tensor hidden_states_13_axes_0 = const()[name = tensor("hidden_states_13_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205405568)))]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205408192)))]; + tensor hidden_states_13_cast = layer_norm(axes = hidden_states_13_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm1_weight_to_fp16, x = input_35_cast)[name = tensor("hidden_states_13_cast")]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(205410816)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208687680)))]; + tensor var_302_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_q_proj_weight_to_fp16, x = hidden_states_13_cast)[name = tensor("op_302_cast")]; + tensor var_303_to_fp16 = const()[name = tensor("op_303_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_17_cast = mul(x = var_302_cast, y = var_303_to_fp16)[name = tensor("tensor_17_cast")]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208690304)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211967168)))]; + tensor tensor_13_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_k_proj_weight_to_fp16, x = hidden_states_13_cast)[name = tensor("tensor_13_cast")]; + tensor var_308 = const()[name = tensor("op_308"), val = tensor([1, -1, 20, 64])]; + tensor var_309_cast = reshape(shape = var_308, x = tensor_13_cast)[name = tensor("op_309_cast")]; + tensor var_310_perm_0 = const()[name = tensor("op_310_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211969792)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215246656)))]; + tensor tensor_15_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_v_proj_weight_to_fp16, x = hidden_states_13_cast)[name = tensor("tensor_15_cast")]; + tensor var_315 = const()[name = tensor("op_315"), val = tensor([1, -1, 20, 64])]; + tensor var_316_cast = reshape(shape = var_315, x = tensor_15_cast)[name = tensor("op_316_cast")]; + tensor var_317_perm_0 = const()[name = tensor("op_317_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_324 = const()[name = tensor("op_324"), val = tensor([1, 77, 20, 64])]; + tensor var_325_cast = reshape(shape = var_324, x = tensor_17_cast)[name = tensor("op_325_cast")]; + tensor var_326_perm_0 = const()[name = tensor("op_326_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_328 = const()[name = tensor("op_328"), val = tensor([20, -1, 64])]; + tensor transpose_148 = transpose(perm = var_326_perm_0, x = var_325_cast)[name = tensor("transpose_148")]; + tensor query_states_5_cast = reshape(shape = var_328, x = transpose_148)[name = tensor("query_states_5_cast")]; + tensor var_330 = const()[name = tensor("op_330"), val = tensor([20, -1, 64])]; + tensor transpose_150 = transpose(perm = var_310_perm_0, x = var_309_cast)[name = tensor("transpose_150")]; + tensor key_states_11_cast = reshape(shape = var_330, x = transpose_150)[name = tensor("key_states_11_cast")]; + tensor var_332 = const()[name = tensor("op_332"), val = tensor([20, -1, 64])]; + tensor transpose_149 = transpose(perm = var_317_perm_0, x = var_316_cast)[name = tensor("transpose_149")]; + tensor value_states_11_cast = reshape(shape = var_332, x = transpose_149)[name = tensor("value_states_11_cast")]; + tensor var_335_perm_0 = const()[name = tensor("op_335_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor transpose_147 = transpose(perm = var_335_perm_0, x = key_states_11_cast)[name = tensor("transpose_147")]; + tensor attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = query_states_5_cast, y = transpose_147)[name = tensor("attn_weights_13_cast")]; + tensor var_337 = const()[name = tensor("op_337"), val = tensor([1, 20, 77, 77])]; + tensor var_338_cast = reshape(shape = var_337, x = attn_weights_13_cast)[name = tensor("op_338_cast")]; + tensor attn_weights_15_cast = add(x = var_338_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_15_cast")]; + tensor var_343 = const()[name = tensor("op_343"), val = tensor([20, 77, 77])]; + tensor input_37_cast = reshape(shape = var_343, x = attn_weights_15_cast)[name = tensor("input_37_cast")]; + tensor input_39_cast = softmax(axis = var_5, x = input_37_cast)[name = tensor("input_39_cast")]; + tensor attn_output_13_transpose_x_0 = const()[name = tensor("attn_output_13_transpose_x_0"), val = tensor(false)]; + tensor attn_output_13_transpose_y_0 = const()[name = tensor("attn_output_13_transpose_y_0"), val = tensor(false)]; + tensor attn_output_13_cast = matmul(transpose_x = attn_output_13_transpose_x_0, transpose_y = attn_output_13_transpose_y_0, x = input_39_cast, y = value_states_11_cast)[name = tensor("attn_output_13_cast")]; + tensor var_348 = const()[name = tensor("op_348"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_15_cast = reshape(shape = var_348, x = attn_output_13_cast)[name = tensor("attn_output_15_cast")]; + tensor attn_output_17_perm_0 = const()[name = tensor("attn_output_17_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_351 = const()[name = tensor("op_351"), val = tensor([1, 77, 1280])]; + tensor transpose_146 = transpose(perm = attn_output_17_perm_0, x = attn_output_15_cast)[name = tensor("transpose_146")]; + tensor input_41_cast = reshape(shape = var_351, x = transpose_146)[name = tensor("input_41_cast")]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215249280)))]; + tensor text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218526144)))]; + tensor hidden_states_15_cast = linear(bias = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_self_attn_out_proj_weight_to_fp16, x = input_41_cast)[name = tensor("hidden_states_15_cast")]; + tensor input_43_cast = add(x = input_35_cast, y = hidden_states_15_cast)[name = tensor("input_43_cast")]; + tensor input_45_axes_0 = const()[name = tensor("input_45_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218528768)))]; + tensor text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218531392)))]; + tensor input_45_cast = layer_norm(axes = input_45_axes_0, beta = text_encoder_text_model_encoder_layers_2_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_2_layer_norm2_weight_to_fp16, x = input_43_cast)[name = tensor("input_45_cast")]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218534016)))]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231641280)))]; + tensor input_47_cast = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_mlp_fc1_weight_to_fp16, x = input_45_cast)[name = tensor("input_47_cast")]; + tensor input_49_mode_0 = const()[name = tensor("input_49_mode_0"), val = tensor("EXACT")]; + tensor input_49_cast = gelu(mode = input_49_mode_0, x = input_47_cast)[name = tensor("input_49_cast")]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231651584)))]; + tensor text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244758848)))]; + tensor hidden_states_17_cast = linear(bias = text_encoder_text_model_encoder_layers_2_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_2_mlp_fc2_weight_to_fp16, x = input_49_cast)[name = tensor("hidden_states_17_cast")]; + tensor input_51_cast = add(x = input_43_cast, y = hidden_states_17_cast)[name = tensor("input_51_cast")]; + tensor hidden_states_19_axes_0 = const()[name = tensor("hidden_states_19_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244761472)))]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244764096)))]; + tensor hidden_states_19_cast = layer_norm(axes = hidden_states_19_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm1_weight_to_fp16, x = input_51_cast)[name = tensor("hidden_states_19_cast")]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(244766720)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248043584)))]; + tensor var_389_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_q_proj_weight_to_fp16, x = hidden_states_19_cast)[name = tensor("op_389_cast")]; + tensor var_390_to_fp16 = const()[name = tensor("op_390_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_23_cast = mul(x = var_389_cast, y = var_390_to_fp16)[name = tensor("tensor_23_cast")]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(248046208)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251323072)))]; + tensor tensor_19_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_k_proj_weight_to_fp16, x = hidden_states_19_cast)[name = tensor("tensor_19_cast")]; + tensor var_395 = const()[name = tensor("op_395"), val = tensor([1, -1, 20, 64])]; + tensor var_396_cast = reshape(shape = var_395, x = tensor_19_cast)[name = tensor("op_396_cast")]; + tensor var_397_perm_0 = const()[name = tensor("op_397_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(251325696)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254602560)))]; + tensor tensor_21_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_v_proj_weight_to_fp16, x = hidden_states_19_cast)[name = tensor("tensor_21_cast")]; + tensor var_402 = const()[name = tensor("op_402"), val = tensor([1, -1, 20, 64])]; + tensor var_403_cast = reshape(shape = var_402, x = tensor_21_cast)[name = tensor("op_403_cast")]; + tensor var_404_perm_0 = const()[name = tensor("op_404_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_411 = const()[name = tensor("op_411"), val = tensor([1, 77, 20, 64])]; + tensor var_412_cast = reshape(shape = var_411, x = tensor_23_cast)[name = tensor("op_412_cast")]; + tensor var_413_perm_0 = const()[name = tensor("op_413_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_415 = const()[name = tensor("op_415"), val = tensor([20, -1, 64])]; + tensor transpose_143 = transpose(perm = var_413_perm_0, x = var_412_cast)[name = tensor("transpose_143")]; + tensor query_states_7_cast = reshape(shape = var_415, x = transpose_143)[name = tensor("query_states_7_cast")]; + tensor var_417 = const()[name = tensor("op_417"), val = tensor([20, -1, 64])]; + tensor transpose_145 = transpose(perm = var_397_perm_0, x = var_396_cast)[name = tensor("transpose_145")]; + tensor key_states_15_cast = reshape(shape = var_417, x = transpose_145)[name = tensor("key_states_15_cast")]; + tensor var_419 = const()[name = tensor("op_419"), val = tensor([20, -1, 64])]; + tensor transpose_144 = transpose(perm = var_404_perm_0, x = var_403_cast)[name = tensor("transpose_144")]; + tensor value_states_15_cast = reshape(shape = var_419, x = transpose_144)[name = tensor("value_states_15_cast")]; + tensor var_422_perm_0 = const()[name = tensor("op_422_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_19_transpose_x_0 = const()[name = tensor("attn_weights_19_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_19_transpose_y_0 = const()[name = tensor("attn_weights_19_transpose_y_0"), val = tensor(false)]; + tensor transpose_142 = transpose(perm = var_422_perm_0, x = key_states_15_cast)[name = tensor("transpose_142")]; + tensor attn_weights_19_cast = matmul(transpose_x = attn_weights_19_transpose_x_0, transpose_y = attn_weights_19_transpose_y_0, x = query_states_7_cast, y = transpose_142)[name = tensor("attn_weights_19_cast")]; + tensor var_424 = const()[name = tensor("op_424"), val = tensor([1, 20, 77, 77])]; + tensor var_425_cast = reshape(shape = var_424, x = attn_weights_19_cast)[name = tensor("op_425_cast")]; + tensor attn_weights_21_cast = add(x = var_425_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_21_cast")]; + tensor var_430 = const()[name = tensor("op_430"), val = tensor([20, 77, 77])]; + tensor input_53_cast = reshape(shape = var_430, x = attn_weights_21_cast)[name = tensor("input_53_cast")]; + tensor input_55_cast = softmax(axis = var_5, x = input_53_cast)[name = tensor("input_55_cast")]; + tensor attn_output_19_transpose_x_0 = const()[name = tensor("attn_output_19_transpose_x_0"), val = tensor(false)]; + tensor attn_output_19_transpose_y_0 = const()[name = tensor("attn_output_19_transpose_y_0"), val = tensor(false)]; + tensor attn_output_19_cast = matmul(transpose_x = attn_output_19_transpose_x_0, transpose_y = attn_output_19_transpose_y_0, x = input_55_cast, y = value_states_15_cast)[name = tensor("attn_output_19_cast")]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_21_cast = reshape(shape = var_435, x = attn_output_19_cast)[name = tensor("attn_output_21_cast")]; + tensor attn_output_23_perm_0 = const()[name = tensor("attn_output_23_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_438 = const()[name = tensor("op_438"), val = tensor([1, 77, 1280])]; + tensor transpose_141 = transpose(perm = attn_output_23_perm_0, x = attn_output_21_cast)[name = tensor("transpose_141")]; + tensor input_57_cast = reshape(shape = var_438, x = transpose_141)[name = tensor("input_57_cast")]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254605184)))]; + tensor text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257882048)))]; + tensor hidden_states_21_cast = linear(bias = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_self_attn_out_proj_weight_to_fp16, x = input_57_cast)[name = tensor("hidden_states_21_cast")]; + tensor input_59_cast = add(x = input_51_cast, y = hidden_states_21_cast)[name = tensor("input_59_cast")]; + tensor input_61_axes_0 = const()[name = tensor("input_61_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257884672)))]; + tensor text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257887296)))]; + tensor input_61_cast = layer_norm(axes = input_61_axes_0, beta = text_encoder_text_model_encoder_layers_3_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_3_layer_norm2_weight_to_fp16, x = input_59_cast)[name = tensor("input_61_cast")]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257889920)))]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270997184)))]; + tensor input_63_cast = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_mlp_fc1_weight_to_fp16, x = input_61_cast)[name = tensor("input_63_cast")]; + tensor input_65_mode_0 = const()[name = tensor("input_65_mode_0"), val = tensor("EXACT")]; + tensor input_65_cast = gelu(mode = input_65_mode_0, x = input_63_cast)[name = tensor("input_65_cast")]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(271007488)))]; + tensor text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284114752)))]; + tensor hidden_states_23_cast = linear(bias = text_encoder_text_model_encoder_layers_3_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_3_mlp_fc2_weight_to_fp16, x = input_65_cast)[name = tensor("hidden_states_23_cast")]; + tensor input_67_cast = add(x = input_59_cast, y = hidden_states_23_cast)[name = tensor("input_67_cast")]; + tensor hidden_states_25_axes_0 = const()[name = tensor("hidden_states_25_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284117376)))]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284120000)))]; + tensor hidden_states_25_cast = layer_norm(axes = hidden_states_25_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm1_weight_to_fp16, x = input_67_cast)[name = tensor("hidden_states_25_cast")]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284122624)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287399488)))]; + tensor var_476_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_q_proj_weight_to_fp16, x = hidden_states_25_cast)[name = tensor("op_476_cast")]; + tensor var_477_to_fp16 = const()[name = tensor("op_477_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_29_cast = mul(x = var_476_cast, y = var_477_to_fp16)[name = tensor("tensor_29_cast")]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287402112)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290678976)))]; + tensor tensor_25_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_k_proj_weight_to_fp16, x = hidden_states_25_cast)[name = tensor("tensor_25_cast")]; + tensor var_482 = const()[name = tensor("op_482"), val = tensor([1, -1, 20, 64])]; + tensor var_483_cast = reshape(shape = var_482, x = tensor_25_cast)[name = tensor("op_483_cast")]; + tensor var_484_perm_0 = const()[name = tensor("op_484_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(290681600)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293958464)))]; + tensor tensor_27_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_v_proj_weight_to_fp16, x = hidden_states_25_cast)[name = tensor("tensor_27_cast")]; + tensor var_489 = const()[name = tensor("op_489"), val = tensor([1, -1, 20, 64])]; + tensor var_490_cast = reshape(shape = var_489, x = tensor_27_cast)[name = tensor("op_490_cast")]; + tensor var_491_perm_0 = const()[name = tensor("op_491_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_498 = const()[name = tensor("op_498"), val = tensor([1, 77, 20, 64])]; + tensor var_499_cast = reshape(shape = var_498, x = tensor_29_cast)[name = tensor("op_499_cast")]; + tensor var_500_perm_0 = const()[name = tensor("op_500_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_502 = const()[name = tensor("op_502"), val = tensor([20, -1, 64])]; + tensor transpose_138 = transpose(perm = var_500_perm_0, x = var_499_cast)[name = tensor("transpose_138")]; + tensor query_states_9_cast = reshape(shape = var_502, x = transpose_138)[name = tensor("query_states_9_cast")]; + tensor var_504 = const()[name = tensor("op_504"), val = tensor([20, -1, 64])]; + tensor transpose_140 = transpose(perm = var_484_perm_0, x = var_483_cast)[name = tensor("transpose_140")]; + tensor key_states_19_cast = reshape(shape = var_504, x = transpose_140)[name = tensor("key_states_19_cast")]; + tensor var_506 = const()[name = tensor("op_506"), val = tensor([20, -1, 64])]; + tensor transpose_139 = transpose(perm = var_491_perm_0, x = var_490_cast)[name = tensor("transpose_139")]; + tensor value_states_19_cast = reshape(shape = var_506, x = transpose_139)[name = tensor("value_states_19_cast")]; + tensor var_509_perm_0 = const()[name = tensor("op_509_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor transpose_137 = transpose(perm = var_509_perm_0, x = key_states_19_cast)[name = tensor("transpose_137")]; + tensor attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = query_states_9_cast, y = transpose_137)[name = tensor("attn_weights_25_cast")]; + tensor var_511 = const()[name = tensor("op_511"), val = tensor([1, 20, 77, 77])]; + tensor var_512_cast = reshape(shape = var_511, x = attn_weights_25_cast)[name = tensor("op_512_cast")]; + tensor attn_weights_27_cast = add(x = var_512_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_27_cast")]; + tensor var_517 = const()[name = tensor("op_517"), val = tensor([20, 77, 77])]; + tensor input_69_cast = reshape(shape = var_517, x = attn_weights_27_cast)[name = tensor("input_69_cast")]; + tensor input_71_cast = softmax(axis = var_5, x = input_69_cast)[name = tensor("input_71_cast")]; + tensor attn_output_25_transpose_x_0 = const()[name = tensor("attn_output_25_transpose_x_0"), val = tensor(false)]; + tensor attn_output_25_transpose_y_0 = const()[name = tensor("attn_output_25_transpose_y_0"), val = tensor(false)]; + tensor attn_output_25_cast = matmul(transpose_x = attn_output_25_transpose_x_0, transpose_y = attn_output_25_transpose_y_0, x = input_71_cast, y = value_states_19_cast)[name = tensor("attn_output_25_cast")]; + tensor var_522 = const()[name = tensor("op_522"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_27_cast = reshape(shape = var_522, x = attn_output_25_cast)[name = tensor("attn_output_27_cast")]; + tensor attn_output_29_perm_0 = const()[name = tensor("attn_output_29_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_525 = const()[name = tensor("op_525"), val = tensor([1, 77, 1280])]; + tensor transpose_136 = transpose(perm = attn_output_29_perm_0, x = attn_output_27_cast)[name = tensor("transpose_136")]; + tensor input_73_cast = reshape(shape = var_525, x = transpose_136)[name = tensor("input_73_cast")]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(293961088)))]; + tensor text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297237952)))]; + tensor hidden_states_27_cast = linear(bias = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_self_attn_out_proj_weight_to_fp16, x = input_73_cast)[name = tensor("hidden_states_27_cast")]; + tensor input_75_cast = add(x = input_67_cast, y = hidden_states_27_cast)[name = tensor("input_75_cast")]; + tensor input_77_axes_0 = const()[name = tensor("input_77_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297240576)))]; + tensor text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297243200)))]; + tensor input_77_cast = layer_norm(axes = input_77_axes_0, beta = text_encoder_text_model_encoder_layers_4_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_4_layer_norm2_weight_to_fp16, x = input_75_cast)[name = tensor("input_77_cast")]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297245824)))]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310353088)))]; + tensor input_79_cast = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_mlp_fc1_weight_to_fp16, x = input_77_cast)[name = tensor("input_79_cast")]; + tensor input_81_mode_0 = const()[name = tensor("input_81_mode_0"), val = tensor("EXACT")]; + tensor input_81_cast = gelu(mode = input_81_mode_0, x = input_79_cast)[name = tensor("input_81_cast")]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310363392)))]; + tensor text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323470656)))]; + tensor hidden_states_29_cast = linear(bias = text_encoder_text_model_encoder_layers_4_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_4_mlp_fc2_weight_to_fp16, x = input_81_cast)[name = tensor("hidden_states_29_cast")]; + tensor input_83_cast = add(x = input_75_cast, y = hidden_states_29_cast)[name = tensor("input_83_cast")]; + tensor hidden_states_31_axes_0 = const()[name = tensor("hidden_states_31_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323473280)))]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323475904)))]; + tensor hidden_states_31_cast = layer_norm(axes = hidden_states_31_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm1_weight_to_fp16, x = input_83_cast)[name = tensor("hidden_states_31_cast")]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(323478528)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(326755392)))]; + tensor var_563_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_q_proj_weight_to_fp16, x = hidden_states_31_cast)[name = tensor("op_563_cast")]; + tensor var_564_to_fp16 = const()[name = tensor("op_564_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_35_cast = mul(x = var_563_cast, y = var_564_to_fp16)[name = tensor("tensor_35_cast")]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(326758016)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330034880)))]; + tensor tensor_31_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_k_proj_weight_to_fp16, x = hidden_states_31_cast)[name = tensor("tensor_31_cast")]; + tensor var_569 = const()[name = tensor("op_569"), val = tensor([1, -1, 20, 64])]; + tensor var_570_cast = reshape(shape = var_569, x = tensor_31_cast)[name = tensor("op_570_cast")]; + tensor var_571_perm_0 = const()[name = tensor("op_571_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(330037504)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333314368)))]; + tensor tensor_33_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_v_proj_weight_to_fp16, x = hidden_states_31_cast)[name = tensor("tensor_33_cast")]; + tensor var_576 = const()[name = tensor("op_576"), val = tensor([1, -1, 20, 64])]; + tensor var_577_cast = reshape(shape = var_576, x = tensor_33_cast)[name = tensor("op_577_cast")]; + tensor var_578_perm_0 = const()[name = tensor("op_578_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_585 = const()[name = tensor("op_585"), val = tensor([1, 77, 20, 64])]; + tensor var_586_cast = reshape(shape = var_585, x = tensor_35_cast)[name = tensor("op_586_cast")]; + tensor var_587_perm_0 = const()[name = tensor("op_587_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_589 = const()[name = tensor("op_589"), val = tensor([20, -1, 64])]; + tensor transpose_133 = transpose(perm = var_587_perm_0, x = var_586_cast)[name = tensor("transpose_133")]; + tensor query_states_11_cast = reshape(shape = var_589, x = transpose_133)[name = tensor("query_states_11_cast")]; + tensor var_591 = const()[name = tensor("op_591"), val = tensor([20, -1, 64])]; + tensor transpose_135 = transpose(perm = var_571_perm_0, x = var_570_cast)[name = tensor("transpose_135")]; + tensor key_states_23_cast = reshape(shape = var_591, x = transpose_135)[name = tensor("key_states_23_cast")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([20, -1, 64])]; + tensor transpose_134 = transpose(perm = var_578_perm_0, x = var_577_cast)[name = tensor("transpose_134")]; + tensor value_states_23_cast = reshape(shape = var_593, x = transpose_134)[name = tensor("value_states_23_cast")]; + tensor var_596_perm_0 = const()[name = tensor("op_596_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_31_transpose_x_0 = const()[name = tensor("attn_weights_31_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_31_transpose_y_0 = const()[name = tensor("attn_weights_31_transpose_y_0"), val = tensor(false)]; + tensor transpose_132 = transpose(perm = var_596_perm_0, x = key_states_23_cast)[name = tensor("transpose_132")]; + tensor attn_weights_31_cast = matmul(transpose_x = attn_weights_31_transpose_x_0, transpose_y = attn_weights_31_transpose_y_0, x = query_states_11_cast, y = transpose_132)[name = tensor("attn_weights_31_cast")]; + tensor var_598 = const()[name = tensor("op_598"), val = tensor([1, 20, 77, 77])]; + tensor var_599_cast = reshape(shape = var_598, x = attn_weights_31_cast)[name = tensor("op_599_cast")]; + tensor attn_weights_33_cast = add(x = var_599_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_33_cast")]; + tensor var_604 = const()[name = tensor("op_604"), val = tensor([20, 77, 77])]; + tensor input_85_cast = reshape(shape = var_604, x = attn_weights_33_cast)[name = tensor("input_85_cast")]; + tensor input_87_cast = softmax(axis = var_5, x = input_85_cast)[name = tensor("input_87_cast")]; + tensor attn_output_31_transpose_x_0 = const()[name = tensor("attn_output_31_transpose_x_0"), val = tensor(false)]; + tensor attn_output_31_transpose_y_0 = const()[name = tensor("attn_output_31_transpose_y_0"), val = tensor(false)]; + tensor attn_output_31_cast = matmul(transpose_x = attn_output_31_transpose_x_0, transpose_y = attn_output_31_transpose_y_0, x = input_87_cast, y = value_states_23_cast)[name = tensor("attn_output_31_cast")]; + tensor var_609 = const()[name = tensor("op_609"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_33_cast = reshape(shape = var_609, x = attn_output_31_cast)[name = tensor("attn_output_33_cast")]; + tensor attn_output_35_perm_0 = const()[name = tensor("attn_output_35_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_612 = const()[name = tensor("op_612"), val = tensor([1, 77, 1280])]; + tensor transpose_131 = transpose(perm = attn_output_35_perm_0, x = attn_output_33_cast)[name = tensor("transpose_131")]; + tensor input_89_cast = reshape(shape = var_612, x = transpose_131)[name = tensor("input_89_cast")]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333316992)))]; + tensor text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336593856)))]; + tensor hidden_states_33_cast = linear(bias = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_self_attn_out_proj_weight_to_fp16, x = input_89_cast)[name = tensor("hidden_states_33_cast")]; + tensor input_91_cast = add(x = input_83_cast, y = hidden_states_33_cast)[name = tensor("input_91_cast")]; + tensor input_93_axes_0 = const()[name = tensor("input_93_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336596480)))]; + tensor text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336599104)))]; + tensor input_93_cast = layer_norm(axes = input_93_axes_0, beta = text_encoder_text_model_encoder_layers_5_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_5_layer_norm2_weight_to_fp16, x = input_91_cast)[name = tensor("input_93_cast")]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(336601728)))]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(349708992)))]; + tensor input_95_cast = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_mlp_fc1_weight_to_fp16, x = input_93_cast)[name = tensor("input_95_cast")]; + tensor input_97_mode_0 = const()[name = tensor("input_97_mode_0"), val = tensor("EXACT")]; + tensor input_97_cast = gelu(mode = input_97_mode_0, x = input_95_cast)[name = tensor("input_97_cast")]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(349719296)))]; + tensor text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362826560)))]; + tensor hidden_states_35_cast = linear(bias = text_encoder_text_model_encoder_layers_5_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_5_mlp_fc2_weight_to_fp16, x = input_97_cast)[name = tensor("hidden_states_35_cast")]; + tensor input_99_cast = add(x = input_91_cast, y = hidden_states_35_cast)[name = tensor("input_99_cast")]; + tensor hidden_states_37_axes_0 = const()[name = tensor("hidden_states_37_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362829184)))]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362831808)))]; + tensor hidden_states_37_cast = layer_norm(axes = hidden_states_37_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm1_weight_to_fp16, x = input_99_cast)[name = tensor("hidden_states_37_cast")]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362834432)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366111296)))]; + tensor var_650_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_q_proj_weight_to_fp16, x = hidden_states_37_cast)[name = tensor("op_650_cast")]; + tensor var_651_to_fp16 = const()[name = tensor("op_651_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_41_cast = mul(x = var_650_cast, y = var_651_to_fp16)[name = tensor("tensor_41_cast")]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(366113920)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369390784)))]; + tensor tensor_37_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_k_proj_weight_to_fp16, x = hidden_states_37_cast)[name = tensor("tensor_37_cast")]; + tensor var_656 = const()[name = tensor("op_656"), val = tensor([1, -1, 20, 64])]; + tensor var_657_cast = reshape(shape = var_656, x = tensor_37_cast)[name = tensor("op_657_cast")]; + tensor var_658_perm_0 = const()[name = tensor("op_658_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369393408)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372670272)))]; + tensor tensor_39_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_v_proj_weight_to_fp16, x = hidden_states_37_cast)[name = tensor("tensor_39_cast")]; + tensor var_663 = const()[name = tensor("op_663"), val = tensor([1, -1, 20, 64])]; + tensor var_664_cast = reshape(shape = var_663, x = tensor_39_cast)[name = tensor("op_664_cast")]; + tensor var_665_perm_0 = const()[name = tensor("op_665_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 77, 20, 64])]; + tensor var_673_cast = reshape(shape = var_672, x = tensor_41_cast)[name = tensor("op_673_cast")]; + tensor var_674_perm_0 = const()[name = tensor("op_674_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_676 = const()[name = tensor("op_676"), val = tensor([20, -1, 64])]; + tensor transpose_128 = transpose(perm = var_674_perm_0, x = var_673_cast)[name = tensor("transpose_128")]; + tensor query_states_13_cast = reshape(shape = var_676, x = transpose_128)[name = tensor("query_states_13_cast")]; + tensor var_678 = const()[name = tensor("op_678"), val = tensor([20, -1, 64])]; + tensor transpose_130 = transpose(perm = var_658_perm_0, x = var_657_cast)[name = tensor("transpose_130")]; + tensor key_states_27_cast = reshape(shape = var_678, x = transpose_130)[name = tensor("key_states_27_cast")]; + tensor var_680 = const()[name = tensor("op_680"), val = tensor([20, -1, 64])]; + tensor transpose_129 = transpose(perm = var_665_perm_0, x = var_664_cast)[name = tensor("transpose_129")]; + tensor value_states_27_cast = reshape(shape = var_680, x = transpose_129)[name = tensor("value_states_27_cast")]; + tensor var_683_perm_0 = const()[name = tensor("op_683_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor transpose_127 = transpose(perm = var_683_perm_0, x = key_states_27_cast)[name = tensor("transpose_127")]; + tensor attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = query_states_13_cast, y = transpose_127)[name = tensor("attn_weights_37_cast")]; + tensor var_685 = const()[name = tensor("op_685"), val = tensor([1, 20, 77, 77])]; + tensor var_686_cast = reshape(shape = var_685, x = attn_weights_37_cast)[name = tensor("op_686_cast")]; + tensor attn_weights_39_cast = add(x = var_686_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_39_cast")]; + tensor var_691 = const()[name = tensor("op_691"), val = tensor([20, 77, 77])]; + tensor input_101_cast = reshape(shape = var_691, x = attn_weights_39_cast)[name = tensor("input_101_cast")]; + tensor input_103_cast = softmax(axis = var_5, x = input_101_cast)[name = tensor("input_103_cast")]; + tensor attn_output_37_transpose_x_0 = const()[name = tensor("attn_output_37_transpose_x_0"), val = tensor(false)]; + tensor attn_output_37_transpose_y_0 = const()[name = tensor("attn_output_37_transpose_y_0"), val = tensor(false)]; + tensor attn_output_37_cast = matmul(transpose_x = attn_output_37_transpose_x_0, transpose_y = attn_output_37_transpose_y_0, x = input_103_cast, y = value_states_27_cast)[name = tensor("attn_output_37_cast")]; + tensor var_696 = const()[name = tensor("op_696"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_39_cast = reshape(shape = var_696, x = attn_output_37_cast)[name = tensor("attn_output_39_cast")]; + tensor attn_output_41_perm_0 = const()[name = tensor("attn_output_41_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_699 = const()[name = tensor("op_699"), val = tensor([1, 77, 1280])]; + tensor transpose_126 = transpose(perm = attn_output_41_perm_0, x = attn_output_39_cast)[name = tensor("transpose_126")]; + tensor input_105_cast = reshape(shape = var_699, x = transpose_126)[name = tensor("input_105_cast")]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372672896)))]; + tensor text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(375949760)))]; + tensor hidden_states_39_cast = linear(bias = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_self_attn_out_proj_weight_to_fp16, x = input_105_cast)[name = tensor("hidden_states_39_cast")]; + tensor input_107_cast = add(x = input_99_cast, y = hidden_states_39_cast)[name = tensor("input_107_cast")]; + tensor input_109_axes_0 = const()[name = tensor("input_109_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(375952384)))]; + tensor text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(375955008)))]; + tensor input_109_cast = layer_norm(axes = input_109_axes_0, beta = text_encoder_text_model_encoder_layers_6_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_6_layer_norm2_weight_to_fp16, x = input_107_cast)[name = tensor("input_109_cast")]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(375957632)))]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389064896)))]; + tensor input_111_cast = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_mlp_fc1_weight_to_fp16, x = input_109_cast)[name = tensor("input_111_cast")]; + tensor input_113_mode_0 = const()[name = tensor("input_113_mode_0"), val = tensor("EXACT")]; + tensor input_113_cast = gelu(mode = input_113_mode_0, x = input_111_cast)[name = tensor("input_113_cast")]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(389075200)))]; + tensor text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402182464)))]; + tensor hidden_states_41_cast = linear(bias = text_encoder_text_model_encoder_layers_6_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_6_mlp_fc2_weight_to_fp16, x = input_113_cast)[name = tensor("hidden_states_41_cast")]; + tensor input_115_cast = add(x = input_107_cast, y = hidden_states_41_cast)[name = tensor("input_115_cast")]; + tensor hidden_states_43_axes_0 = const()[name = tensor("hidden_states_43_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402185088)))]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402187712)))]; + tensor hidden_states_43_cast = layer_norm(axes = hidden_states_43_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm1_weight_to_fp16, x = input_115_cast)[name = tensor("hidden_states_43_cast")]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(402190336)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405467200)))]; + tensor var_737_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_q_proj_weight_to_fp16, x = hidden_states_43_cast)[name = tensor("op_737_cast")]; + tensor var_738_to_fp16 = const()[name = tensor("op_738_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_47_cast = mul(x = var_737_cast, y = var_738_to_fp16)[name = tensor("tensor_47_cast")]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405469824)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408746688)))]; + tensor tensor_43_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_k_proj_weight_to_fp16, x = hidden_states_43_cast)[name = tensor("tensor_43_cast")]; + tensor var_743 = const()[name = tensor("op_743"), val = tensor([1, -1, 20, 64])]; + tensor var_744_cast = reshape(shape = var_743, x = tensor_43_cast)[name = tensor("op_744_cast")]; + tensor var_745_perm_0 = const()[name = tensor("op_745_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408749312)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412026176)))]; + tensor tensor_45_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_v_proj_weight_to_fp16, x = hidden_states_43_cast)[name = tensor("tensor_45_cast")]; + tensor var_750 = const()[name = tensor("op_750"), val = tensor([1, -1, 20, 64])]; + tensor var_751_cast = reshape(shape = var_750, x = tensor_45_cast)[name = tensor("op_751_cast")]; + tensor var_752_perm_0 = const()[name = tensor("op_752_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_759 = const()[name = tensor("op_759"), val = tensor([1, 77, 20, 64])]; + tensor var_760_cast = reshape(shape = var_759, x = tensor_47_cast)[name = tensor("op_760_cast")]; + tensor var_761_perm_0 = const()[name = tensor("op_761_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_763 = const()[name = tensor("op_763"), val = tensor([20, -1, 64])]; + tensor transpose_123 = transpose(perm = var_761_perm_0, x = var_760_cast)[name = tensor("transpose_123")]; + tensor query_states_15_cast = reshape(shape = var_763, x = transpose_123)[name = tensor("query_states_15_cast")]; + tensor var_765 = const()[name = tensor("op_765"), val = tensor([20, -1, 64])]; + tensor transpose_125 = transpose(perm = var_745_perm_0, x = var_744_cast)[name = tensor("transpose_125")]; + tensor key_states_31_cast = reshape(shape = var_765, x = transpose_125)[name = tensor("key_states_31_cast")]; + tensor var_767 = const()[name = tensor("op_767"), val = tensor([20, -1, 64])]; + tensor transpose_124 = transpose(perm = var_752_perm_0, x = var_751_cast)[name = tensor("transpose_124")]; + tensor value_states_31_cast = reshape(shape = var_767, x = transpose_124)[name = tensor("value_states_31_cast")]; + tensor var_770_perm_0 = const()[name = tensor("op_770_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_43_transpose_x_0 = const()[name = tensor("attn_weights_43_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_43_transpose_y_0 = const()[name = tensor("attn_weights_43_transpose_y_0"), val = tensor(false)]; + tensor transpose_122 = transpose(perm = var_770_perm_0, x = key_states_31_cast)[name = tensor("transpose_122")]; + tensor attn_weights_43_cast = matmul(transpose_x = attn_weights_43_transpose_x_0, transpose_y = attn_weights_43_transpose_y_0, x = query_states_15_cast, y = transpose_122)[name = tensor("attn_weights_43_cast")]; + tensor var_772 = const()[name = tensor("op_772"), val = tensor([1, 20, 77, 77])]; + tensor var_773_cast = reshape(shape = var_772, x = attn_weights_43_cast)[name = tensor("op_773_cast")]; + tensor attn_weights_45_cast = add(x = var_773_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_45_cast")]; + tensor var_778 = const()[name = tensor("op_778"), val = tensor([20, 77, 77])]; + tensor input_117_cast = reshape(shape = var_778, x = attn_weights_45_cast)[name = tensor("input_117_cast")]; + tensor input_119_cast = softmax(axis = var_5, x = input_117_cast)[name = tensor("input_119_cast")]; + tensor attn_output_43_transpose_x_0 = const()[name = tensor("attn_output_43_transpose_x_0"), val = tensor(false)]; + tensor attn_output_43_transpose_y_0 = const()[name = tensor("attn_output_43_transpose_y_0"), val = tensor(false)]; + tensor attn_output_43_cast = matmul(transpose_x = attn_output_43_transpose_x_0, transpose_y = attn_output_43_transpose_y_0, x = input_119_cast, y = value_states_31_cast)[name = tensor("attn_output_43_cast")]; + tensor var_783 = const()[name = tensor("op_783"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_45_cast = reshape(shape = var_783, x = attn_output_43_cast)[name = tensor("attn_output_45_cast")]; + tensor attn_output_47_perm_0 = const()[name = tensor("attn_output_47_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_786 = const()[name = tensor("op_786"), val = tensor([1, 77, 1280])]; + tensor transpose_121 = transpose(perm = attn_output_47_perm_0, x = attn_output_45_cast)[name = tensor("transpose_121")]; + tensor input_121_cast = reshape(shape = var_786, x = transpose_121)[name = tensor("input_121_cast")]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(412028800)))]; + tensor text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415305664)))]; + tensor hidden_states_45_cast = linear(bias = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_self_attn_out_proj_weight_to_fp16, x = input_121_cast)[name = tensor("hidden_states_45_cast")]; + tensor input_123_cast = add(x = input_115_cast, y = hidden_states_45_cast)[name = tensor("input_123_cast")]; + tensor input_125_axes_0 = const()[name = tensor("input_125_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415308288)))]; + tensor text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415310912)))]; + tensor input_125_cast = layer_norm(axes = input_125_axes_0, beta = text_encoder_text_model_encoder_layers_7_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_7_layer_norm2_weight_to_fp16, x = input_123_cast)[name = tensor("input_125_cast")]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415313536)))]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428420800)))]; + tensor input_127_cast = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_mlp_fc1_weight_to_fp16, x = input_125_cast)[name = tensor("input_127_cast")]; + tensor input_129_mode_0 = const()[name = tensor("input_129_mode_0"), val = tensor("EXACT")]; + tensor input_129_cast = gelu(mode = input_129_mode_0, x = input_127_cast)[name = tensor("input_129_cast")]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428431104)))]; + tensor text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(441538368)))]; + tensor hidden_states_47_cast = linear(bias = text_encoder_text_model_encoder_layers_7_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_7_mlp_fc2_weight_to_fp16, x = input_129_cast)[name = tensor("hidden_states_47_cast")]; + tensor input_131_cast = add(x = input_123_cast, y = hidden_states_47_cast)[name = tensor("input_131_cast")]; + tensor hidden_states_49_axes_0 = const()[name = tensor("hidden_states_49_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(441540992)))]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(441543616)))]; + tensor hidden_states_49_cast = layer_norm(axes = hidden_states_49_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm1_weight_to_fp16, x = input_131_cast)[name = tensor("hidden_states_49_cast")]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(441546240)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444823104)))]; + tensor var_824_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_q_proj_weight_to_fp16, x = hidden_states_49_cast)[name = tensor("op_824_cast")]; + tensor var_825_to_fp16 = const()[name = tensor("op_825_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_53_cast = mul(x = var_824_cast, y = var_825_to_fp16)[name = tensor("tensor_53_cast")]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(444825728)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448102592)))]; + tensor tensor_49_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_k_proj_weight_to_fp16, x = hidden_states_49_cast)[name = tensor("tensor_49_cast")]; + tensor var_830 = const()[name = tensor("op_830"), val = tensor([1, -1, 20, 64])]; + tensor var_831_cast = reshape(shape = var_830, x = tensor_49_cast)[name = tensor("op_831_cast")]; + tensor var_832_perm_0 = const()[name = tensor("op_832_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448105216)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(451382080)))]; + tensor tensor_51_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_v_proj_weight_to_fp16, x = hidden_states_49_cast)[name = tensor("tensor_51_cast")]; + tensor var_837 = const()[name = tensor("op_837"), val = tensor([1, -1, 20, 64])]; + tensor var_838_cast = reshape(shape = var_837, x = tensor_51_cast)[name = tensor("op_838_cast")]; + tensor var_839_perm_0 = const()[name = tensor("op_839_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_846 = const()[name = tensor("op_846"), val = tensor([1, 77, 20, 64])]; + tensor var_847_cast = reshape(shape = var_846, x = tensor_53_cast)[name = tensor("op_847_cast")]; + tensor var_848_perm_0 = const()[name = tensor("op_848_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_850 = const()[name = tensor("op_850"), val = tensor([20, -1, 64])]; + tensor transpose_118 = transpose(perm = var_848_perm_0, x = var_847_cast)[name = tensor("transpose_118")]; + tensor query_states_17_cast = reshape(shape = var_850, x = transpose_118)[name = tensor("query_states_17_cast")]; + tensor var_852 = const()[name = tensor("op_852"), val = tensor([20, -1, 64])]; + tensor transpose_120 = transpose(perm = var_832_perm_0, x = var_831_cast)[name = tensor("transpose_120")]; + tensor key_states_35_cast = reshape(shape = var_852, x = transpose_120)[name = tensor("key_states_35_cast")]; + tensor var_854 = const()[name = tensor("op_854"), val = tensor([20, -1, 64])]; + tensor transpose_119 = transpose(perm = var_839_perm_0, x = var_838_cast)[name = tensor("transpose_119")]; + tensor value_states_35_cast = reshape(shape = var_854, x = transpose_119)[name = tensor("value_states_35_cast")]; + tensor var_857_perm_0 = const()[name = tensor("op_857_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor transpose_117 = transpose(perm = var_857_perm_0, x = key_states_35_cast)[name = tensor("transpose_117")]; + tensor attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = query_states_17_cast, y = transpose_117)[name = tensor("attn_weights_49_cast")]; + tensor var_859 = const()[name = tensor("op_859"), val = tensor([1, 20, 77, 77])]; + tensor var_860_cast = reshape(shape = var_859, x = attn_weights_49_cast)[name = tensor("op_860_cast")]; + tensor attn_weights_51_cast = add(x = var_860_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_51_cast")]; + tensor var_865 = const()[name = tensor("op_865"), val = tensor([20, 77, 77])]; + tensor input_133_cast = reshape(shape = var_865, x = attn_weights_51_cast)[name = tensor("input_133_cast")]; + tensor input_135_cast = softmax(axis = var_5, x = input_133_cast)[name = tensor("input_135_cast")]; + tensor attn_output_49_transpose_x_0 = const()[name = tensor("attn_output_49_transpose_x_0"), val = tensor(false)]; + tensor attn_output_49_transpose_y_0 = const()[name = tensor("attn_output_49_transpose_y_0"), val = tensor(false)]; + tensor attn_output_49_cast = matmul(transpose_x = attn_output_49_transpose_x_0, transpose_y = attn_output_49_transpose_y_0, x = input_135_cast, y = value_states_35_cast)[name = tensor("attn_output_49_cast")]; + tensor var_870 = const()[name = tensor("op_870"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_51_cast = reshape(shape = var_870, x = attn_output_49_cast)[name = tensor("attn_output_51_cast")]; + tensor attn_output_53_perm_0 = const()[name = tensor("attn_output_53_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_873 = const()[name = tensor("op_873"), val = tensor([1, 77, 1280])]; + tensor transpose_116 = transpose(perm = attn_output_53_perm_0, x = attn_output_51_cast)[name = tensor("transpose_116")]; + tensor input_137_cast = reshape(shape = var_873, x = transpose_116)[name = tensor("input_137_cast")]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(451384704)))]; + tensor text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454661568)))]; + tensor hidden_states_51_cast = linear(bias = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_self_attn_out_proj_weight_to_fp16, x = input_137_cast)[name = tensor("hidden_states_51_cast")]; + tensor input_139_cast = add(x = input_131_cast, y = hidden_states_51_cast)[name = tensor("input_139_cast")]; + tensor input_141_axes_0 = const()[name = tensor("input_141_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454664192)))]; + tensor text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454666816)))]; + tensor input_141_cast = layer_norm(axes = input_141_axes_0, beta = text_encoder_text_model_encoder_layers_8_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_8_layer_norm2_weight_to_fp16, x = input_139_cast)[name = tensor("input_141_cast")]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(454669440)))]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(467776704)))]; + tensor input_143_cast = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_mlp_fc1_weight_to_fp16, x = input_141_cast)[name = tensor("input_143_cast")]; + tensor input_145_mode_0 = const()[name = tensor("input_145_mode_0"), val = tensor("EXACT")]; + tensor input_145_cast = gelu(mode = input_145_mode_0, x = input_143_cast)[name = tensor("input_145_cast")]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(467787008)))]; + tensor text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480894272)))]; + tensor hidden_states_53_cast = linear(bias = text_encoder_text_model_encoder_layers_8_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_8_mlp_fc2_weight_to_fp16, x = input_145_cast)[name = tensor("hidden_states_53_cast")]; + tensor input_147_cast = add(x = input_139_cast, y = hidden_states_53_cast)[name = tensor("input_147_cast")]; + tensor hidden_states_55_axes_0 = const()[name = tensor("hidden_states_55_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480896896)))]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480899520)))]; + tensor hidden_states_55_cast = layer_norm(axes = hidden_states_55_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm1_weight_to_fp16, x = input_147_cast)[name = tensor("hidden_states_55_cast")]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480902144)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484179008)))]; + tensor var_911_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_q_proj_weight_to_fp16, x = hidden_states_55_cast)[name = tensor("op_911_cast")]; + tensor var_912_to_fp16 = const()[name = tensor("op_912_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_59_cast = mul(x = var_911_cast, y = var_912_to_fp16)[name = tensor("tensor_59_cast")]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484181632)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(487458496)))]; + tensor tensor_55_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_k_proj_weight_to_fp16, x = hidden_states_55_cast)[name = tensor("tensor_55_cast")]; + tensor var_917 = const()[name = tensor("op_917"), val = tensor([1, -1, 20, 64])]; + tensor var_918_cast = reshape(shape = var_917, x = tensor_55_cast)[name = tensor("op_918_cast")]; + tensor var_919_perm_0 = const()[name = tensor("op_919_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(487461120)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(490737984)))]; + tensor tensor_57_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_v_proj_weight_to_fp16, x = hidden_states_55_cast)[name = tensor("tensor_57_cast")]; + tensor var_924 = const()[name = tensor("op_924"), val = tensor([1, -1, 20, 64])]; + tensor var_925_cast = reshape(shape = var_924, x = tensor_57_cast)[name = tensor("op_925_cast")]; + tensor var_926_perm_0 = const()[name = tensor("op_926_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_933 = const()[name = tensor("op_933"), val = tensor([1, 77, 20, 64])]; + tensor var_934_cast = reshape(shape = var_933, x = tensor_59_cast)[name = tensor("op_934_cast")]; + tensor var_935_perm_0 = const()[name = tensor("op_935_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_937 = const()[name = tensor("op_937"), val = tensor([20, -1, 64])]; + tensor transpose_113 = transpose(perm = var_935_perm_0, x = var_934_cast)[name = tensor("transpose_113")]; + tensor query_states_19_cast = reshape(shape = var_937, x = transpose_113)[name = tensor("query_states_19_cast")]; + tensor var_939 = const()[name = tensor("op_939"), val = tensor([20, -1, 64])]; + tensor transpose_115 = transpose(perm = var_919_perm_0, x = var_918_cast)[name = tensor("transpose_115")]; + tensor key_states_39_cast = reshape(shape = var_939, x = transpose_115)[name = tensor("key_states_39_cast")]; + tensor var_941 = const()[name = tensor("op_941"), val = tensor([20, -1, 64])]; + tensor transpose_114 = transpose(perm = var_926_perm_0, x = var_925_cast)[name = tensor("transpose_114")]; + tensor value_states_39_cast = reshape(shape = var_941, x = transpose_114)[name = tensor("value_states_39_cast")]; + tensor var_944_perm_0 = const()[name = tensor("op_944_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_55_transpose_x_0 = const()[name = tensor("attn_weights_55_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_55_transpose_y_0 = const()[name = tensor("attn_weights_55_transpose_y_0"), val = tensor(false)]; + tensor transpose_112 = transpose(perm = var_944_perm_0, x = key_states_39_cast)[name = tensor("transpose_112")]; + tensor attn_weights_55_cast = matmul(transpose_x = attn_weights_55_transpose_x_0, transpose_y = attn_weights_55_transpose_y_0, x = query_states_19_cast, y = transpose_112)[name = tensor("attn_weights_55_cast")]; + tensor var_946 = const()[name = tensor("op_946"), val = tensor([1, 20, 77, 77])]; + tensor var_947_cast = reshape(shape = var_946, x = attn_weights_55_cast)[name = tensor("op_947_cast")]; + tensor attn_weights_57_cast = add(x = var_947_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_57_cast")]; + tensor var_952 = const()[name = tensor("op_952"), val = tensor([20, 77, 77])]; + tensor input_149_cast = reshape(shape = var_952, x = attn_weights_57_cast)[name = tensor("input_149_cast")]; + tensor input_151_cast = softmax(axis = var_5, x = input_149_cast)[name = tensor("input_151_cast")]; + tensor attn_output_55_transpose_x_0 = const()[name = tensor("attn_output_55_transpose_x_0"), val = tensor(false)]; + tensor attn_output_55_transpose_y_0 = const()[name = tensor("attn_output_55_transpose_y_0"), val = tensor(false)]; + tensor attn_output_55_cast = matmul(transpose_x = attn_output_55_transpose_x_0, transpose_y = attn_output_55_transpose_y_0, x = input_151_cast, y = value_states_39_cast)[name = tensor("attn_output_55_cast")]; + tensor var_957 = const()[name = tensor("op_957"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_57_cast = reshape(shape = var_957, x = attn_output_55_cast)[name = tensor("attn_output_57_cast")]; + tensor attn_output_59_perm_0 = const()[name = tensor("attn_output_59_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_960 = const()[name = tensor("op_960"), val = tensor([1, 77, 1280])]; + tensor transpose_111 = transpose(perm = attn_output_59_perm_0, x = attn_output_57_cast)[name = tensor("transpose_111")]; + tensor input_153_cast = reshape(shape = var_960, x = transpose_111)[name = tensor("input_153_cast")]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(490740608)))]; + tensor text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(494017472)))]; + tensor hidden_states_57_cast = linear(bias = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_self_attn_out_proj_weight_to_fp16, x = input_153_cast)[name = tensor("hidden_states_57_cast")]; + tensor input_155_cast = add(x = input_147_cast, y = hidden_states_57_cast)[name = tensor("input_155_cast")]; + tensor input_157_axes_0 = const()[name = tensor("input_157_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(494020096)))]; + tensor text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(494022720)))]; + tensor input_157_cast = layer_norm(axes = input_157_axes_0, beta = text_encoder_text_model_encoder_layers_9_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_9_layer_norm2_weight_to_fp16, x = input_155_cast)[name = tensor("input_157_cast")]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(494025344)))]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(507132608)))]; + tensor input_159_cast = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_mlp_fc1_weight_to_fp16, x = input_157_cast)[name = tensor("input_159_cast")]; + tensor input_161_mode_0 = const()[name = tensor("input_161_mode_0"), val = tensor("EXACT")]; + tensor input_161_cast = gelu(mode = input_161_mode_0, x = input_159_cast)[name = tensor("input_161_cast")]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(507142912)))]; + tensor text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520250176)))]; + tensor hidden_states_59_cast = linear(bias = text_encoder_text_model_encoder_layers_9_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_9_mlp_fc2_weight_to_fp16, x = input_161_cast)[name = tensor("hidden_states_59_cast")]; + tensor input_163_cast = add(x = input_155_cast, y = hidden_states_59_cast)[name = tensor("input_163_cast")]; + tensor hidden_states_61_axes_0 = const()[name = tensor("hidden_states_61_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520252800)))]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520255424)))]; + tensor hidden_states_61_cast = layer_norm(axes = hidden_states_61_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm1_weight_to_fp16, x = input_163_cast)[name = tensor("hidden_states_61_cast")]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520258048)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(523534912)))]; + tensor var_998_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_q_proj_weight_to_fp16, x = hidden_states_61_cast)[name = tensor("op_998_cast")]; + tensor var_999_to_fp16 = const()[name = tensor("op_999_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_65_cast = mul(x = var_998_cast, y = var_999_to_fp16)[name = tensor("tensor_65_cast")]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(523537536)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526814400)))]; + tensor tensor_61_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_k_proj_weight_to_fp16, x = hidden_states_61_cast)[name = tensor("tensor_61_cast")]; + tensor var_1004 = const()[name = tensor("op_1004"), val = tensor([1, -1, 20, 64])]; + tensor var_1005_cast = reshape(shape = var_1004, x = tensor_61_cast)[name = tensor("op_1005_cast")]; + tensor var_1006_perm_0 = const()[name = tensor("op_1006_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526817024)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(530093888)))]; + tensor tensor_63_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_v_proj_weight_to_fp16, x = hidden_states_61_cast)[name = tensor("tensor_63_cast")]; + tensor var_1011 = const()[name = tensor("op_1011"), val = tensor([1, -1, 20, 64])]; + tensor var_1012_cast = reshape(shape = var_1011, x = tensor_63_cast)[name = tensor("op_1012_cast")]; + tensor var_1013_perm_0 = const()[name = tensor("op_1013_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1020 = const()[name = tensor("op_1020"), val = tensor([1, 77, 20, 64])]; + tensor var_1021_cast = reshape(shape = var_1020, x = tensor_65_cast)[name = tensor("op_1021_cast")]; + tensor var_1022_perm_0 = const()[name = tensor("op_1022_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1024 = const()[name = tensor("op_1024"), val = tensor([20, -1, 64])]; + tensor transpose_108 = transpose(perm = var_1022_perm_0, x = var_1021_cast)[name = tensor("transpose_108")]; + tensor query_states_21_cast = reshape(shape = var_1024, x = transpose_108)[name = tensor("query_states_21_cast")]; + tensor var_1026 = const()[name = tensor("op_1026"), val = tensor([20, -1, 64])]; + tensor transpose_110 = transpose(perm = var_1006_perm_0, x = var_1005_cast)[name = tensor("transpose_110")]; + tensor key_states_43_cast = reshape(shape = var_1026, x = transpose_110)[name = tensor("key_states_43_cast")]; + tensor var_1028 = const()[name = tensor("op_1028"), val = tensor([20, -1, 64])]; + tensor transpose_109 = transpose(perm = var_1013_perm_0, x = var_1012_cast)[name = tensor("transpose_109")]; + tensor value_states_43_cast = reshape(shape = var_1028, x = transpose_109)[name = tensor("value_states_43_cast")]; + tensor var_1031_perm_0 = const()[name = tensor("op_1031_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor transpose_107 = transpose(perm = var_1031_perm_0, x = key_states_43_cast)[name = tensor("transpose_107")]; + tensor attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = query_states_21_cast, y = transpose_107)[name = tensor("attn_weights_61_cast")]; + tensor var_1033 = const()[name = tensor("op_1033"), val = tensor([1, 20, 77, 77])]; + tensor var_1034_cast = reshape(shape = var_1033, x = attn_weights_61_cast)[name = tensor("op_1034_cast")]; + tensor attn_weights_63_cast = add(x = var_1034_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_63_cast")]; + tensor var_1039 = const()[name = tensor("op_1039"), val = tensor([20, 77, 77])]; + tensor input_165_cast = reshape(shape = var_1039, x = attn_weights_63_cast)[name = tensor("input_165_cast")]; + tensor input_167_cast = softmax(axis = var_5, x = input_165_cast)[name = tensor("input_167_cast")]; + tensor attn_output_61_transpose_x_0 = const()[name = tensor("attn_output_61_transpose_x_0"), val = tensor(false)]; + tensor attn_output_61_transpose_y_0 = const()[name = tensor("attn_output_61_transpose_y_0"), val = tensor(false)]; + tensor attn_output_61_cast = matmul(transpose_x = attn_output_61_transpose_x_0, transpose_y = attn_output_61_transpose_y_0, x = input_167_cast, y = value_states_43_cast)[name = tensor("attn_output_61_cast")]; + tensor var_1044 = const()[name = tensor("op_1044"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_63_cast = reshape(shape = var_1044, x = attn_output_61_cast)[name = tensor("attn_output_63_cast")]; + tensor attn_output_65_perm_0 = const()[name = tensor("attn_output_65_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1047 = const()[name = tensor("op_1047"), val = tensor([1, 77, 1280])]; + tensor transpose_106 = transpose(perm = attn_output_65_perm_0, x = attn_output_63_cast)[name = tensor("transpose_106")]; + tensor input_169_cast = reshape(shape = var_1047, x = transpose_106)[name = tensor("input_169_cast")]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(530096512)))]; + tensor text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(533373376)))]; + tensor hidden_states_63_cast = linear(bias = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_self_attn_out_proj_weight_to_fp16, x = input_169_cast)[name = tensor("hidden_states_63_cast")]; + tensor input_171_cast = add(x = input_163_cast, y = hidden_states_63_cast)[name = tensor("input_171_cast")]; + tensor input_173_axes_0 = const()[name = tensor("input_173_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(533376000)))]; + tensor text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(533378624)))]; + tensor input_173_cast = layer_norm(axes = input_173_axes_0, beta = text_encoder_text_model_encoder_layers_10_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_10_layer_norm2_weight_to_fp16, x = input_171_cast)[name = tensor("input_173_cast")]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(533381248)))]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546488512)))]; + tensor input_175_cast = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_mlp_fc1_weight_to_fp16, x = input_173_cast)[name = tensor("input_175_cast")]; + tensor input_177_mode_0 = const()[name = tensor("input_177_mode_0"), val = tensor("EXACT")]; + tensor input_177_cast = gelu(mode = input_177_mode_0, x = input_175_cast)[name = tensor("input_177_cast")]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546498816)))]; + tensor text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(559606080)))]; + tensor hidden_states_65_cast = linear(bias = text_encoder_text_model_encoder_layers_10_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_10_mlp_fc2_weight_to_fp16, x = input_177_cast)[name = tensor("hidden_states_65_cast")]; + tensor input_179_cast = add(x = input_171_cast, y = hidden_states_65_cast)[name = tensor("input_179_cast")]; + tensor hidden_states_67_axes_0 = const()[name = tensor("hidden_states_67_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(559608704)))]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(559611328)))]; + tensor hidden_states_67_cast = layer_norm(axes = hidden_states_67_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm1_weight_to_fp16, x = input_179_cast)[name = tensor("hidden_states_67_cast")]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(559613952)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(562890816)))]; + tensor var_1085_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_q_proj_weight_to_fp16, x = hidden_states_67_cast)[name = tensor("op_1085_cast")]; + tensor var_1086_to_fp16 = const()[name = tensor("op_1086_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_71_cast = mul(x = var_1085_cast, y = var_1086_to_fp16)[name = tensor("tensor_71_cast")]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(562893440)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566170304)))]; + tensor tensor_67_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_k_proj_weight_to_fp16, x = hidden_states_67_cast)[name = tensor("tensor_67_cast")]; + tensor var_1091 = const()[name = tensor("op_1091"), val = tensor([1, -1, 20, 64])]; + tensor var_1092_cast = reshape(shape = var_1091, x = tensor_67_cast)[name = tensor("op_1092_cast")]; + tensor var_1093_perm_0 = const()[name = tensor("op_1093_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566172928)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569449792)))]; + tensor tensor_69_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_v_proj_weight_to_fp16, x = hidden_states_67_cast)[name = tensor("tensor_69_cast")]; + tensor var_1098 = const()[name = tensor("op_1098"), val = tensor([1, -1, 20, 64])]; + tensor var_1099_cast = reshape(shape = var_1098, x = tensor_69_cast)[name = tensor("op_1099_cast")]; + tensor var_1100_perm_0 = const()[name = tensor("op_1100_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1107 = const()[name = tensor("op_1107"), val = tensor([1, 77, 20, 64])]; + tensor var_1108_cast = reshape(shape = var_1107, x = tensor_71_cast)[name = tensor("op_1108_cast")]; + tensor var_1109_perm_0 = const()[name = tensor("op_1109_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1111 = const()[name = tensor("op_1111"), val = tensor([20, -1, 64])]; + tensor transpose_103 = transpose(perm = var_1109_perm_0, x = var_1108_cast)[name = tensor("transpose_103")]; + tensor query_states_23_cast = reshape(shape = var_1111, x = transpose_103)[name = tensor("query_states_23_cast")]; + tensor var_1113 = const()[name = tensor("op_1113"), val = tensor([20, -1, 64])]; + tensor transpose_105 = transpose(perm = var_1093_perm_0, x = var_1092_cast)[name = tensor("transpose_105")]; + tensor key_states_47_cast = reshape(shape = var_1113, x = transpose_105)[name = tensor("key_states_47_cast")]; + tensor var_1115 = const()[name = tensor("op_1115"), val = tensor([20, -1, 64])]; + tensor transpose_104 = transpose(perm = var_1100_perm_0, x = var_1099_cast)[name = tensor("transpose_104")]; + tensor value_states_47_cast = reshape(shape = var_1115, x = transpose_104)[name = tensor("value_states_47_cast")]; + tensor var_1118_perm_0 = const()[name = tensor("op_1118_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_67_transpose_x_0 = const()[name = tensor("attn_weights_67_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_67_transpose_y_0 = const()[name = tensor("attn_weights_67_transpose_y_0"), val = tensor(false)]; + tensor transpose_102 = transpose(perm = var_1118_perm_0, x = key_states_47_cast)[name = tensor("transpose_102")]; + tensor attn_weights_67_cast = matmul(transpose_x = attn_weights_67_transpose_x_0, transpose_y = attn_weights_67_transpose_y_0, x = query_states_23_cast, y = transpose_102)[name = tensor("attn_weights_67_cast")]; + tensor var_1120 = const()[name = tensor("op_1120"), val = tensor([1, 20, 77, 77])]; + tensor var_1121_cast = reshape(shape = var_1120, x = attn_weights_67_cast)[name = tensor("op_1121_cast")]; + tensor attn_weights_69_cast = add(x = var_1121_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_69_cast")]; + tensor var_1126 = const()[name = tensor("op_1126"), val = tensor([20, 77, 77])]; + tensor input_181_cast = reshape(shape = var_1126, x = attn_weights_69_cast)[name = tensor("input_181_cast")]; + tensor input_183_cast = softmax(axis = var_5, x = input_181_cast)[name = tensor("input_183_cast")]; + tensor attn_output_67_transpose_x_0 = const()[name = tensor("attn_output_67_transpose_x_0"), val = tensor(false)]; + tensor attn_output_67_transpose_y_0 = const()[name = tensor("attn_output_67_transpose_y_0"), val = tensor(false)]; + tensor attn_output_67_cast = matmul(transpose_x = attn_output_67_transpose_x_0, transpose_y = attn_output_67_transpose_y_0, x = input_183_cast, y = value_states_47_cast)[name = tensor("attn_output_67_cast")]; + tensor var_1131 = const()[name = tensor("op_1131"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_69_cast = reshape(shape = var_1131, x = attn_output_67_cast)[name = tensor("attn_output_69_cast")]; + tensor attn_output_71_perm_0 = const()[name = tensor("attn_output_71_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1134 = const()[name = tensor("op_1134"), val = tensor([1, 77, 1280])]; + tensor transpose_101 = transpose(perm = attn_output_71_perm_0, x = attn_output_69_cast)[name = tensor("transpose_101")]; + tensor input_185_cast = reshape(shape = var_1134, x = transpose_101)[name = tensor("input_185_cast")]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(569452416)))]; + tensor text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572729280)))]; + tensor hidden_states_69_cast = linear(bias = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_self_attn_out_proj_weight_to_fp16, x = input_185_cast)[name = tensor("hidden_states_69_cast")]; + tensor input_187_cast = add(x = input_179_cast, y = hidden_states_69_cast)[name = tensor("input_187_cast")]; + tensor input_189_axes_0 = const()[name = tensor("input_189_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572731904)))]; + tensor text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572734528)))]; + tensor input_189_cast = layer_norm(axes = input_189_axes_0, beta = text_encoder_text_model_encoder_layers_11_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_11_layer_norm2_weight_to_fp16, x = input_187_cast)[name = tensor("input_189_cast")]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572737152)))]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(585844416)))]; + tensor input_191_cast = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_mlp_fc1_weight_to_fp16, x = input_189_cast)[name = tensor("input_191_cast")]; + tensor input_193_mode_0 = const()[name = tensor("input_193_mode_0"), val = tensor("EXACT")]; + tensor input_193_cast = gelu(mode = input_193_mode_0, x = input_191_cast)[name = tensor("input_193_cast")]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(585854720)))]; + tensor text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598961984)))]; + tensor hidden_states_71_cast = linear(bias = text_encoder_text_model_encoder_layers_11_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_11_mlp_fc2_weight_to_fp16, x = input_193_cast)[name = tensor("hidden_states_71_cast")]; + tensor input_195_cast = add(x = input_187_cast, y = hidden_states_71_cast)[name = tensor("input_195_cast")]; + tensor hidden_states_73_axes_0 = const()[name = tensor("hidden_states_73_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598964608)))]; + tensor text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598967232)))]; + tensor hidden_states_73_cast = layer_norm(axes = hidden_states_73_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm1_weight_to_fp16, x = input_195_cast)[name = tensor("hidden_states_73_cast")]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(598969856)))]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602246720)))]; + tensor var_1172_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_q_proj_weight_to_fp16, x = hidden_states_73_cast)[name = tensor("op_1172_cast")]; + tensor var_1173_to_fp16 = const()[name = tensor("op_1173_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_77_cast = mul(x = var_1172_cast, y = var_1173_to_fp16)[name = tensor("tensor_77_cast")]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602249344)))]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605526208)))]; + tensor tensor_73_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_k_proj_weight_to_fp16, x = hidden_states_73_cast)[name = tensor("tensor_73_cast")]; + tensor var_1178 = const()[name = tensor("op_1178"), val = tensor([1, -1, 20, 64])]; + tensor var_1179_cast = reshape(shape = var_1178, x = tensor_73_cast)[name = tensor("op_1179_cast")]; + tensor var_1180_perm_0 = const()[name = tensor("op_1180_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605528832)))]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(608805696)))]; + tensor tensor_75_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_v_proj_weight_to_fp16, x = hidden_states_73_cast)[name = tensor("tensor_75_cast")]; + tensor var_1185 = const()[name = tensor("op_1185"), val = tensor([1, -1, 20, 64])]; + tensor var_1186_cast = reshape(shape = var_1185, x = tensor_75_cast)[name = tensor("op_1186_cast")]; + tensor var_1187_perm_0 = const()[name = tensor("op_1187_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1194 = const()[name = tensor("op_1194"), val = tensor([1, 77, 20, 64])]; + tensor var_1195_cast = reshape(shape = var_1194, x = tensor_77_cast)[name = tensor("op_1195_cast")]; + tensor var_1196_perm_0 = const()[name = tensor("op_1196_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1198 = const()[name = tensor("op_1198"), val = tensor([20, -1, 64])]; + tensor transpose_98 = transpose(perm = var_1196_perm_0, x = var_1195_cast)[name = tensor("transpose_98")]; + tensor query_states_25_cast = reshape(shape = var_1198, x = transpose_98)[name = tensor("query_states_25_cast")]; + tensor var_1200 = const()[name = tensor("op_1200"), val = tensor([20, -1, 64])]; + tensor transpose_100 = transpose(perm = var_1180_perm_0, x = var_1179_cast)[name = tensor("transpose_100")]; + tensor key_states_51_cast = reshape(shape = var_1200, x = transpose_100)[name = tensor("key_states_51_cast")]; + tensor var_1202 = const()[name = tensor("op_1202"), val = tensor([20, -1, 64])]; + tensor transpose_99 = transpose(perm = var_1187_perm_0, x = var_1186_cast)[name = tensor("transpose_99")]; + tensor value_states_51_cast = reshape(shape = var_1202, x = transpose_99)[name = tensor("value_states_51_cast")]; + tensor var_1205_perm_0 = const()[name = tensor("op_1205_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor transpose_97 = transpose(perm = var_1205_perm_0, x = key_states_51_cast)[name = tensor("transpose_97")]; + tensor attn_weights_73_cast = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = query_states_25_cast, y = transpose_97)[name = tensor("attn_weights_73_cast")]; + tensor var_1207 = const()[name = tensor("op_1207"), val = tensor([1, 20, 77, 77])]; + tensor var_1208_cast = reshape(shape = var_1207, x = attn_weights_73_cast)[name = tensor("op_1208_cast")]; + tensor attn_weights_75_cast = add(x = var_1208_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_75_cast")]; + tensor var_1213 = const()[name = tensor("op_1213"), val = tensor([20, 77, 77])]; + tensor input_197_cast = reshape(shape = var_1213, x = attn_weights_75_cast)[name = tensor("input_197_cast")]; + tensor input_199_cast = softmax(axis = var_5, x = input_197_cast)[name = tensor("input_199_cast")]; + tensor attn_output_73_transpose_x_0 = const()[name = tensor("attn_output_73_transpose_x_0"), val = tensor(false)]; + tensor attn_output_73_transpose_y_0 = const()[name = tensor("attn_output_73_transpose_y_0"), val = tensor(false)]; + tensor attn_output_73_cast = matmul(transpose_x = attn_output_73_transpose_x_0, transpose_y = attn_output_73_transpose_y_0, x = input_199_cast, y = value_states_51_cast)[name = tensor("attn_output_73_cast")]; + tensor var_1218 = const()[name = tensor("op_1218"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_75_cast = reshape(shape = var_1218, x = attn_output_73_cast)[name = tensor("attn_output_75_cast")]; + tensor attn_output_77_perm_0 = const()[name = tensor("attn_output_77_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1221 = const()[name = tensor("op_1221"), val = tensor([1, 77, 1280])]; + tensor transpose_96 = transpose(perm = attn_output_77_perm_0, x = attn_output_75_cast)[name = tensor("transpose_96")]; + tensor input_201_cast = reshape(shape = var_1221, x = transpose_96)[name = tensor("input_201_cast")]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(608808320)))]; + tensor text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(612085184)))]; + tensor hidden_states_75_cast = linear(bias = text_encoder_text_model_encoder_layers_12_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_self_attn_out_proj_weight_to_fp16, x = input_201_cast)[name = tensor("hidden_states_75_cast")]; + tensor input_203_cast = add(x = input_195_cast, y = hidden_states_75_cast)[name = tensor("input_203_cast")]; + tensor input_205_axes_0 = const()[name = tensor("input_205_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(612087808)))]; + tensor text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(612090432)))]; + tensor input_205_cast = layer_norm(axes = input_205_axes_0, beta = text_encoder_text_model_encoder_layers_12_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_12_layer_norm2_weight_to_fp16, x = input_203_cast)[name = tensor("input_205_cast")]; + tensor text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(612093056)))]; + tensor text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(625200320)))]; + tensor input_207_cast = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_mlp_fc1_weight_to_fp16, x = input_205_cast)[name = tensor("input_207_cast")]; + tensor input_209_mode_0 = const()[name = tensor("input_209_mode_0"), val = tensor("EXACT")]; + tensor input_209_cast = gelu(mode = input_209_mode_0, x = input_207_cast)[name = tensor("input_209_cast")]; + tensor text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(625210624)))]; + tensor text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638317888)))]; + tensor hidden_states_77_cast = linear(bias = text_encoder_text_model_encoder_layers_12_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_12_mlp_fc2_weight_to_fp16, x = input_209_cast)[name = tensor("hidden_states_77_cast")]; + tensor input_211_cast = add(x = input_203_cast, y = hidden_states_77_cast)[name = tensor("input_211_cast")]; + tensor hidden_states_79_axes_0 = const()[name = tensor("hidden_states_79_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638320512)))]; + tensor text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638323136)))]; + tensor hidden_states_79_cast = layer_norm(axes = hidden_states_79_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm1_weight_to_fp16, x = input_211_cast)[name = tensor("hidden_states_79_cast")]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638325760)))]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(641602624)))]; + tensor var_1259_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_q_proj_weight_to_fp16, x = hidden_states_79_cast)[name = tensor("op_1259_cast")]; + tensor var_1260_to_fp16 = const()[name = tensor("op_1260_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_83_cast = mul(x = var_1259_cast, y = var_1260_to_fp16)[name = tensor("tensor_83_cast")]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(641605248)))]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644882112)))]; + tensor tensor_79_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_k_proj_weight_to_fp16, x = hidden_states_79_cast)[name = tensor("tensor_79_cast")]; + tensor var_1265 = const()[name = tensor("op_1265"), val = tensor([1, -1, 20, 64])]; + tensor var_1266_cast = reshape(shape = var_1265, x = tensor_79_cast)[name = tensor("op_1266_cast")]; + tensor var_1267_perm_0 = const()[name = tensor("op_1267_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644884736)))]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(648161600)))]; + tensor tensor_81_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_v_proj_weight_to_fp16, x = hidden_states_79_cast)[name = tensor("tensor_81_cast")]; + tensor var_1272 = const()[name = tensor("op_1272"), val = tensor([1, -1, 20, 64])]; + tensor var_1273_cast = reshape(shape = var_1272, x = tensor_81_cast)[name = tensor("op_1273_cast")]; + tensor var_1274_perm_0 = const()[name = tensor("op_1274_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1281 = const()[name = tensor("op_1281"), val = tensor([1, 77, 20, 64])]; + tensor var_1282_cast = reshape(shape = var_1281, x = tensor_83_cast)[name = tensor("op_1282_cast")]; + tensor var_1283_perm_0 = const()[name = tensor("op_1283_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1285 = const()[name = tensor("op_1285"), val = tensor([20, -1, 64])]; + tensor transpose_93 = transpose(perm = var_1283_perm_0, x = var_1282_cast)[name = tensor("transpose_93")]; + tensor query_states_27_cast = reshape(shape = var_1285, x = transpose_93)[name = tensor("query_states_27_cast")]; + tensor var_1287 = const()[name = tensor("op_1287"), val = tensor([20, -1, 64])]; + tensor transpose_95 = transpose(perm = var_1267_perm_0, x = var_1266_cast)[name = tensor("transpose_95")]; + tensor key_states_55_cast = reshape(shape = var_1287, x = transpose_95)[name = tensor("key_states_55_cast")]; + tensor var_1289 = const()[name = tensor("op_1289"), val = tensor([20, -1, 64])]; + tensor transpose_94 = transpose(perm = var_1274_perm_0, x = var_1273_cast)[name = tensor("transpose_94")]; + tensor value_states_55_cast = reshape(shape = var_1289, x = transpose_94)[name = tensor("value_states_55_cast")]; + tensor var_1292_perm_0 = const()[name = tensor("op_1292_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_79_transpose_x_0 = const()[name = tensor("attn_weights_79_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_79_transpose_y_0 = const()[name = tensor("attn_weights_79_transpose_y_0"), val = tensor(false)]; + tensor transpose_92 = transpose(perm = var_1292_perm_0, x = key_states_55_cast)[name = tensor("transpose_92")]; + tensor attn_weights_79_cast = matmul(transpose_x = attn_weights_79_transpose_x_0, transpose_y = attn_weights_79_transpose_y_0, x = query_states_27_cast, y = transpose_92)[name = tensor("attn_weights_79_cast")]; + tensor var_1294 = const()[name = tensor("op_1294"), val = tensor([1, 20, 77, 77])]; + tensor var_1295_cast = reshape(shape = var_1294, x = attn_weights_79_cast)[name = tensor("op_1295_cast")]; + tensor attn_weights_81_cast = add(x = var_1295_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_81_cast")]; + tensor var_1300 = const()[name = tensor("op_1300"), val = tensor([20, 77, 77])]; + tensor input_213_cast = reshape(shape = var_1300, x = attn_weights_81_cast)[name = tensor("input_213_cast")]; + tensor input_215_cast = softmax(axis = var_5, x = input_213_cast)[name = tensor("input_215_cast")]; + tensor attn_output_79_transpose_x_0 = const()[name = tensor("attn_output_79_transpose_x_0"), val = tensor(false)]; + tensor attn_output_79_transpose_y_0 = const()[name = tensor("attn_output_79_transpose_y_0"), val = tensor(false)]; + tensor attn_output_79_cast = matmul(transpose_x = attn_output_79_transpose_x_0, transpose_y = attn_output_79_transpose_y_0, x = input_215_cast, y = value_states_55_cast)[name = tensor("attn_output_79_cast")]; + tensor var_1305 = const()[name = tensor("op_1305"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_81_cast = reshape(shape = var_1305, x = attn_output_79_cast)[name = tensor("attn_output_81_cast")]; + tensor attn_output_83_perm_0 = const()[name = tensor("attn_output_83_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1308 = const()[name = tensor("op_1308"), val = tensor([1, 77, 1280])]; + tensor transpose_91 = transpose(perm = attn_output_83_perm_0, x = attn_output_81_cast)[name = tensor("transpose_91")]; + tensor input_217_cast = reshape(shape = var_1308, x = transpose_91)[name = tensor("input_217_cast")]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(648164224)))]; + tensor text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(651441088)))]; + tensor hidden_states_81_cast = linear(bias = text_encoder_text_model_encoder_layers_13_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_self_attn_out_proj_weight_to_fp16, x = input_217_cast)[name = tensor("hidden_states_81_cast")]; + tensor input_219_cast = add(x = input_211_cast, y = hidden_states_81_cast)[name = tensor("input_219_cast")]; + tensor input_221_axes_0 = const()[name = tensor("input_221_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(651443712)))]; + tensor text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(651446336)))]; + tensor input_221_cast = layer_norm(axes = input_221_axes_0, beta = text_encoder_text_model_encoder_layers_13_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_13_layer_norm2_weight_to_fp16, x = input_219_cast)[name = tensor("input_221_cast")]; + tensor text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(651448960)))]; + tensor text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(664556224)))]; + tensor input_223_cast = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_mlp_fc1_weight_to_fp16, x = input_221_cast)[name = tensor("input_223_cast")]; + tensor input_225_mode_0 = const()[name = tensor("input_225_mode_0"), val = tensor("EXACT")]; + tensor input_225_cast = gelu(mode = input_225_mode_0, x = input_223_cast)[name = tensor("input_225_cast")]; + tensor text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(664566528)))]; + tensor text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677673792)))]; + tensor hidden_states_83_cast = linear(bias = text_encoder_text_model_encoder_layers_13_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_13_mlp_fc2_weight_to_fp16, x = input_225_cast)[name = tensor("hidden_states_83_cast")]; + tensor input_227_cast = add(x = input_219_cast, y = hidden_states_83_cast)[name = tensor("input_227_cast")]; + tensor hidden_states_85_axes_0 = const()[name = tensor("hidden_states_85_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677676416)))]; + tensor text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677679040)))]; + tensor hidden_states_85_cast = layer_norm(axes = hidden_states_85_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm1_weight_to_fp16, x = input_227_cast)[name = tensor("hidden_states_85_cast")]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677681664)))]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(680958528)))]; + tensor var_1346_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_q_proj_weight_to_fp16, x = hidden_states_85_cast)[name = tensor("op_1346_cast")]; + tensor var_1347_to_fp16 = const()[name = tensor("op_1347_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_89_cast = mul(x = var_1346_cast, y = var_1347_to_fp16)[name = tensor("tensor_89_cast")]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(680961152)))]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(684238016)))]; + tensor tensor_85_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_k_proj_weight_to_fp16, x = hidden_states_85_cast)[name = tensor("tensor_85_cast")]; + tensor var_1352 = const()[name = tensor("op_1352"), val = tensor([1, -1, 20, 64])]; + tensor var_1353_cast = reshape(shape = var_1352, x = tensor_85_cast)[name = tensor("op_1353_cast")]; + tensor var_1354_perm_0 = const()[name = tensor("op_1354_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(684240640)))]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(687517504)))]; + tensor tensor_87_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_v_proj_weight_to_fp16, x = hidden_states_85_cast)[name = tensor("tensor_87_cast")]; + tensor var_1359 = const()[name = tensor("op_1359"), val = tensor([1, -1, 20, 64])]; + tensor var_1360_cast = reshape(shape = var_1359, x = tensor_87_cast)[name = tensor("op_1360_cast")]; + tensor var_1361_perm_0 = const()[name = tensor("op_1361_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1368 = const()[name = tensor("op_1368"), val = tensor([1, 77, 20, 64])]; + tensor var_1369_cast = reshape(shape = var_1368, x = tensor_89_cast)[name = tensor("op_1369_cast")]; + tensor var_1370_perm_0 = const()[name = tensor("op_1370_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1372 = const()[name = tensor("op_1372"), val = tensor([20, -1, 64])]; + tensor transpose_88 = transpose(perm = var_1370_perm_0, x = var_1369_cast)[name = tensor("transpose_88")]; + tensor query_states_29_cast = reshape(shape = var_1372, x = transpose_88)[name = tensor("query_states_29_cast")]; + tensor var_1374 = const()[name = tensor("op_1374"), val = tensor([20, -1, 64])]; + tensor transpose_90 = transpose(perm = var_1354_perm_0, x = var_1353_cast)[name = tensor("transpose_90")]; + tensor key_states_59_cast = reshape(shape = var_1374, x = transpose_90)[name = tensor("key_states_59_cast")]; + tensor var_1376 = const()[name = tensor("op_1376"), val = tensor([20, -1, 64])]; + tensor transpose_89 = transpose(perm = var_1361_perm_0, x = var_1360_cast)[name = tensor("transpose_89")]; + tensor value_states_59_cast = reshape(shape = var_1376, x = transpose_89)[name = tensor("value_states_59_cast")]; + tensor var_1379_perm_0 = const()[name = tensor("op_1379_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor transpose_87 = transpose(perm = var_1379_perm_0, x = key_states_59_cast)[name = tensor("transpose_87")]; + tensor attn_weights_85_cast = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = query_states_29_cast, y = transpose_87)[name = tensor("attn_weights_85_cast")]; + tensor var_1381 = const()[name = tensor("op_1381"), val = tensor([1, 20, 77, 77])]; + tensor var_1382_cast = reshape(shape = var_1381, x = attn_weights_85_cast)[name = tensor("op_1382_cast")]; + tensor attn_weights_87_cast = add(x = var_1382_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_87_cast")]; + tensor var_1387 = const()[name = tensor("op_1387"), val = tensor([20, 77, 77])]; + tensor input_229_cast = reshape(shape = var_1387, x = attn_weights_87_cast)[name = tensor("input_229_cast")]; + tensor input_231_cast = softmax(axis = var_5, x = input_229_cast)[name = tensor("input_231_cast")]; + tensor attn_output_85_transpose_x_0 = const()[name = tensor("attn_output_85_transpose_x_0"), val = tensor(false)]; + tensor attn_output_85_transpose_y_0 = const()[name = tensor("attn_output_85_transpose_y_0"), val = tensor(false)]; + tensor attn_output_85_cast = matmul(transpose_x = attn_output_85_transpose_x_0, transpose_y = attn_output_85_transpose_y_0, x = input_231_cast, y = value_states_59_cast)[name = tensor("attn_output_85_cast")]; + tensor var_1392 = const()[name = tensor("op_1392"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_87_cast = reshape(shape = var_1392, x = attn_output_85_cast)[name = tensor("attn_output_87_cast")]; + tensor attn_output_89_perm_0 = const()[name = tensor("attn_output_89_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1395 = const()[name = tensor("op_1395"), val = tensor([1, 77, 1280])]; + tensor transpose_86 = transpose(perm = attn_output_89_perm_0, x = attn_output_87_cast)[name = tensor("transpose_86")]; + tensor input_233_cast = reshape(shape = var_1395, x = transpose_86)[name = tensor("input_233_cast")]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(687520128)))]; + tensor text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(690796992)))]; + tensor hidden_states_87_cast = linear(bias = text_encoder_text_model_encoder_layers_14_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_self_attn_out_proj_weight_to_fp16, x = input_233_cast)[name = tensor("hidden_states_87_cast")]; + tensor input_235_cast = add(x = input_227_cast, y = hidden_states_87_cast)[name = tensor("input_235_cast")]; + tensor input_237_axes_0 = const()[name = tensor("input_237_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(690799616)))]; + tensor text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(690802240)))]; + tensor input_237_cast = layer_norm(axes = input_237_axes_0, beta = text_encoder_text_model_encoder_layers_14_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_14_layer_norm2_weight_to_fp16, x = input_235_cast)[name = tensor("input_237_cast")]; + tensor text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(690804864)))]; + tensor text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703912128)))]; + tensor input_239_cast = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_mlp_fc1_weight_to_fp16, x = input_237_cast)[name = tensor("input_239_cast")]; + tensor input_241_mode_0 = const()[name = tensor("input_241_mode_0"), val = tensor("EXACT")]; + tensor input_241_cast = gelu(mode = input_241_mode_0, x = input_239_cast)[name = tensor("input_241_cast")]; + tensor text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703922432)))]; + tensor text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(717029696)))]; + tensor hidden_states_89_cast = linear(bias = text_encoder_text_model_encoder_layers_14_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_14_mlp_fc2_weight_to_fp16, x = input_241_cast)[name = tensor("hidden_states_89_cast")]; + tensor input_243_cast = add(x = input_235_cast, y = hidden_states_89_cast)[name = tensor("input_243_cast")]; + tensor hidden_states_91_axes_0 = const()[name = tensor("hidden_states_91_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(717032320)))]; + tensor text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(717034944)))]; + tensor hidden_states_91_cast = layer_norm(axes = hidden_states_91_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm1_weight_to_fp16, x = input_243_cast)[name = tensor("hidden_states_91_cast")]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(717037568)))]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(720314432)))]; + tensor var_1433_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_q_proj_weight_to_fp16, x = hidden_states_91_cast)[name = tensor("op_1433_cast")]; + tensor var_1434_to_fp16 = const()[name = tensor("op_1434_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_95_cast = mul(x = var_1433_cast, y = var_1434_to_fp16)[name = tensor("tensor_95_cast")]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(720317056)))]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(723593920)))]; + tensor tensor_91_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_k_proj_weight_to_fp16, x = hidden_states_91_cast)[name = tensor("tensor_91_cast")]; + tensor var_1439 = const()[name = tensor("op_1439"), val = tensor([1, -1, 20, 64])]; + tensor var_1440_cast = reshape(shape = var_1439, x = tensor_91_cast)[name = tensor("op_1440_cast")]; + tensor var_1441_perm_0 = const()[name = tensor("op_1441_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(723596544)))]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726873408)))]; + tensor tensor_93_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_v_proj_weight_to_fp16, x = hidden_states_91_cast)[name = tensor("tensor_93_cast")]; + tensor var_1446 = const()[name = tensor("op_1446"), val = tensor([1, -1, 20, 64])]; + tensor var_1447_cast = reshape(shape = var_1446, x = tensor_93_cast)[name = tensor("op_1447_cast")]; + tensor var_1448_perm_0 = const()[name = tensor("op_1448_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1455 = const()[name = tensor("op_1455"), val = tensor([1, 77, 20, 64])]; + tensor var_1456_cast = reshape(shape = var_1455, x = tensor_95_cast)[name = tensor("op_1456_cast")]; + tensor var_1457_perm_0 = const()[name = tensor("op_1457_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1459 = const()[name = tensor("op_1459"), val = tensor([20, -1, 64])]; + tensor transpose_83 = transpose(perm = var_1457_perm_0, x = var_1456_cast)[name = tensor("transpose_83")]; + tensor query_states_31_cast = reshape(shape = var_1459, x = transpose_83)[name = tensor("query_states_31_cast")]; + tensor var_1461 = const()[name = tensor("op_1461"), val = tensor([20, -1, 64])]; + tensor transpose_85 = transpose(perm = var_1441_perm_0, x = var_1440_cast)[name = tensor("transpose_85")]; + tensor key_states_63_cast = reshape(shape = var_1461, x = transpose_85)[name = tensor("key_states_63_cast")]; + tensor var_1463 = const()[name = tensor("op_1463"), val = tensor([20, -1, 64])]; + tensor transpose_84 = transpose(perm = var_1448_perm_0, x = var_1447_cast)[name = tensor("transpose_84")]; + tensor value_states_63_cast = reshape(shape = var_1463, x = transpose_84)[name = tensor("value_states_63_cast")]; + tensor var_1466_perm_0 = const()[name = tensor("op_1466_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_91_transpose_x_0 = const()[name = tensor("attn_weights_91_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_91_transpose_y_0 = const()[name = tensor("attn_weights_91_transpose_y_0"), val = tensor(false)]; + tensor transpose_82 = transpose(perm = var_1466_perm_0, x = key_states_63_cast)[name = tensor("transpose_82")]; + tensor attn_weights_91_cast = matmul(transpose_x = attn_weights_91_transpose_x_0, transpose_y = attn_weights_91_transpose_y_0, x = query_states_31_cast, y = transpose_82)[name = tensor("attn_weights_91_cast")]; + tensor var_1468 = const()[name = tensor("op_1468"), val = tensor([1, 20, 77, 77])]; + tensor var_1469_cast = reshape(shape = var_1468, x = attn_weights_91_cast)[name = tensor("op_1469_cast")]; + tensor attn_weights_93_cast = add(x = var_1469_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_93_cast")]; + tensor var_1474 = const()[name = tensor("op_1474"), val = tensor([20, 77, 77])]; + tensor input_245_cast = reshape(shape = var_1474, x = attn_weights_93_cast)[name = tensor("input_245_cast")]; + tensor input_247_cast = softmax(axis = var_5, x = input_245_cast)[name = tensor("input_247_cast")]; + tensor attn_output_91_transpose_x_0 = const()[name = tensor("attn_output_91_transpose_x_0"), val = tensor(false)]; + tensor attn_output_91_transpose_y_0 = const()[name = tensor("attn_output_91_transpose_y_0"), val = tensor(false)]; + tensor attn_output_91_cast = matmul(transpose_x = attn_output_91_transpose_x_0, transpose_y = attn_output_91_transpose_y_0, x = input_247_cast, y = value_states_63_cast)[name = tensor("attn_output_91_cast")]; + tensor var_1479 = const()[name = tensor("op_1479"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_93_cast = reshape(shape = var_1479, x = attn_output_91_cast)[name = tensor("attn_output_93_cast")]; + tensor attn_output_95_perm_0 = const()[name = tensor("attn_output_95_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1482 = const()[name = tensor("op_1482"), val = tensor([1, 77, 1280])]; + tensor transpose_81 = transpose(perm = attn_output_95_perm_0, x = attn_output_93_cast)[name = tensor("transpose_81")]; + tensor input_249_cast = reshape(shape = var_1482, x = transpose_81)[name = tensor("input_249_cast")]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(726876032)))]; + tensor text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730152896)))]; + tensor hidden_states_93_cast = linear(bias = text_encoder_text_model_encoder_layers_15_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_self_attn_out_proj_weight_to_fp16, x = input_249_cast)[name = tensor("hidden_states_93_cast")]; + tensor input_251_cast = add(x = input_243_cast, y = hidden_states_93_cast)[name = tensor("input_251_cast")]; + tensor input_253_axes_0 = const()[name = tensor("input_253_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730155520)))]; + tensor text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730158144)))]; + tensor input_253_cast = layer_norm(axes = input_253_axes_0, beta = text_encoder_text_model_encoder_layers_15_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_15_layer_norm2_weight_to_fp16, x = input_251_cast)[name = tensor("input_253_cast")]; + tensor text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730160768)))]; + tensor text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(743268032)))]; + tensor input_255_cast = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_mlp_fc1_weight_to_fp16, x = input_253_cast)[name = tensor("input_255_cast")]; + tensor input_257_mode_0 = const()[name = tensor("input_257_mode_0"), val = tensor("EXACT")]; + tensor input_257_cast = gelu(mode = input_257_mode_0, x = input_255_cast)[name = tensor("input_257_cast")]; + tensor text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(743278336)))]; + tensor text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(756385600)))]; + tensor hidden_states_95_cast = linear(bias = text_encoder_text_model_encoder_layers_15_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_15_mlp_fc2_weight_to_fp16, x = input_257_cast)[name = tensor("hidden_states_95_cast")]; + tensor input_259_cast = add(x = input_251_cast, y = hidden_states_95_cast)[name = tensor("input_259_cast")]; + tensor hidden_states_97_axes_0 = const()[name = tensor("hidden_states_97_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(756388224)))]; + tensor text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(756390848)))]; + tensor hidden_states_97_cast = layer_norm(axes = hidden_states_97_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm1_weight_to_fp16, x = input_259_cast)[name = tensor("hidden_states_97_cast")]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(756393472)))]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(759670336)))]; + tensor var_1520_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_q_proj_weight_to_fp16, x = hidden_states_97_cast)[name = tensor("op_1520_cast")]; + tensor var_1521_to_fp16 = const()[name = tensor("op_1521_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_101_cast = mul(x = var_1520_cast, y = var_1521_to_fp16)[name = tensor("tensor_101_cast")]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(759672960)))]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762949824)))]; + tensor tensor_97_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_k_proj_weight_to_fp16, x = hidden_states_97_cast)[name = tensor("tensor_97_cast")]; + tensor var_1526 = const()[name = tensor("op_1526"), val = tensor([1, -1, 20, 64])]; + tensor var_1527_cast = reshape(shape = var_1526, x = tensor_97_cast)[name = tensor("op_1527_cast")]; + tensor var_1528_perm_0 = const()[name = tensor("op_1528_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(762952448)))]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(766229312)))]; + tensor tensor_99_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_v_proj_weight_to_fp16, x = hidden_states_97_cast)[name = tensor("tensor_99_cast")]; + tensor var_1533 = const()[name = tensor("op_1533"), val = tensor([1, -1, 20, 64])]; + tensor var_1534_cast = reshape(shape = var_1533, x = tensor_99_cast)[name = tensor("op_1534_cast")]; + tensor var_1535_perm_0 = const()[name = tensor("op_1535_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1542 = const()[name = tensor("op_1542"), val = tensor([1, 77, 20, 64])]; + tensor var_1543_cast = reshape(shape = var_1542, x = tensor_101_cast)[name = tensor("op_1543_cast")]; + tensor var_1544_perm_0 = const()[name = tensor("op_1544_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1546 = const()[name = tensor("op_1546"), val = tensor([20, -1, 64])]; + tensor transpose_78 = transpose(perm = var_1544_perm_0, x = var_1543_cast)[name = tensor("transpose_78")]; + tensor query_states_33_cast = reshape(shape = var_1546, x = transpose_78)[name = tensor("query_states_33_cast")]; + tensor var_1548 = const()[name = tensor("op_1548"), val = tensor([20, -1, 64])]; + tensor transpose_80 = transpose(perm = var_1528_perm_0, x = var_1527_cast)[name = tensor("transpose_80")]; + tensor key_states_67_cast = reshape(shape = var_1548, x = transpose_80)[name = tensor("key_states_67_cast")]; + tensor var_1550 = const()[name = tensor("op_1550"), val = tensor([20, -1, 64])]; + tensor transpose_79 = transpose(perm = var_1535_perm_0, x = var_1534_cast)[name = tensor("transpose_79")]; + tensor value_states_67_cast = reshape(shape = var_1550, x = transpose_79)[name = tensor("value_states_67_cast")]; + tensor var_1553_perm_0 = const()[name = tensor("op_1553_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor transpose_77 = transpose(perm = var_1553_perm_0, x = key_states_67_cast)[name = tensor("transpose_77")]; + tensor attn_weights_97_cast = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = query_states_33_cast, y = transpose_77)[name = tensor("attn_weights_97_cast")]; + tensor var_1555 = const()[name = tensor("op_1555"), val = tensor([1, 20, 77, 77])]; + tensor var_1556_cast = reshape(shape = var_1555, x = attn_weights_97_cast)[name = tensor("op_1556_cast")]; + tensor attn_weights_99_cast = add(x = var_1556_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_99_cast")]; + tensor var_1561 = const()[name = tensor("op_1561"), val = tensor([20, 77, 77])]; + tensor input_261_cast = reshape(shape = var_1561, x = attn_weights_99_cast)[name = tensor("input_261_cast")]; + tensor input_263_cast = softmax(axis = var_5, x = input_261_cast)[name = tensor("input_263_cast")]; + tensor attn_output_97_transpose_x_0 = const()[name = tensor("attn_output_97_transpose_x_0"), val = tensor(false)]; + tensor attn_output_97_transpose_y_0 = const()[name = tensor("attn_output_97_transpose_y_0"), val = tensor(false)]; + tensor attn_output_97_cast = matmul(transpose_x = attn_output_97_transpose_x_0, transpose_y = attn_output_97_transpose_y_0, x = input_263_cast, y = value_states_67_cast)[name = tensor("attn_output_97_cast")]; + tensor var_1566 = const()[name = tensor("op_1566"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_99_cast = reshape(shape = var_1566, x = attn_output_97_cast)[name = tensor("attn_output_99_cast")]; + tensor attn_output_101_perm_0 = const()[name = tensor("attn_output_101_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1569 = const()[name = tensor("op_1569"), val = tensor([1, 77, 1280])]; + tensor transpose_76 = transpose(perm = attn_output_101_perm_0, x = attn_output_99_cast)[name = tensor("transpose_76")]; + tensor input_265_cast = reshape(shape = var_1569, x = transpose_76)[name = tensor("input_265_cast")]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(766231936)))]; + tensor text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(769508800)))]; + tensor hidden_states_99_cast = linear(bias = text_encoder_text_model_encoder_layers_16_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_self_attn_out_proj_weight_to_fp16, x = input_265_cast)[name = tensor("hidden_states_99_cast")]; + tensor input_267_cast = add(x = input_259_cast, y = hidden_states_99_cast)[name = tensor("input_267_cast")]; + tensor input_269_axes_0 = const()[name = tensor("input_269_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(769511424)))]; + tensor text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(769514048)))]; + tensor input_269_cast = layer_norm(axes = input_269_axes_0, beta = text_encoder_text_model_encoder_layers_16_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_16_layer_norm2_weight_to_fp16, x = input_267_cast)[name = tensor("input_269_cast")]; + tensor text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(769516672)))]; + tensor text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(782623936)))]; + tensor input_271_cast = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_mlp_fc1_weight_to_fp16, x = input_269_cast)[name = tensor("input_271_cast")]; + tensor input_273_mode_0 = const()[name = tensor("input_273_mode_0"), val = tensor("EXACT")]; + tensor input_273_cast = gelu(mode = input_273_mode_0, x = input_271_cast)[name = tensor("input_273_cast")]; + tensor text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(782634240)))]; + tensor text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795741504)))]; + tensor hidden_states_101_cast = linear(bias = text_encoder_text_model_encoder_layers_16_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_16_mlp_fc2_weight_to_fp16, x = input_273_cast)[name = tensor("hidden_states_101_cast")]; + tensor input_275_cast = add(x = input_267_cast, y = hidden_states_101_cast)[name = tensor("input_275_cast")]; + tensor hidden_states_103_axes_0 = const()[name = tensor("hidden_states_103_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795744128)))]; + tensor text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795746752)))]; + tensor hidden_states_103_cast = layer_norm(axes = hidden_states_103_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm1_weight_to_fp16, x = input_275_cast)[name = tensor("hidden_states_103_cast")]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(795749376)))]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799026240)))]; + tensor var_1607_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_q_proj_weight_to_fp16, x = hidden_states_103_cast)[name = tensor("op_1607_cast")]; + tensor var_1608_to_fp16 = const()[name = tensor("op_1608_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_107_cast = mul(x = var_1607_cast, y = var_1608_to_fp16)[name = tensor("tensor_107_cast")]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799028864)))]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(802305728)))]; + tensor tensor_103_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_k_proj_weight_to_fp16, x = hidden_states_103_cast)[name = tensor("tensor_103_cast")]; + tensor var_1613 = const()[name = tensor("op_1613"), val = tensor([1, -1, 20, 64])]; + tensor var_1614_cast = reshape(shape = var_1613, x = tensor_103_cast)[name = tensor("op_1614_cast")]; + tensor var_1615_perm_0 = const()[name = tensor("op_1615_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(802308352)))]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805585216)))]; + tensor tensor_105_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_v_proj_weight_to_fp16, x = hidden_states_103_cast)[name = tensor("tensor_105_cast")]; + tensor var_1620 = const()[name = tensor("op_1620"), val = tensor([1, -1, 20, 64])]; + tensor var_1621_cast = reshape(shape = var_1620, x = tensor_105_cast)[name = tensor("op_1621_cast")]; + tensor var_1622_perm_0 = const()[name = tensor("op_1622_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1629 = const()[name = tensor("op_1629"), val = tensor([1, 77, 20, 64])]; + tensor var_1630_cast = reshape(shape = var_1629, x = tensor_107_cast)[name = tensor("op_1630_cast")]; + tensor var_1631_perm_0 = const()[name = tensor("op_1631_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1633 = const()[name = tensor("op_1633"), val = tensor([20, -1, 64])]; + tensor transpose_73 = transpose(perm = var_1631_perm_0, x = var_1630_cast)[name = tensor("transpose_73")]; + tensor query_states_35_cast = reshape(shape = var_1633, x = transpose_73)[name = tensor("query_states_35_cast")]; + tensor var_1635 = const()[name = tensor("op_1635"), val = tensor([20, -1, 64])]; + tensor transpose_75 = transpose(perm = var_1615_perm_0, x = var_1614_cast)[name = tensor("transpose_75")]; + tensor key_states_71_cast = reshape(shape = var_1635, x = transpose_75)[name = tensor("key_states_71_cast")]; + tensor var_1637 = const()[name = tensor("op_1637"), val = tensor([20, -1, 64])]; + tensor transpose_74 = transpose(perm = var_1622_perm_0, x = var_1621_cast)[name = tensor("transpose_74")]; + tensor value_states_71_cast = reshape(shape = var_1637, x = transpose_74)[name = tensor("value_states_71_cast")]; + tensor var_1640_perm_0 = const()[name = tensor("op_1640_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_103_transpose_x_0 = const()[name = tensor("attn_weights_103_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_103_transpose_y_0 = const()[name = tensor("attn_weights_103_transpose_y_0"), val = tensor(false)]; + tensor transpose_72 = transpose(perm = var_1640_perm_0, x = key_states_71_cast)[name = tensor("transpose_72")]; + tensor attn_weights_103_cast = matmul(transpose_x = attn_weights_103_transpose_x_0, transpose_y = attn_weights_103_transpose_y_0, x = query_states_35_cast, y = transpose_72)[name = tensor("attn_weights_103_cast")]; + tensor var_1642 = const()[name = tensor("op_1642"), val = tensor([1, 20, 77, 77])]; + tensor var_1643_cast = reshape(shape = var_1642, x = attn_weights_103_cast)[name = tensor("op_1643_cast")]; + tensor attn_weights_105_cast = add(x = var_1643_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_105_cast")]; + tensor var_1648 = const()[name = tensor("op_1648"), val = tensor([20, 77, 77])]; + tensor input_277_cast = reshape(shape = var_1648, x = attn_weights_105_cast)[name = tensor("input_277_cast")]; + tensor input_279_cast = softmax(axis = var_5, x = input_277_cast)[name = tensor("input_279_cast")]; + tensor attn_output_103_transpose_x_0 = const()[name = tensor("attn_output_103_transpose_x_0"), val = tensor(false)]; + tensor attn_output_103_transpose_y_0 = const()[name = tensor("attn_output_103_transpose_y_0"), val = tensor(false)]; + tensor attn_output_103_cast = matmul(transpose_x = attn_output_103_transpose_x_0, transpose_y = attn_output_103_transpose_y_0, x = input_279_cast, y = value_states_71_cast)[name = tensor("attn_output_103_cast")]; + tensor var_1653 = const()[name = tensor("op_1653"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_105_cast = reshape(shape = var_1653, x = attn_output_103_cast)[name = tensor("attn_output_105_cast")]; + tensor attn_output_107_perm_0 = const()[name = tensor("attn_output_107_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1656 = const()[name = tensor("op_1656"), val = tensor([1, 77, 1280])]; + tensor transpose_71 = transpose(perm = attn_output_107_perm_0, x = attn_output_105_cast)[name = tensor("transpose_71")]; + tensor input_281_cast = reshape(shape = var_1656, x = transpose_71)[name = tensor("input_281_cast")]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805587840)))]; + tensor text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808864704)))]; + tensor hidden_states_105_cast = linear(bias = text_encoder_text_model_encoder_layers_17_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_self_attn_out_proj_weight_to_fp16, x = input_281_cast)[name = tensor("hidden_states_105_cast")]; + tensor input_283_cast = add(x = input_275_cast, y = hidden_states_105_cast)[name = tensor("input_283_cast")]; + tensor input_285_axes_0 = const()[name = tensor("input_285_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808867328)))]; + tensor text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808869952)))]; + tensor input_285_cast = layer_norm(axes = input_285_axes_0, beta = text_encoder_text_model_encoder_layers_17_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_17_layer_norm2_weight_to_fp16, x = input_283_cast)[name = tensor("input_285_cast")]; + tensor text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808872576)))]; + tensor text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821979840)))]; + tensor input_287_cast = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_mlp_fc1_weight_to_fp16, x = input_285_cast)[name = tensor("input_287_cast")]; + tensor input_289_mode_0 = const()[name = tensor("input_289_mode_0"), val = tensor("EXACT")]; + tensor input_289_cast = gelu(mode = input_289_mode_0, x = input_287_cast)[name = tensor("input_289_cast")]; + tensor text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821990144)))]; + tensor text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835097408)))]; + tensor hidden_states_107_cast = linear(bias = text_encoder_text_model_encoder_layers_17_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_17_mlp_fc2_weight_to_fp16, x = input_289_cast)[name = tensor("hidden_states_107_cast")]; + tensor input_291_cast = add(x = input_283_cast, y = hidden_states_107_cast)[name = tensor("input_291_cast")]; + tensor hidden_states_109_axes_0 = const()[name = tensor("hidden_states_109_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835100032)))]; + tensor text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835102656)))]; + tensor hidden_states_109_cast = layer_norm(axes = hidden_states_109_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm1_weight_to_fp16, x = input_291_cast)[name = tensor("hidden_states_109_cast")]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835105280)))]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838382144)))]; + tensor var_1694_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_q_proj_weight_to_fp16, x = hidden_states_109_cast)[name = tensor("op_1694_cast")]; + tensor var_1695_to_fp16 = const()[name = tensor("op_1695_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_113_cast = mul(x = var_1694_cast, y = var_1695_to_fp16)[name = tensor("tensor_113_cast")]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838384768)))]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841661632)))]; + tensor tensor_109_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_k_proj_weight_to_fp16, x = hidden_states_109_cast)[name = tensor("tensor_109_cast")]; + tensor var_1700 = const()[name = tensor("op_1700"), val = tensor([1, -1, 20, 64])]; + tensor var_1701_cast = reshape(shape = var_1700, x = tensor_109_cast)[name = tensor("op_1701_cast")]; + tensor var_1702_perm_0 = const()[name = tensor("op_1702_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(841664256)))]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(844941120)))]; + tensor tensor_111_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_v_proj_weight_to_fp16, x = hidden_states_109_cast)[name = tensor("tensor_111_cast")]; + tensor var_1707 = const()[name = tensor("op_1707"), val = tensor([1, -1, 20, 64])]; + tensor var_1708_cast = reshape(shape = var_1707, x = tensor_111_cast)[name = tensor("op_1708_cast")]; + tensor var_1709_perm_0 = const()[name = tensor("op_1709_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1716 = const()[name = tensor("op_1716"), val = tensor([1, 77, 20, 64])]; + tensor var_1717_cast = reshape(shape = var_1716, x = tensor_113_cast)[name = tensor("op_1717_cast")]; + tensor var_1718_perm_0 = const()[name = tensor("op_1718_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1720 = const()[name = tensor("op_1720"), val = tensor([20, -1, 64])]; + tensor transpose_68 = transpose(perm = var_1718_perm_0, x = var_1717_cast)[name = tensor("transpose_68")]; + tensor query_states_37_cast = reshape(shape = var_1720, x = transpose_68)[name = tensor("query_states_37_cast")]; + tensor var_1722 = const()[name = tensor("op_1722"), val = tensor([20, -1, 64])]; + tensor transpose_70 = transpose(perm = var_1702_perm_0, x = var_1701_cast)[name = tensor("transpose_70")]; + tensor key_states_75_cast = reshape(shape = var_1722, x = transpose_70)[name = tensor("key_states_75_cast")]; + tensor var_1724 = const()[name = tensor("op_1724"), val = tensor([20, -1, 64])]; + tensor transpose_69 = transpose(perm = var_1709_perm_0, x = var_1708_cast)[name = tensor("transpose_69")]; + tensor value_states_75_cast = reshape(shape = var_1724, x = transpose_69)[name = tensor("value_states_75_cast")]; + tensor var_1727_perm_0 = const()[name = tensor("op_1727_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor transpose_67 = transpose(perm = var_1727_perm_0, x = key_states_75_cast)[name = tensor("transpose_67")]; + tensor attn_weights_109_cast = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = query_states_37_cast, y = transpose_67)[name = tensor("attn_weights_109_cast")]; + tensor var_1729 = const()[name = tensor("op_1729"), val = tensor([1, 20, 77, 77])]; + tensor var_1730_cast = reshape(shape = var_1729, x = attn_weights_109_cast)[name = tensor("op_1730_cast")]; + tensor attn_weights_111_cast = add(x = var_1730_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_111_cast")]; + tensor var_1735 = const()[name = tensor("op_1735"), val = tensor([20, 77, 77])]; + tensor input_293_cast = reshape(shape = var_1735, x = attn_weights_111_cast)[name = tensor("input_293_cast")]; + tensor input_295_cast = softmax(axis = var_5, x = input_293_cast)[name = tensor("input_295_cast")]; + tensor attn_output_109_transpose_x_0 = const()[name = tensor("attn_output_109_transpose_x_0"), val = tensor(false)]; + tensor attn_output_109_transpose_y_0 = const()[name = tensor("attn_output_109_transpose_y_0"), val = tensor(false)]; + tensor attn_output_109_cast = matmul(transpose_x = attn_output_109_transpose_x_0, transpose_y = attn_output_109_transpose_y_0, x = input_295_cast, y = value_states_75_cast)[name = tensor("attn_output_109_cast")]; + tensor var_1740 = const()[name = tensor("op_1740"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_111_cast = reshape(shape = var_1740, x = attn_output_109_cast)[name = tensor("attn_output_111_cast")]; + tensor attn_output_113_perm_0 = const()[name = tensor("attn_output_113_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1743 = const()[name = tensor("op_1743"), val = tensor([1, 77, 1280])]; + tensor transpose_66 = transpose(perm = attn_output_113_perm_0, x = attn_output_111_cast)[name = tensor("transpose_66")]; + tensor input_297_cast = reshape(shape = var_1743, x = transpose_66)[name = tensor("input_297_cast")]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(844943744)))]; + tensor text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(848220608)))]; + tensor hidden_states_111_cast = linear(bias = text_encoder_text_model_encoder_layers_18_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_self_attn_out_proj_weight_to_fp16, x = input_297_cast)[name = tensor("hidden_states_111_cast")]; + tensor input_299_cast = add(x = input_291_cast, y = hidden_states_111_cast)[name = tensor("input_299_cast")]; + tensor input_301_axes_0 = const()[name = tensor("input_301_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(848223232)))]; + tensor text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(848225856)))]; + tensor input_301_cast = layer_norm(axes = input_301_axes_0, beta = text_encoder_text_model_encoder_layers_18_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_18_layer_norm2_weight_to_fp16, x = input_299_cast)[name = tensor("input_301_cast")]; + tensor text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(848228480)))]; + tensor text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861335744)))]; + tensor input_303_cast = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_mlp_fc1_weight_to_fp16, x = input_301_cast)[name = tensor("input_303_cast")]; + tensor input_305_mode_0 = const()[name = tensor("input_305_mode_0"), val = tensor("EXACT")]; + tensor input_305_cast = gelu(mode = input_305_mode_0, x = input_303_cast)[name = tensor("input_305_cast")]; + tensor text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(861346048)))]; + tensor text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(874453312)))]; + tensor hidden_states_113_cast = linear(bias = text_encoder_text_model_encoder_layers_18_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_18_mlp_fc2_weight_to_fp16, x = input_305_cast)[name = tensor("hidden_states_113_cast")]; + tensor input_307_cast = add(x = input_299_cast, y = hidden_states_113_cast)[name = tensor("input_307_cast")]; + tensor hidden_states_115_axes_0 = const()[name = tensor("hidden_states_115_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(874455936)))]; + tensor text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(874458560)))]; + tensor hidden_states_115_cast = layer_norm(axes = hidden_states_115_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm1_weight_to_fp16, x = input_307_cast)[name = tensor("hidden_states_115_cast")]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(874461184)))]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877738048)))]; + tensor var_1781_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_q_proj_weight_to_fp16, x = hidden_states_115_cast)[name = tensor("op_1781_cast")]; + tensor var_1782_to_fp16 = const()[name = tensor("op_1782_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_119_cast = mul(x = var_1781_cast, y = var_1782_to_fp16)[name = tensor("tensor_119_cast")]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877740672)))]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(881017536)))]; + tensor tensor_115_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_k_proj_weight_to_fp16, x = hidden_states_115_cast)[name = tensor("tensor_115_cast")]; + tensor var_1787 = const()[name = tensor("op_1787"), val = tensor([1, -1, 20, 64])]; + tensor var_1788_cast = reshape(shape = var_1787, x = tensor_115_cast)[name = tensor("op_1788_cast")]; + tensor var_1789_perm_0 = const()[name = tensor("op_1789_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(881020160)))]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(884297024)))]; + tensor tensor_117_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_v_proj_weight_to_fp16, x = hidden_states_115_cast)[name = tensor("tensor_117_cast")]; + tensor var_1794 = const()[name = tensor("op_1794"), val = tensor([1, -1, 20, 64])]; + tensor var_1795_cast = reshape(shape = var_1794, x = tensor_117_cast)[name = tensor("op_1795_cast")]; + tensor var_1796_perm_0 = const()[name = tensor("op_1796_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1803 = const()[name = tensor("op_1803"), val = tensor([1, 77, 20, 64])]; + tensor var_1804_cast = reshape(shape = var_1803, x = tensor_119_cast)[name = tensor("op_1804_cast")]; + tensor var_1805_perm_0 = const()[name = tensor("op_1805_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1807 = const()[name = tensor("op_1807"), val = tensor([20, -1, 64])]; + tensor transpose_63 = transpose(perm = var_1805_perm_0, x = var_1804_cast)[name = tensor("transpose_63")]; + tensor query_states_39_cast = reshape(shape = var_1807, x = transpose_63)[name = tensor("query_states_39_cast")]; + tensor var_1809 = const()[name = tensor("op_1809"), val = tensor([20, -1, 64])]; + tensor transpose_65 = transpose(perm = var_1789_perm_0, x = var_1788_cast)[name = tensor("transpose_65")]; + tensor key_states_79_cast = reshape(shape = var_1809, x = transpose_65)[name = tensor("key_states_79_cast")]; + tensor var_1811 = const()[name = tensor("op_1811"), val = tensor([20, -1, 64])]; + tensor transpose_64 = transpose(perm = var_1796_perm_0, x = var_1795_cast)[name = tensor("transpose_64")]; + tensor value_states_79_cast = reshape(shape = var_1811, x = transpose_64)[name = tensor("value_states_79_cast")]; + tensor var_1814_perm_0 = const()[name = tensor("op_1814_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_115_transpose_x_0 = const()[name = tensor("attn_weights_115_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_115_transpose_y_0 = const()[name = tensor("attn_weights_115_transpose_y_0"), val = tensor(false)]; + tensor transpose_62 = transpose(perm = var_1814_perm_0, x = key_states_79_cast)[name = tensor("transpose_62")]; + tensor attn_weights_115_cast = matmul(transpose_x = attn_weights_115_transpose_x_0, transpose_y = attn_weights_115_transpose_y_0, x = query_states_39_cast, y = transpose_62)[name = tensor("attn_weights_115_cast")]; + tensor var_1816 = const()[name = tensor("op_1816"), val = tensor([1, 20, 77, 77])]; + tensor var_1817_cast = reshape(shape = var_1816, x = attn_weights_115_cast)[name = tensor("op_1817_cast")]; + tensor attn_weights_117_cast = add(x = var_1817_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_117_cast")]; + tensor var_1822 = const()[name = tensor("op_1822"), val = tensor([20, 77, 77])]; + tensor input_309_cast = reshape(shape = var_1822, x = attn_weights_117_cast)[name = tensor("input_309_cast")]; + tensor input_311_cast = softmax(axis = var_5, x = input_309_cast)[name = tensor("input_311_cast")]; + tensor attn_output_115_transpose_x_0 = const()[name = tensor("attn_output_115_transpose_x_0"), val = tensor(false)]; + tensor attn_output_115_transpose_y_0 = const()[name = tensor("attn_output_115_transpose_y_0"), val = tensor(false)]; + tensor attn_output_115_cast = matmul(transpose_x = attn_output_115_transpose_x_0, transpose_y = attn_output_115_transpose_y_0, x = input_311_cast, y = value_states_79_cast)[name = tensor("attn_output_115_cast")]; + tensor var_1827 = const()[name = tensor("op_1827"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_117_cast = reshape(shape = var_1827, x = attn_output_115_cast)[name = tensor("attn_output_117_cast")]; + tensor attn_output_119_perm_0 = const()[name = tensor("attn_output_119_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1830 = const()[name = tensor("op_1830"), val = tensor([1, 77, 1280])]; + tensor transpose_61 = transpose(perm = attn_output_119_perm_0, x = attn_output_117_cast)[name = tensor("transpose_61")]; + tensor input_313_cast = reshape(shape = var_1830, x = transpose_61)[name = tensor("input_313_cast")]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(884299648)))]; + tensor text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887576512)))]; + tensor hidden_states_117_cast = linear(bias = text_encoder_text_model_encoder_layers_19_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_self_attn_out_proj_weight_to_fp16, x = input_313_cast)[name = tensor("hidden_states_117_cast")]; + tensor input_315_cast = add(x = input_307_cast, y = hidden_states_117_cast)[name = tensor("input_315_cast")]; + tensor input_317_axes_0 = const()[name = tensor("input_317_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887579136)))]; + tensor text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887581760)))]; + tensor input_317_cast = layer_norm(axes = input_317_axes_0, beta = text_encoder_text_model_encoder_layers_19_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_19_layer_norm2_weight_to_fp16, x = input_315_cast)[name = tensor("input_317_cast")]; + tensor text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(887584384)))]; + tensor text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(900691648)))]; + tensor input_319_cast = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_mlp_fc1_weight_to_fp16, x = input_317_cast)[name = tensor("input_319_cast")]; + tensor input_321_mode_0 = const()[name = tensor("input_321_mode_0"), val = tensor("EXACT")]; + tensor input_321_cast = gelu(mode = input_321_mode_0, x = input_319_cast)[name = tensor("input_321_cast")]; + tensor text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(900701952)))]; + tensor text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(913809216)))]; + tensor hidden_states_119_cast = linear(bias = text_encoder_text_model_encoder_layers_19_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_19_mlp_fc2_weight_to_fp16, x = input_321_cast)[name = tensor("hidden_states_119_cast")]; + tensor input_323_cast = add(x = input_315_cast, y = hidden_states_119_cast)[name = tensor("input_323_cast")]; + tensor hidden_states_121_axes_0 = const()[name = tensor("hidden_states_121_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(913811840)))]; + tensor text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(913814464)))]; + tensor hidden_states_121_cast = layer_norm(axes = hidden_states_121_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm1_weight_to_fp16, x = input_323_cast)[name = tensor("hidden_states_121_cast")]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(913817088)))]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(917093952)))]; + tensor var_1868_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_q_proj_weight_to_fp16, x = hidden_states_121_cast)[name = tensor("op_1868_cast")]; + tensor var_1869_to_fp16 = const()[name = tensor("op_1869_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_125_cast = mul(x = var_1868_cast, y = var_1869_to_fp16)[name = tensor("tensor_125_cast")]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(917096576)))]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(920373440)))]; + tensor tensor_121_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_k_proj_weight_to_fp16, x = hidden_states_121_cast)[name = tensor("tensor_121_cast")]; + tensor var_1874 = const()[name = tensor("op_1874"), val = tensor([1, -1, 20, 64])]; + tensor var_1875_cast = reshape(shape = var_1874, x = tensor_121_cast)[name = tensor("op_1875_cast")]; + tensor var_1876_perm_0 = const()[name = tensor("op_1876_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(920376064)))]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(923652928)))]; + tensor tensor_123_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_v_proj_weight_to_fp16, x = hidden_states_121_cast)[name = tensor("tensor_123_cast")]; + tensor var_1881 = const()[name = tensor("op_1881"), val = tensor([1, -1, 20, 64])]; + tensor var_1882_cast = reshape(shape = var_1881, x = tensor_123_cast)[name = tensor("op_1882_cast")]; + tensor var_1883_perm_0 = const()[name = tensor("op_1883_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1890 = const()[name = tensor("op_1890"), val = tensor([1, 77, 20, 64])]; + tensor var_1891_cast = reshape(shape = var_1890, x = tensor_125_cast)[name = tensor("op_1891_cast")]; + tensor var_1892_perm_0 = const()[name = tensor("op_1892_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1894 = const()[name = tensor("op_1894"), val = tensor([20, -1, 64])]; + tensor transpose_58 = transpose(perm = var_1892_perm_0, x = var_1891_cast)[name = tensor("transpose_58")]; + tensor query_states_41_cast = reshape(shape = var_1894, x = transpose_58)[name = tensor("query_states_41_cast")]; + tensor var_1896 = const()[name = tensor("op_1896"), val = tensor([20, -1, 64])]; + tensor transpose_60 = transpose(perm = var_1876_perm_0, x = var_1875_cast)[name = tensor("transpose_60")]; + tensor key_states_83_cast = reshape(shape = var_1896, x = transpose_60)[name = tensor("key_states_83_cast")]; + tensor var_1898 = const()[name = tensor("op_1898"), val = tensor([20, -1, 64])]; + tensor transpose_59 = transpose(perm = var_1883_perm_0, x = var_1882_cast)[name = tensor("transpose_59")]; + tensor value_states_83_cast = reshape(shape = var_1898, x = transpose_59)[name = tensor("value_states_83_cast")]; + tensor var_1901_perm_0 = const()[name = tensor("op_1901_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor transpose_57 = transpose(perm = var_1901_perm_0, x = key_states_83_cast)[name = tensor("transpose_57")]; + tensor attn_weights_121_cast = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = query_states_41_cast, y = transpose_57)[name = tensor("attn_weights_121_cast")]; + tensor var_1903 = const()[name = tensor("op_1903"), val = tensor([1, 20, 77, 77])]; + tensor var_1904_cast = reshape(shape = var_1903, x = attn_weights_121_cast)[name = tensor("op_1904_cast")]; + tensor attn_weights_123_cast = add(x = var_1904_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_123_cast")]; + tensor var_1909 = const()[name = tensor("op_1909"), val = tensor([20, 77, 77])]; + tensor input_325_cast = reshape(shape = var_1909, x = attn_weights_123_cast)[name = tensor("input_325_cast")]; + tensor input_327_cast = softmax(axis = var_5, x = input_325_cast)[name = tensor("input_327_cast")]; + tensor attn_output_121_transpose_x_0 = const()[name = tensor("attn_output_121_transpose_x_0"), val = tensor(false)]; + tensor attn_output_121_transpose_y_0 = const()[name = tensor("attn_output_121_transpose_y_0"), val = tensor(false)]; + tensor attn_output_121_cast = matmul(transpose_x = attn_output_121_transpose_x_0, transpose_y = attn_output_121_transpose_y_0, x = input_327_cast, y = value_states_83_cast)[name = tensor("attn_output_121_cast")]; + tensor var_1914 = const()[name = tensor("op_1914"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_123_cast = reshape(shape = var_1914, x = attn_output_121_cast)[name = tensor("attn_output_123_cast")]; + tensor attn_output_125_perm_0 = const()[name = tensor("attn_output_125_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1917 = const()[name = tensor("op_1917"), val = tensor([1, 77, 1280])]; + tensor transpose_56 = transpose(perm = attn_output_125_perm_0, x = attn_output_123_cast)[name = tensor("transpose_56")]; + tensor input_329_cast = reshape(shape = var_1917, x = transpose_56)[name = tensor("input_329_cast")]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(923655552)))]; + tensor text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(926932416)))]; + tensor hidden_states_123_cast = linear(bias = text_encoder_text_model_encoder_layers_20_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_self_attn_out_proj_weight_to_fp16, x = input_329_cast)[name = tensor("hidden_states_123_cast")]; + tensor input_331_cast = add(x = input_323_cast, y = hidden_states_123_cast)[name = tensor("input_331_cast")]; + tensor input_333_axes_0 = const()[name = tensor("input_333_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(926935040)))]; + tensor text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(926937664)))]; + tensor input_333_cast = layer_norm(axes = input_333_axes_0, beta = text_encoder_text_model_encoder_layers_20_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_20_layer_norm2_weight_to_fp16, x = input_331_cast)[name = tensor("input_333_cast")]; + tensor text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(926940288)))]; + tensor text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940047552)))]; + tensor input_335_cast = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_mlp_fc1_weight_to_fp16, x = input_333_cast)[name = tensor("input_335_cast")]; + tensor input_337_mode_0 = const()[name = tensor("input_337_mode_0"), val = tensor("EXACT")]; + tensor input_337_cast = gelu(mode = input_337_mode_0, x = input_335_cast)[name = tensor("input_337_cast")]; + tensor text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(940057856)))]; + tensor text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(953165120)))]; + tensor hidden_states_125_cast = linear(bias = text_encoder_text_model_encoder_layers_20_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_20_mlp_fc2_weight_to_fp16, x = input_337_cast)[name = tensor("hidden_states_125_cast")]; + tensor input_339_cast = add(x = input_331_cast, y = hidden_states_125_cast)[name = tensor("input_339_cast")]; + tensor hidden_states_127_axes_0 = const()[name = tensor("hidden_states_127_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(953167744)))]; + tensor text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(953170368)))]; + tensor hidden_states_127_cast = layer_norm(axes = hidden_states_127_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm1_weight_to_fp16, x = input_339_cast)[name = tensor("hidden_states_127_cast")]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(953172992)))]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956449856)))]; + tensor var_1955_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_q_proj_weight_to_fp16, x = hidden_states_127_cast)[name = tensor("op_1955_cast")]; + tensor var_1956_to_fp16 = const()[name = tensor("op_1956_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_131_cast = mul(x = var_1955_cast, y = var_1956_to_fp16)[name = tensor("tensor_131_cast")]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(956452480)))]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(959729344)))]; + tensor tensor_127_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_k_proj_weight_to_fp16, x = hidden_states_127_cast)[name = tensor("tensor_127_cast")]; + tensor var_1961 = const()[name = tensor("op_1961"), val = tensor([1, -1, 20, 64])]; + tensor var_1962_cast = reshape(shape = var_1961, x = tensor_127_cast)[name = tensor("op_1962_cast")]; + tensor var_1963_perm_0 = const()[name = tensor("op_1963_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(959731968)))]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(963008832)))]; + tensor tensor_129_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_v_proj_weight_to_fp16, x = hidden_states_127_cast)[name = tensor("tensor_129_cast")]; + tensor var_1968 = const()[name = tensor("op_1968"), val = tensor([1, -1, 20, 64])]; + tensor var_1969_cast = reshape(shape = var_1968, x = tensor_129_cast)[name = tensor("op_1969_cast")]; + tensor var_1970_perm_0 = const()[name = tensor("op_1970_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1977 = const()[name = tensor("op_1977"), val = tensor([1, 77, 20, 64])]; + tensor var_1978_cast = reshape(shape = var_1977, x = tensor_131_cast)[name = tensor("op_1978_cast")]; + tensor var_1979_perm_0 = const()[name = tensor("op_1979_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_1981 = const()[name = tensor("op_1981"), val = tensor([20, -1, 64])]; + tensor transpose_53 = transpose(perm = var_1979_perm_0, x = var_1978_cast)[name = tensor("transpose_53")]; + tensor query_states_43_cast = reshape(shape = var_1981, x = transpose_53)[name = tensor("query_states_43_cast")]; + tensor var_1983 = const()[name = tensor("op_1983"), val = tensor([20, -1, 64])]; + tensor transpose_55 = transpose(perm = var_1963_perm_0, x = var_1962_cast)[name = tensor("transpose_55")]; + tensor key_states_87_cast = reshape(shape = var_1983, x = transpose_55)[name = tensor("key_states_87_cast")]; + tensor var_1985 = const()[name = tensor("op_1985"), val = tensor([20, -1, 64])]; + tensor transpose_54 = transpose(perm = var_1970_perm_0, x = var_1969_cast)[name = tensor("transpose_54")]; + tensor value_states_87_cast = reshape(shape = var_1985, x = transpose_54)[name = tensor("value_states_87_cast")]; + tensor var_1988_perm_0 = const()[name = tensor("op_1988_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_127_transpose_x_0 = const()[name = tensor("attn_weights_127_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_127_transpose_y_0 = const()[name = tensor("attn_weights_127_transpose_y_0"), val = tensor(false)]; + tensor transpose_52 = transpose(perm = var_1988_perm_0, x = key_states_87_cast)[name = tensor("transpose_52")]; + tensor attn_weights_127_cast = matmul(transpose_x = attn_weights_127_transpose_x_0, transpose_y = attn_weights_127_transpose_y_0, x = query_states_43_cast, y = transpose_52)[name = tensor("attn_weights_127_cast")]; + tensor var_1990 = const()[name = tensor("op_1990"), val = tensor([1, 20, 77, 77])]; + tensor var_1991_cast = reshape(shape = var_1990, x = attn_weights_127_cast)[name = tensor("op_1991_cast")]; + tensor attn_weights_129_cast = add(x = var_1991_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_129_cast")]; + tensor var_1996 = const()[name = tensor("op_1996"), val = tensor([20, 77, 77])]; + tensor input_341_cast = reshape(shape = var_1996, x = attn_weights_129_cast)[name = tensor("input_341_cast")]; + tensor input_343_cast = softmax(axis = var_5, x = input_341_cast)[name = tensor("input_343_cast")]; + tensor attn_output_127_transpose_x_0 = const()[name = tensor("attn_output_127_transpose_x_0"), val = tensor(false)]; + tensor attn_output_127_transpose_y_0 = const()[name = tensor("attn_output_127_transpose_y_0"), val = tensor(false)]; + tensor attn_output_127_cast = matmul(transpose_x = attn_output_127_transpose_x_0, transpose_y = attn_output_127_transpose_y_0, x = input_343_cast, y = value_states_87_cast)[name = tensor("attn_output_127_cast")]; + tensor var_2001 = const()[name = tensor("op_2001"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_129_cast = reshape(shape = var_2001, x = attn_output_127_cast)[name = tensor("attn_output_129_cast")]; + tensor attn_output_131_perm_0 = const()[name = tensor("attn_output_131_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2004 = const()[name = tensor("op_2004"), val = tensor([1, 77, 1280])]; + tensor transpose_51 = transpose(perm = attn_output_131_perm_0, x = attn_output_129_cast)[name = tensor("transpose_51")]; + tensor input_345_cast = reshape(shape = var_2004, x = transpose_51)[name = tensor("input_345_cast")]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(963011456)))]; + tensor text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(966288320)))]; + tensor hidden_states_129_cast = linear(bias = text_encoder_text_model_encoder_layers_21_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_self_attn_out_proj_weight_to_fp16, x = input_345_cast)[name = tensor("hidden_states_129_cast")]; + tensor input_347_cast = add(x = input_339_cast, y = hidden_states_129_cast)[name = tensor("input_347_cast")]; + tensor input_349_axes_0 = const()[name = tensor("input_349_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(966290944)))]; + tensor text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(966293568)))]; + tensor input_349_cast = layer_norm(axes = input_349_axes_0, beta = text_encoder_text_model_encoder_layers_21_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_21_layer_norm2_weight_to_fp16, x = input_347_cast)[name = tensor("input_349_cast")]; + tensor text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(966296192)))]; + tensor text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(979403456)))]; + tensor input_351_cast = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_mlp_fc1_weight_to_fp16, x = input_349_cast)[name = tensor("input_351_cast")]; + tensor input_353_mode_0 = const()[name = tensor("input_353_mode_0"), val = tensor("EXACT")]; + tensor input_353_cast = gelu(mode = input_353_mode_0, x = input_351_cast)[name = tensor("input_353_cast")]; + tensor text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(979413760)))]; + tensor text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992521024)))]; + tensor hidden_states_131_cast = linear(bias = text_encoder_text_model_encoder_layers_21_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_21_mlp_fc2_weight_to_fp16, x = input_353_cast)[name = tensor("hidden_states_131_cast")]; + tensor input_355_cast = add(x = input_347_cast, y = hidden_states_131_cast)[name = tensor("input_355_cast")]; + tensor hidden_states_133_axes_0 = const()[name = tensor("hidden_states_133_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992523648)))]; + tensor text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992526272)))]; + tensor hidden_states_133_cast = layer_norm(axes = hidden_states_133_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm1_weight_to_fp16, x = input_355_cast)[name = tensor("hidden_states_133_cast")]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992528896)))]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(995805760)))]; + tensor var_2042_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_q_proj_weight_to_fp16, x = hidden_states_133_cast)[name = tensor("op_2042_cast")]; + tensor var_2043_to_fp16 = const()[name = tensor("op_2043_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_137_cast = mul(x = var_2042_cast, y = var_2043_to_fp16)[name = tensor("tensor_137_cast")]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(995808384)))]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(999085248)))]; + tensor tensor_133_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_k_proj_weight_to_fp16, x = hidden_states_133_cast)[name = tensor("tensor_133_cast")]; + tensor var_2048 = const()[name = tensor("op_2048"), val = tensor([1, -1, 20, 64])]; + tensor var_2049_cast = reshape(shape = var_2048, x = tensor_133_cast)[name = tensor("op_2049_cast")]; + tensor var_2050_perm_0 = const()[name = tensor("op_2050_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(999087872)))]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002364736)))]; + tensor tensor_135_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_v_proj_weight_to_fp16, x = hidden_states_133_cast)[name = tensor("tensor_135_cast")]; + tensor var_2055 = const()[name = tensor("op_2055"), val = tensor([1, -1, 20, 64])]; + tensor var_2056_cast = reshape(shape = var_2055, x = tensor_135_cast)[name = tensor("op_2056_cast")]; + tensor var_2057_perm_0 = const()[name = tensor("op_2057_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2064 = const()[name = tensor("op_2064"), val = tensor([1, 77, 20, 64])]; + tensor var_2065_cast = reshape(shape = var_2064, x = tensor_137_cast)[name = tensor("op_2065_cast")]; + tensor var_2066_perm_0 = const()[name = tensor("op_2066_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2068 = const()[name = tensor("op_2068"), val = tensor([20, -1, 64])]; + tensor transpose_48 = transpose(perm = var_2066_perm_0, x = var_2065_cast)[name = tensor("transpose_48")]; + tensor query_states_45_cast = reshape(shape = var_2068, x = transpose_48)[name = tensor("query_states_45_cast")]; + tensor var_2070 = const()[name = tensor("op_2070"), val = tensor([20, -1, 64])]; + tensor transpose_50 = transpose(perm = var_2050_perm_0, x = var_2049_cast)[name = tensor("transpose_50")]; + tensor key_states_91_cast = reshape(shape = var_2070, x = transpose_50)[name = tensor("key_states_91_cast")]; + tensor var_2072 = const()[name = tensor("op_2072"), val = tensor([20, -1, 64])]; + tensor transpose_49 = transpose(perm = var_2057_perm_0, x = var_2056_cast)[name = tensor("transpose_49")]; + tensor value_states_91_cast = reshape(shape = var_2072, x = transpose_49)[name = tensor("value_states_91_cast")]; + tensor var_2075_perm_0 = const()[name = tensor("op_2075_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_133_transpose_x_0 = const()[name = tensor("attn_weights_133_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_133_transpose_y_0 = const()[name = tensor("attn_weights_133_transpose_y_0"), val = tensor(false)]; + tensor transpose_47 = transpose(perm = var_2075_perm_0, x = key_states_91_cast)[name = tensor("transpose_47")]; + tensor attn_weights_133_cast = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = query_states_45_cast, y = transpose_47)[name = tensor("attn_weights_133_cast")]; + tensor var_2077 = const()[name = tensor("op_2077"), val = tensor([1, 20, 77, 77])]; + tensor var_2078_cast = reshape(shape = var_2077, x = attn_weights_133_cast)[name = tensor("op_2078_cast")]; + tensor attn_weights_135_cast = add(x = var_2078_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_135_cast")]; + tensor var_2083 = const()[name = tensor("op_2083"), val = tensor([20, 77, 77])]; + tensor input_357_cast = reshape(shape = var_2083, x = attn_weights_135_cast)[name = tensor("input_357_cast")]; + tensor input_359_cast = softmax(axis = var_5, x = input_357_cast)[name = tensor("input_359_cast")]; + tensor attn_output_133_transpose_x_0 = const()[name = tensor("attn_output_133_transpose_x_0"), val = tensor(false)]; + tensor attn_output_133_transpose_y_0 = const()[name = tensor("attn_output_133_transpose_y_0"), val = tensor(false)]; + tensor attn_output_133_cast = matmul(transpose_x = attn_output_133_transpose_x_0, transpose_y = attn_output_133_transpose_y_0, x = input_359_cast, y = value_states_91_cast)[name = tensor("attn_output_133_cast")]; + tensor var_2088 = const()[name = tensor("op_2088"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_135_cast = reshape(shape = var_2088, x = attn_output_133_cast)[name = tensor("attn_output_135_cast")]; + tensor attn_output_137_perm_0 = const()[name = tensor("attn_output_137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2091 = const()[name = tensor("op_2091"), val = tensor([1, 77, 1280])]; + tensor transpose_46 = transpose(perm = attn_output_137_perm_0, x = attn_output_135_cast)[name = tensor("transpose_46")]; + tensor input_361_cast = reshape(shape = var_2091, x = transpose_46)[name = tensor("input_361_cast")]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1002367360)))]; + tensor text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1005644224)))]; + tensor hidden_states_135_cast = linear(bias = text_encoder_text_model_encoder_layers_22_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_self_attn_out_proj_weight_to_fp16, x = input_361_cast)[name = tensor("hidden_states_135_cast")]; + tensor input_363_cast = add(x = input_355_cast, y = hidden_states_135_cast)[name = tensor("input_363_cast")]; + tensor input_365_axes_0 = const()[name = tensor("input_365_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1005646848)))]; + tensor text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1005649472)))]; + tensor input_365_cast = layer_norm(axes = input_365_axes_0, beta = text_encoder_text_model_encoder_layers_22_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_22_layer_norm2_weight_to_fp16, x = input_363_cast)[name = tensor("input_365_cast")]; + tensor text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1005652096)))]; + tensor text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018759360)))]; + tensor input_367_cast = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_mlp_fc1_weight_to_fp16, x = input_365_cast)[name = tensor("input_367_cast")]; + tensor input_369_mode_0 = const()[name = tensor("input_369_mode_0"), val = tensor("EXACT")]; + tensor input_369_cast = gelu(mode = input_369_mode_0, x = input_367_cast)[name = tensor("input_369_cast")]; + tensor text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018769664)))]; + tensor text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031876928)))]; + tensor hidden_states_137_cast = linear(bias = text_encoder_text_model_encoder_layers_22_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_22_mlp_fc2_weight_to_fp16, x = input_369_cast)[name = tensor("hidden_states_137_cast")]; + tensor input_371_cast = add(x = input_363_cast, y = hidden_states_137_cast)[name = tensor("input_371_cast")]; + tensor hidden_states_139_axes_0 = const()[name = tensor("hidden_states_139_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_23_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031879552)))]; + tensor text_encoder_text_model_encoder_layers_23_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031882176)))]; + tensor hidden_states_139_cast = layer_norm(axes = hidden_states_139_axes_0, beta = text_encoder_text_model_encoder_layers_23_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_23_layer_norm1_weight_to_fp16, x = input_371_cast)[name = tensor("hidden_states_139_cast")]; + tensor text_encoder_text_model_encoder_layers_23_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031884800)))]; + tensor text_encoder_text_model_encoder_layers_23_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1035161664)))]; + tensor var_2129_cast = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_q_proj_weight_to_fp16, x = hidden_states_139_cast)[name = tensor("op_2129_cast")]; + tensor var_2130_to_fp16 = const()[name = tensor("op_2130_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_143_cast = mul(x = var_2129_cast, y = var_2130_to_fp16)[name = tensor("tensor_143_cast")]; + tensor text_encoder_text_model_encoder_layers_23_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1035164288)))]; + tensor text_encoder_text_model_encoder_layers_23_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1038441152)))]; + tensor tensor_139_cast = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_k_proj_weight_to_fp16, x = hidden_states_139_cast)[name = tensor("tensor_139_cast")]; + tensor var_2135 = const()[name = tensor("op_2135"), val = tensor([1, -1, 20, 64])]; + tensor var_2136_cast = reshape(shape = var_2135, x = tensor_139_cast)[name = tensor("op_2136_cast")]; + tensor var_2137_perm_0 = const()[name = tensor("op_2137_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_23_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1038443776)))]; + tensor text_encoder_text_model_encoder_layers_23_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1041720640)))]; + tensor tensor_141_cast = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_v_proj_weight_to_fp16, x = hidden_states_139_cast)[name = tensor("tensor_141_cast")]; + tensor var_2142 = const()[name = tensor("op_2142"), val = tensor([1, -1, 20, 64])]; + tensor var_2143_cast = reshape(shape = var_2142, x = tensor_141_cast)[name = tensor("op_2143_cast")]; + tensor var_2144_perm_0 = const()[name = tensor("op_2144_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2151 = const()[name = tensor("op_2151"), val = tensor([1, 77, 20, 64])]; + tensor var_2152_cast = reshape(shape = var_2151, x = tensor_143_cast)[name = tensor("op_2152_cast")]; + tensor var_2153_perm_0 = const()[name = tensor("op_2153_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2155 = const()[name = tensor("op_2155"), val = tensor([20, -1, 64])]; + tensor transpose_43 = transpose(perm = var_2153_perm_0, x = var_2152_cast)[name = tensor("transpose_43")]; + tensor query_states_47_cast = reshape(shape = var_2155, x = transpose_43)[name = tensor("query_states_47_cast")]; + tensor var_2157 = const()[name = tensor("op_2157"), val = tensor([20, -1, 64])]; + tensor transpose_45 = transpose(perm = var_2137_perm_0, x = var_2136_cast)[name = tensor("transpose_45")]; + tensor key_states_95_cast = reshape(shape = var_2157, x = transpose_45)[name = tensor("key_states_95_cast")]; + tensor var_2159 = const()[name = tensor("op_2159"), val = tensor([20, -1, 64])]; + tensor transpose_44 = transpose(perm = var_2144_perm_0, x = var_2143_cast)[name = tensor("transpose_44")]; + tensor value_states_95_cast = reshape(shape = var_2159, x = transpose_44)[name = tensor("value_states_95_cast")]; + tensor var_2162_perm_0 = const()[name = tensor("op_2162_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_139_transpose_x_0 = const()[name = tensor("attn_weights_139_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_139_transpose_y_0 = const()[name = tensor("attn_weights_139_transpose_y_0"), val = tensor(false)]; + tensor transpose_42 = transpose(perm = var_2162_perm_0, x = key_states_95_cast)[name = tensor("transpose_42")]; + tensor attn_weights_139_cast = matmul(transpose_x = attn_weights_139_transpose_x_0, transpose_y = attn_weights_139_transpose_y_0, x = query_states_47_cast, y = transpose_42)[name = tensor("attn_weights_139_cast")]; + tensor var_2164 = const()[name = tensor("op_2164"), val = tensor([1, 20, 77, 77])]; + tensor var_2165_cast = reshape(shape = var_2164, x = attn_weights_139_cast)[name = tensor("op_2165_cast")]; + tensor attn_weights_141_cast = add(x = var_2165_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_141_cast")]; + tensor var_2170 = const()[name = tensor("op_2170"), val = tensor([20, 77, 77])]; + tensor input_373_cast = reshape(shape = var_2170, x = attn_weights_141_cast)[name = tensor("input_373_cast")]; + tensor input_375_cast = softmax(axis = var_5, x = input_373_cast)[name = tensor("input_375_cast")]; + tensor attn_output_139_transpose_x_0 = const()[name = tensor("attn_output_139_transpose_x_0"), val = tensor(false)]; + tensor attn_output_139_transpose_y_0 = const()[name = tensor("attn_output_139_transpose_y_0"), val = tensor(false)]; + tensor attn_output_139_cast = matmul(transpose_x = attn_output_139_transpose_x_0, transpose_y = attn_output_139_transpose_y_0, x = input_375_cast, y = value_states_95_cast)[name = tensor("attn_output_139_cast")]; + tensor var_2175 = const()[name = tensor("op_2175"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_141_cast = reshape(shape = var_2175, x = attn_output_139_cast)[name = tensor("attn_output_141_cast")]; + tensor attn_output_143_perm_0 = const()[name = tensor("attn_output_143_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2178 = const()[name = tensor("op_2178"), val = tensor([1, 77, 1280])]; + tensor transpose_41 = transpose(perm = attn_output_143_perm_0, x = attn_output_141_cast)[name = tensor("transpose_41")]; + tensor input_377_cast = reshape(shape = var_2178, x = transpose_41)[name = tensor("input_377_cast")]; + tensor text_encoder_text_model_encoder_layers_23_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1041723264)))]; + tensor text_encoder_text_model_encoder_layers_23_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1045000128)))]; + tensor hidden_states_141_cast = linear(bias = text_encoder_text_model_encoder_layers_23_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_self_attn_out_proj_weight_to_fp16, x = input_377_cast)[name = tensor("hidden_states_141_cast")]; + tensor input_379_cast = add(x = input_371_cast, y = hidden_states_141_cast)[name = tensor("input_379_cast")]; + tensor input_381_axes_0 = const()[name = tensor("input_381_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_23_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1045002752)))]; + tensor text_encoder_text_model_encoder_layers_23_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1045005376)))]; + tensor input_381_cast = layer_norm(axes = input_381_axes_0, beta = text_encoder_text_model_encoder_layers_23_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_23_layer_norm2_weight_to_fp16, x = input_379_cast)[name = tensor("input_381_cast")]; + tensor text_encoder_text_model_encoder_layers_23_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1045008000)))]; + tensor text_encoder_text_model_encoder_layers_23_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1058115264)))]; + tensor input_383_cast = linear(bias = text_encoder_text_model_encoder_layers_23_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_mlp_fc1_weight_to_fp16, x = input_381_cast)[name = tensor("input_383_cast")]; + tensor input_385_mode_0 = const()[name = tensor("input_385_mode_0"), val = tensor("EXACT")]; + tensor input_385_cast = gelu(mode = input_385_mode_0, x = input_383_cast)[name = tensor("input_385_cast")]; + tensor text_encoder_text_model_encoder_layers_23_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1058125568)))]; + tensor text_encoder_text_model_encoder_layers_23_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_23_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1071232832)))]; + tensor hidden_states_143_cast = linear(bias = text_encoder_text_model_encoder_layers_23_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_23_mlp_fc2_weight_to_fp16, x = input_385_cast)[name = tensor("hidden_states_143_cast")]; + tensor input_387_cast = add(x = input_379_cast, y = hidden_states_143_cast)[name = tensor("input_387_cast")]; + tensor hidden_states_145_axes_0 = const()[name = tensor("hidden_states_145_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_24_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1071235456)))]; + tensor text_encoder_text_model_encoder_layers_24_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1071238080)))]; + tensor hidden_states_145_cast = layer_norm(axes = hidden_states_145_axes_0, beta = text_encoder_text_model_encoder_layers_24_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_24_layer_norm1_weight_to_fp16, x = input_387_cast)[name = tensor("hidden_states_145_cast")]; + tensor text_encoder_text_model_encoder_layers_24_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1071240704)))]; + tensor text_encoder_text_model_encoder_layers_24_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1074517568)))]; + tensor var_2216_cast = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_q_proj_weight_to_fp16, x = hidden_states_145_cast)[name = tensor("op_2216_cast")]; + tensor var_2217_to_fp16 = const()[name = tensor("op_2217_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_149_cast = mul(x = var_2216_cast, y = var_2217_to_fp16)[name = tensor("tensor_149_cast")]; + tensor text_encoder_text_model_encoder_layers_24_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1074520192)))]; + tensor text_encoder_text_model_encoder_layers_24_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1077797056)))]; + tensor tensor_145_cast = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_k_proj_weight_to_fp16, x = hidden_states_145_cast)[name = tensor("tensor_145_cast")]; + tensor var_2222 = const()[name = tensor("op_2222"), val = tensor([1, -1, 20, 64])]; + tensor var_2223_cast = reshape(shape = var_2222, x = tensor_145_cast)[name = tensor("op_2223_cast")]; + tensor var_2224_perm_0 = const()[name = tensor("op_2224_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_24_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1077799680)))]; + tensor text_encoder_text_model_encoder_layers_24_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1081076544)))]; + tensor tensor_147_cast = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_v_proj_weight_to_fp16, x = hidden_states_145_cast)[name = tensor("tensor_147_cast")]; + tensor var_2229 = const()[name = tensor("op_2229"), val = tensor([1, -1, 20, 64])]; + tensor var_2230_cast = reshape(shape = var_2229, x = tensor_147_cast)[name = tensor("op_2230_cast")]; + tensor var_2231_perm_0 = const()[name = tensor("op_2231_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2238 = const()[name = tensor("op_2238"), val = tensor([1, 77, 20, 64])]; + tensor var_2239_cast = reshape(shape = var_2238, x = tensor_149_cast)[name = tensor("op_2239_cast")]; + tensor var_2240_perm_0 = const()[name = tensor("op_2240_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2242 = const()[name = tensor("op_2242"), val = tensor([20, -1, 64])]; + tensor transpose_38 = transpose(perm = var_2240_perm_0, x = var_2239_cast)[name = tensor("transpose_38")]; + tensor query_states_49_cast = reshape(shape = var_2242, x = transpose_38)[name = tensor("query_states_49_cast")]; + tensor var_2244 = const()[name = tensor("op_2244"), val = tensor([20, -1, 64])]; + tensor transpose_40 = transpose(perm = var_2224_perm_0, x = var_2223_cast)[name = tensor("transpose_40")]; + tensor key_states_99_cast = reshape(shape = var_2244, x = transpose_40)[name = tensor("key_states_99_cast")]; + tensor var_2246 = const()[name = tensor("op_2246"), val = tensor([20, -1, 64])]; + tensor transpose_39 = transpose(perm = var_2231_perm_0, x = var_2230_cast)[name = tensor("transpose_39")]; + tensor value_states_99_cast = reshape(shape = var_2246, x = transpose_39)[name = tensor("value_states_99_cast")]; + tensor var_2249_perm_0 = const()[name = tensor("op_2249_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_145_transpose_x_0 = const()[name = tensor("attn_weights_145_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_145_transpose_y_0 = const()[name = tensor("attn_weights_145_transpose_y_0"), val = tensor(false)]; + tensor transpose_37 = transpose(perm = var_2249_perm_0, x = key_states_99_cast)[name = tensor("transpose_37")]; + tensor attn_weights_145_cast = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = query_states_49_cast, y = transpose_37)[name = tensor("attn_weights_145_cast")]; + tensor var_2251 = const()[name = tensor("op_2251"), val = tensor([1, 20, 77, 77])]; + tensor var_2252_cast = reshape(shape = var_2251, x = attn_weights_145_cast)[name = tensor("op_2252_cast")]; + tensor attn_weights_147_cast = add(x = var_2252_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_147_cast")]; + tensor var_2257 = const()[name = tensor("op_2257"), val = tensor([20, 77, 77])]; + tensor input_389_cast = reshape(shape = var_2257, x = attn_weights_147_cast)[name = tensor("input_389_cast")]; + tensor input_391_cast = softmax(axis = var_5, x = input_389_cast)[name = tensor("input_391_cast")]; + tensor attn_output_145_transpose_x_0 = const()[name = tensor("attn_output_145_transpose_x_0"), val = tensor(false)]; + tensor attn_output_145_transpose_y_0 = const()[name = tensor("attn_output_145_transpose_y_0"), val = tensor(false)]; + tensor attn_output_145_cast = matmul(transpose_x = attn_output_145_transpose_x_0, transpose_y = attn_output_145_transpose_y_0, x = input_391_cast, y = value_states_99_cast)[name = tensor("attn_output_145_cast")]; + tensor var_2262 = const()[name = tensor("op_2262"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_147_cast = reshape(shape = var_2262, x = attn_output_145_cast)[name = tensor("attn_output_147_cast")]; + tensor attn_output_149_perm_0 = const()[name = tensor("attn_output_149_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2265 = const()[name = tensor("op_2265"), val = tensor([1, 77, 1280])]; + tensor transpose_36 = transpose(perm = attn_output_149_perm_0, x = attn_output_147_cast)[name = tensor("transpose_36")]; + tensor input_393_cast = reshape(shape = var_2265, x = transpose_36)[name = tensor("input_393_cast")]; + tensor text_encoder_text_model_encoder_layers_24_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1081079168)))]; + tensor text_encoder_text_model_encoder_layers_24_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1084356032)))]; + tensor hidden_states_147_cast = linear(bias = text_encoder_text_model_encoder_layers_24_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_self_attn_out_proj_weight_to_fp16, x = input_393_cast)[name = tensor("hidden_states_147_cast")]; + tensor input_395_cast = add(x = input_387_cast, y = hidden_states_147_cast)[name = tensor("input_395_cast")]; + tensor input_397_axes_0 = const()[name = tensor("input_397_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_24_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1084358656)))]; + tensor text_encoder_text_model_encoder_layers_24_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1084361280)))]; + tensor input_397_cast = layer_norm(axes = input_397_axes_0, beta = text_encoder_text_model_encoder_layers_24_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_24_layer_norm2_weight_to_fp16, x = input_395_cast)[name = tensor("input_397_cast")]; + tensor text_encoder_text_model_encoder_layers_24_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1084363904)))]; + tensor text_encoder_text_model_encoder_layers_24_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1097471168)))]; + tensor input_399_cast = linear(bias = text_encoder_text_model_encoder_layers_24_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_mlp_fc1_weight_to_fp16, x = input_397_cast)[name = tensor("input_399_cast")]; + tensor input_401_mode_0 = const()[name = tensor("input_401_mode_0"), val = tensor("EXACT")]; + tensor input_401_cast = gelu(mode = input_401_mode_0, x = input_399_cast)[name = tensor("input_401_cast")]; + tensor text_encoder_text_model_encoder_layers_24_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1097481472)))]; + tensor text_encoder_text_model_encoder_layers_24_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_24_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1110588736)))]; + tensor hidden_states_149_cast = linear(bias = text_encoder_text_model_encoder_layers_24_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_24_mlp_fc2_weight_to_fp16, x = input_401_cast)[name = tensor("hidden_states_149_cast")]; + tensor input_403_cast = add(x = input_395_cast, y = hidden_states_149_cast)[name = tensor("input_403_cast")]; + tensor hidden_states_151_axes_0 = const()[name = tensor("hidden_states_151_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_25_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1110591360)))]; + tensor text_encoder_text_model_encoder_layers_25_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1110593984)))]; + tensor hidden_states_151_cast = layer_norm(axes = hidden_states_151_axes_0, beta = text_encoder_text_model_encoder_layers_25_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_25_layer_norm1_weight_to_fp16, x = input_403_cast)[name = tensor("hidden_states_151_cast")]; + tensor text_encoder_text_model_encoder_layers_25_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1110596608)))]; + tensor text_encoder_text_model_encoder_layers_25_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113873472)))]; + tensor var_2303_cast = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_q_proj_weight_to_fp16, x = hidden_states_151_cast)[name = tensor("op_2303_cast")]; + tensor var_2304_to_fp16 = const()[name = tensor("op_2304_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_155_cast = mul(x = var_2303_cast, y = var_2304_to_fp16)[name = tensor("tensor_155_cast")]; + tensor text_encoder_text_model_encoder_layers_25_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1113876096)))]; + tensor text_encoder_text_model_encoder_layers_25_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1117152960)))]; + tensor tensor_151_cast = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_k_proj_weight_to_fp16, x = hidden_states_151_cast)[name = tensor("tensor_151_cast")]; + tensor var_2309 = const()[name = tensor("op_2309"), val = tensor([1, -1, 20, 64])]; + tensor var_2310_cast = reshape(shape = var_2309, x = tensor_151_cast)[name = tensor("op_2310_cast")]; + tensor var_2311_perm_0 = const()[name = tensor("op_2311_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_25_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1117155584)))]; + tensor text_encoder_text_model_encoder_layers_25_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1120432448)))]; + tensor tensor_153_cast = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_v_proj_weight_to_fp16, x = hidden_states_151_cast)[name = tensor("tensor_153_cast")]; + tensor var_2316 = const()[name = tensor("op_2316"), val = tensor([1, -1, 20, 64])]; + tensor var_2317_cast = reshape(shape = var_2316, x = tensor_153_cast)[name = tensor("op_2317_cast")]; + tensor var_2318_perm_0 = const()[name = tensor("op_2318_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2325 = const()[name = tensor("op_2325"), val = tensor([1, 77, 20, 64])]; + tensor var_2326_cast = reshape(shape = var_2325, x = tensor_155_cast)[name = tensor("op_2326_cast")]; + tensor var_2327_perm_0 = const()[name = tensor("op_2327_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2329 = const()[name = tensor("op_2329"), val = tensor([20, -1, 64])]; + tensor transpose_33 = transpose(perm = var_2327_perm_0, x = var_2326_cast)[name = tensor("transpose_33")]; + tensor query_states_51_cast = reshape(shape = var_2329, x = transpose_33)[name = tensor("query_states_51_cast")]; + tensor var_2331 = const()[name = tensor("op_2331"), val = tensor([20, -1, 64])]; + tensor transpose_35 = transpose(perm = var_2311_perm_0, x = var_2310_cast)[name = tensor("transpose_35")]; + tensor key_states_103_cast = reshape(shape = var_2331, x = transpose_35)[name = tensor("key_states_103_cast")]; + tensor var_2333 = const()[name = tensor("op_2333"), val = tensor([20, -1, 64])]; + tensor transpose_34 = transpose(perm = var_2318_perm_0, x = var_2317_cast)[name = tensor("transpose_34")]; + tensor value_states_103_cast = reshape(shape = var_2333, x = transpose_34)[name = tensor("value_states_103_cast")]; + tensor var_2336_perm_0 = const()[name = tensor("op_2336_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_151_transpose_x_0 = const()[name = tensor("attn_weights_151_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_151_transpose_y_0 = const()[name = tensor("attn_weights_151_transpose_y_0"), val = tensor(false)]; + tensor transpose_32 = transpose(perm = var_2336_perm_0, x = key_states_103_cast)[name = tensor("transpose_32")]; + tensor attn_weights_151_cast = matmul(transpose_x = attn_weights_151_transpose_x_0, transpose_y = attn_weights_151_transpose_y_0, x = query_states_51_cast, y = transpose_32)[name = tensor("attn_weights_151_cast")]; + tensor var_2338 = const()[name = tensor("op_2338"), val = tensor([1, 20, 77, 77])]; + tensor var_2339_cast = reshape(shape = var_2338, x = attn_weights_151_cast)[name = tensor("op_2339_cast")]; + tensor attn_weights_153_cast = add(x = var_2339_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_153_cast")]; + tensor var_2344 = const()[name = tensor("op_2344"), val = tensor([20, 77, 77])]; + tensor input_405_cast = reshape(shape = var_2344, x = attn_weights_153_cast)[name = tensor("input_405_cast")]; + tensor input_407_cast = softmax(axis = var_5, x = input_405_cast)[name = tensor("input_407_cast")]; + tensor attn_output_151_transpose_x_0 = const()[name = tensor("attn_output_151_transpose_x_0"), val = tensor(false)]; + tensor attn_output_151_transpose_y_0 = const()[name = tensor("attn_output_151_transpose_y_0"), val = tensor(false)]; + tensor attn_output_151_cast = matmul(transpose_x = attn_output_151_transpose_x_0, transpose_y = attn_output_151_transpose_y_0, x = input_407_cast, y = value_states_103_cast)[name = tensor("attn_output_151_cast")]; + tensor var_2349 = const()[name = tensor("op_2349"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_153_cast = reshape(shape = var_2349, x = attn_output_151_cast)[name = tensor("attn_output_153_cast")]; + tensor attn_output_155_perm_0 = const()[name = tensor("attn_output_155_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2352 = const()[name = tensor("op_2352"), val = tensor([1, 77, 1280])]; + tensor transpose_31 = transpose(perm = attn_output_155_perm_0, x = attn_output_153_cast)[name = tensor("transpose_31")]; + tensor input_409_cast = reshape(shape = var_2352, x = transpose_31)[name = tensor("input_409_cast")]; + tensor text_encoder_text_model_encoder_layers_25_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1120435072)))]; + tensor text_encoder_text_model_encoder_layers_25_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1123711936)))]; + tensor hidden_states_153_cast = linear(bias = text_encoder_text_model_encoder_layers_25_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_self_attn_out_proj_weight_to_fp16, x = input_409_cast)[name = tensor("hidden_states_153_cast")]; + tensor input_411_cast = add(x = input_403_cast, y = hidden_states_153_cast)[name = tensor("input_411_cast")]; + tensor input_413_axes_0 = const()[name = tensor("input_413_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_25_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1123714560)))]; + tensor text_encoder_text_model_encoder_layers_25_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1123717184)))]; + tensor input_413_cast = layer_norm(axes = input_413_axes_0, beta = text_encoder_text_model_encoder_layers_25_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_25_layer_norm2_weight_to_fp16, x = input_411_cast)[name = tensor("input_413_cast")]; + tensor text_encoder_text_model_encoder_layers_25_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1123719808)))]; + tensor text_encoder_text_model_encoder_layers_25_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1136827072)))]; + tensor input_415_cast = linear(bias = text_encoder_text_model_encoder_layers_25_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_mlp_fc1_weight_to_fp16, x = input_413_cast)[name = tensor("input_415_cast")]; + tensor input_417_mode_0 = const()[name = tensor("input_417_mode_0"), val = tensor("EXACT")]; + tensor input_417_cast = gelu(mode = input_417_mode_0, x = input_415_cast)[name = tensor("input_417_cast")]; + tensor text_encoder_text_model_encoder_layers_25_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1136837376)))]; + tensor text_encoder_text_model_encoder_layers_25_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_25_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149944640)))]; + tensor hidden_states_155_cast = linear(bias = text_encoder_text_model_encoder_layers_25_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_25_mlp_fc2_weight_to_fp16, x = input_417_cast)[name = tensor("hidden_states_155_cast")]; + tensor input_419_cast = add(x = input_411_cast, y = hidden_states_155_cast)[name = tensor("input_419_cast")]; + tensor hidden_states_157_axes_0 = const()[name = tensor("hidden_states_157_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_26_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149947264)))]; + tensor text_encoder_text_model_encoder_layers_26_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149949888)))]; + tensor hidden_states_157_cast = layer_norm(axes = hidden_states_157_axes_0, beta = text_encoder_text_model_encoder_layers_26_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_26_layer_norm1_weight_to_fp16, x = input_419_cast)[name = tensor("hidden_states_157_cast")]; + tensor text_encoder_text_model_encoder_layers_26_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1149952512)))]; + tensor text_encoder_text_model_encoder_layers_26_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153229376)))]; + tensor var_2390_cast = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_q_proj_weight_to_fp16, x = hidden_states_157_cast)[name = tensor("op_2390_cast")]; + tensor var_2391_to_fp16 = const()[name = tensor("op_2391_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_161_cast = mul(x = var_2390_cast, y = var_2391_to_fp16)[name = tensor("tensor_161_cast")]; + tensor text_encoder_text_model_encoder_layers_26_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1153232000)))]; + tensor text_encoder_text_model_encoder_layers_26_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1156508864)))]; + tensor tensor_157_cast = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_k_proj_weight_to_fp16, x = hidden_states_157_cast)[name = tensor("tensor_157_cast")]; + tensor var_2396 = const()[name = tensor("op_2396"), val = tensor([1, -1, 20, 64])]; + tensor var_2397_cast = reshape(shape = var_2396, x = tensor_157_cast)[name = tensor("op_2397_cast")]; + tensor var_2398_perm_0 = const()[name = tensor("op_2398_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_26_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1156511488)))]; + tensor text_encoder_text_model_encoder_layers_26_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1159788352)))]; + tensor tensor_159_cast = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_v_proj_weight_to_fp16, x = hidden_states_157_cast)[name = tensor("tensor_159_cast")]; + tensor var_2403 = const()[name = tensor("op_2403"), val = tensor([1, -1, 20, 64])]; + tensor var_2404_cast = reshape(shape = var_2403, x = tensor_159_cast)[name = tensor("op_2404_cast")]; + tensor var_2405_perm_0 = const()[name = tensor("op_2405_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2412 = const()[name = tensor("op_2412"), val = tensor([1, 77, 20, 64])]; + tensor var_2413_cast = reshape(shape = var_2412, x = tensor_161_cast)[name = tensor("op_2413_cast")]; + tensor var_2414_perm_0 = const()[name = tensor("op_2414_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2416 = const()[name = tensor("op_2416"), val = tensor([20, -1, 64])]; + tensor transpose_28 = transpose(perm = var_2414_perm_0, x = var_2413_cast)[name = tensor("transpose_28")]; + tensor query_states_53_cast = reshape(shape = var_2416, x = transpose_28)[name = tensor("query_states_53_cast")]; + tensor var_2418 = const()[name = tensor("op_2418"), val = tensor([20, -1, 64])]; + tensor transpose_30 = transpose(perm = var_2398_perm_0, x = var_2397_cast)[name = tensor("transpose_30")]; + tensor key_states_107_cast = reshape(shape = var_2418, x = transpose_30)[name = tensor("key_states_107_cast")]; + tensor var_2420 = const()[name = tensor("op_2420"), val = tensor([20, -1, 64])]; + tensor transpose_29 = transpose(perm = var_2405_perm_0, x = var_2404_cast)[name = tensor("transpose_29")]; + tensor value_states_107_cast = reshape(shape = var_2420, x = transpose_29)[name = tensor("value_states_107_cast")]; + tensor var_2423_perm_0 = const()[name = tensor("op_2423_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_157_transpose_x_0 = const()[name = tensor("attn_weights_157_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_157_transpose_y_0 = const()[name = tensor("attn_weights_157_transpose_y_0"), val = tensor(false)]; + tensor transpose_27 = transpose(perm = var_2423_perm_0, x = key_states_107_cast)[name = tensor("transpose_27")]; + tensor attn_weights_157_cast = matmul(transpose_x = attn_weights_157_transpose_x_0, transpose_y = attn_weights_157_transpose_y_0, x = query_states_53_cast, y = transpose_27)[name = tensor("attn_weights_157_cast")]; + tensor var_2425 = const()[name = tensor("op_2425"), val = tensor([1, 20, 77, 77])]; + tensor var_2426_cast = reshape(shape = var_2425, x = attn_weights_157_cast)[name = tensor("op_2426_cast")]; + tensor attn_weights_159_cast = add(x = var_2426_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_159_cast")]; + tensor var_2431 = const()[name = tensor("op_2431"), val = tensor([20, 77, 77])]; + tensor input_421_cast = reshape(shape = var_2431, x = attn_weights_159_cast)[name = tensor("input_421_cast")]; + tensor input_423_cast = softmax(axis = var_5, x = input_421_cast)[name = tensor("input_423_cast")]; + tensor attn_output_157_transpose_x_0 = const()[name = tensor("attn_output_157_transpose_x_0"), val = tensor(false)]; + tensor attn_output_157_transpose_y_0 = const()[name = tensor("attn_output_157_transpose_y_0"), val = tensor(false)]; + tensor attn_output_157_cast = matmul(transpose_x = attn_output_157_transpose_x_0, transpose_y = attn_output_157_transpose_y_0, x = input_423_cast, y = value_states_107_cast)[name = tensor("attn_output_157_cast")]; + tensor var_2436 = const()[name = tensor("op_2436"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_159_cast = reshape(shape = var_2436, x = attn_output_157_cast)[name = tensor("attn_output_159_cast")]; + tensor attn_output_161_perm_0 = const()[name = tensor("attn_output_161_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2439 = const()[name = tensor("op_2439"), val = tensor([1, 77, 1280])]; + tensor transpose_26 = transpose(perm = attn_output_161_perm_0, x = attn_output_159_cast)[name = tensor("transpose_26")]; + tensor input_425_cast = reshape(shape = var_2439, x = transpose_26)[name = tensor("input_425_cast")]; + tensor text_encoder_text_model_encoder_layers_26_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1159790976)))]; + tensor text_encoder_text_model_encoder_layers_26_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1163067840)))]; + tensor hidden_states_159_cast = linear(bias = text_encoder_text_model_encoder_layers_26_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_self_attn_out_proj_weight_to_fp16, x = input_425_cast)[name = tensor("hidden_states_159_cast")]; + tensor input_427_cast = add(x = input_419_cast, y = hidden_states_159_cast)[name = tensor("input_427_cast")]; + tensor input_429_axes_0 = const()[name = tensor("input_429_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_26_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1163070464)))]; + tensor text_encoder_text_model_encoder_layers_26_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1163073088)))]; + tensor input_429_cast = layer_norm(axes = input_429_axes_0, beta = text_encoder_text_model_encoder_layers_26_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_26_layer_norm2_weight_to_fp16, x = input_427_cast)[name = tensor("input_429_cast")]; + tensor text_encoder_text_model_encoder_layers_26_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1163075712)))]; + tensor text_encoder_text_model_encoder_layers_26_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1176182976)))]; + tensor input_431_cast = linear(bias = text_encoder_text_model_encoder_layers_26_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_mlp_fc1_weight_to_fp16, x = input_429_cast)[name = tensor("input_431_cast")]; + tensor input_433_mode_0 = const()[name = tensor("input_433_mode_0"), val = tensor("EXACT")]; + tensor input_433_cast = gelu(mode = input_433_mode_0, x = input_431_cast)[name = tensor("input_433_cast")]; + tensor text_encoder_text_model_encoder_layers_26_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1176193280)))]; + tensor text_encoder_text_model_encoder_layers_26_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_26_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1189300544)))]; + tensor hidden_states_161_cast = linear(bias = text_encoder_text_model_encoder_layers_26_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_26_mlp_fc2_weight_to_fp16, x = input_433_cast)[name = tensor("hidden_states_161_cast")]; + tensor input_435_cast = add(x = input_427_cast, y = hidden_states_161_cast)[name = tensor("input_435_cast")]; + tensor hidden_states_163_axes_0 = const()[name = tensor("hidden_states_163_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_27_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1189303168)))]; + tensor text_encoder_text_model_encoder_layers_27_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1189305792)))]; + tensor hidden_states_163_cast = layer_norm(axes = hidden_states_163_axes_0, beta = text_encoder_text_model_encoder_layers_27_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_27_layer_norm1_weight_to_fp16, x = input_435_cast)[name = tensor("hidden_states_163_cast")]; + tensor text_encoder_text_model_encoder_layers_27_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1189308416)))]; + tensor text_encoder_text_model_encoder_layers_27_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1192585280)))]; + tensor var_2477_cast = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_q_proj_weight_to_fp16, x = hidden_states_163_cast)[name = tensor("op_2477_cast")]; + tensor var_2478_to_fp16 = const()[name = tensor("op_2478_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_167_cast = mul(x = var_2477_cast, y = var_2478_to_fp16)[name = tensor("tensor_167_cast")]; + tensor text_encoder_text_model_encoder_layers_27_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1192587904)))]; + tensor text_encoder_text_model_encoder_layers_27_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1195864768)))]; + tensor tensor_163_cast = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_k_proj_weight_to_fp16, x = hidden_states_163_cast)[name = tensor("tensor_163_cast")]; + tensor var_2483 = const()[name = tensor("op_2483"), val = tensor([1, -1, 20, 64])]; + tensor var_2484_cast = reshape(shape = var_2483, x = tensor_163_cast)[name = tensor("op_2484_cast")]; + tensor var_2485_perm_0 = const()[name = tensor("op_2485_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_27_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1195867392)))]; + tensor text_encoder_text_model_encoder_layers_27_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1199144256)))]; + tensor tensor_165_cast = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_v_proj_weight_to_fp16, x = hidden_states_163_cast)[name = tensor("tensor_165_cast")]; + tensor var_2490 = const()[name = tensor("op_2490"), val = tensor([1, -1, 20, 64])]; + tensor var_2491_cast = reshape(shape = var_2490, x = tensor_165_cast)[name = tensor("op_2491_cast")]; + tensor var_2492_perm_0 = const()[name = tensor("op_2492_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2499 = const()[name = tensor("op_2499"), val = tensor([1, 77, 20, 64])]; + tensor var_2500_cast = reshape(shape = var_2499, x = tensor_167_cast)[name = tensor("op_2500_cast")]; + tensor var_2501_perm_0 = const()[name = tensor("op_2501_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2503 = const()[name = tensor("op_2503"), val = tensor([20, -1, 64])]; + tensor transpose_23 = transpose(perm = var_2501_perm_0, x = var_2500_cast)[name = tensor("transpose_23")]; + tensor query_states_55_cast = reshape(shape = var_2503, x = transpose_23)[name = tensor("query_states_55_cast")]; + tensor var_2505 = const()[name = tensor("op_2505"), val = tensor([20, -1, 64])]; + tensor transpose_25 = transpose(perm = var_2485_perm_0, x = var_2484_cast)[name = tensor("transpose_25")]; + tensor key_states_111_cast = reshape(shape = var_2505, x = transpose_25)[name = tensor("key_states_111_cast")]; + tensor var_2507 = const()[name = tensor("op_2507"), val = tensor([20, -1, 64])]; + tensor transpose_24 = transpose(perm = var_2492_perm_0, x = var_2491_cast)[name = tensor("transpose_24")]; + tensor value_states_111_cast = reshape(shape = var_2507, x = transpose_24)[name = tensor("value_states_111_cast")]; + tensor var_2510_perm_0 = const()[name = tensor("op_2510_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_163_transpose_x_0 = const()[name = tensor("attn_weights_163_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_163_transpose_y_0 = const()[name = tensor("attn_weights_163_transpose_y_0"), val = tensor(false)]; + tensor transpose_22 = transpose(perm = var_2510_perm_0, x = key_states_111_cast)[name = tensor("transpose_22")]; + tensor attn_weights_163_cast = matmul(transpose_x = attn_weights_163_transpose_x_0, transpose_y = attn_weights_163_transpose_y_0, x = query_states_55_cast, y = transpose_22)[name = tensor("attn_weights_163_cast")]; + tensor var_2512 = const()[name = tensor("op_2512"), val = tensor([1, 20, 77, 77])]; + tensor var_2513_cast = reshape(shape = var_2512, x = attn_weights_163_cast)[name = tensor("op_2513_cast")]; + tensor attn_weights_165_cast = add(x = var_2513_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_165_cast")]; + tensor var_2518 = const()[name = tensor("op_2518"), val = tensor([20, 77, 77])]; + tensor input_437_cast = reshape(shape = var_2518, x = attn_weights_165_cast)[name = tensor("input_437_cast")]; + tensor input_439_cast = softmax(axis = var_5, x = input_437_cast)[name = tensor("input_439_cast")]; + tensor attn_output_163_transpose_x_0 = const()[name = tensor("attn_output_163_transpose_x_0"), val = tensor(false)]; + tensor attn_output_163_transpose_y_0 = const()[name = tensor("attn_output_163_transpose_y_0"), val = tensor(false)]; + tensor attn_output_163_cast = matmul(transpose_x = attn_output_163_transpose_x_0, transpose_y = attn_output_163_transpose_y_0, x = input_439_cast, y = value_states_111_cast)[name = tensor("attn_output_163_cast")]; + tensor var_2523 = const()[name = tensor("op_2523"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_165_cast = reshape(shape = var_2523, x = attn_output_163_cast)[name = tensor("attn_output_165_cast")]; + tensor attn_output_167_perm_0 = const()[name = tensor("attn_output_167_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2526 = const()[name = tensor("op_2526"), val = tensor([1, 77, 1280])]; + tensor transpose_21 = transpose(perm = attn_output_167_perm_0, x = attn_output_165_cast)[name = tensor("transpose_21")]; + tensor input_441_cast = reshape(shape = var_2526, x = transpose_21)[name = tensor("input_441_cast")]; + tensor text_encoder_text_model_encoder_layers_27_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1199146880)))]; + tensor text_encoder_text_model_encoder_layers_27_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1202423744)))]; + tensor hidden_states_165_cast = linear(bias = text_encoder_text_model_encoder_layers_27_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_self_attn_out_proj_weight_to_fp16, x = input_441_cast)[name = tensor("hidden_states_165_cast")]; + tensor input_443_cast = add(x = input_435_cast, y = hidden_states_165_cast)[name = tensor("input_443_cast")]; + tensor input_445_axes_0 = const()[name = tensor("input_445_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_27_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1202426368)))]; + tensor text_encoder_text_model_encoder_layers_27_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1202428992)))]; + tensor input_445_cast = layer_norm(axes = input_445_axes_0, beta = text_encoder_text_model_encoder_layers_27_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_27_layer_norm2_weight_to_fp16, x = input_443_cast)[name = tensor("input_445_cast")]; + tensor text_encoder_text_model_encoder_layers_27_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1202431616)))]; + tensor text_encoder_text_model_encoder_layers_27_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1215538880)))]; + tensor input_447_cast = linear(bias = text_encoder_text_model_encoder_layers_27_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_mlp_fc1_weight_to_fp16, x = input_445_cast)[name = tensor("input_447_cast")]; + tensor input_449_mode_0 = const()[name = tensor("input_449_mode_0"), val = tensor("EXACT")]; + tensor input_449_cast = gelu(mode = input_449_mode_0, x = input_447_cast)[name = tensor("input_449_cast")]; + tensor text_encoder_text_model_encoder_layers_27_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1215549184)))]; + tensor text_encoder_text_model_encoder_layers_27_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_27_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1228656448)))]; + tensor hidden_states_167_cast = linear(bias = text_encoder_text_model_encoder_layers_27_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_27_mlp_fc2_weight_to_fp16, x = input_449_cast)[name = tensor("hidden_states_167_cast")]; + tensor input_451_cast = add(x = input_443_cast, y = hidden_states_167_cast)[name = tensor("input_451_cast")]; + tensor hidden_states_169_axes_0 = const()[name = tensor("hidden_states_169_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_28_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1228659072)))]; + tensor text_encoder_text_model_encoder_layers_28_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1228661696)))]; + tensor hidden_states_169_cast = layer_norm(axes = hidden_states_169_axes_0, beta = text_encoder_text_model_encoder_layers_28_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_28_layer_norm1_weight_to_fp16, x = input_451_cast)[name = tensor("hidden_states_169_cast")]; + tensor text_encoder_text_model_encoder_layers_28_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1228664320)))]; + tensor text_encoder_text_model_encoder_layers_28_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1231941184)))]; + tensor var_2564_cast = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_q_proj_weight_to_fp16, x = hidden_states_169_cast)[name = tensor("op_2564_cast")]; + tensor var_2565_to_fp16 = const()[name = tensor("op_2565_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_173_cast = mul(x = var_2564_cast, y = var_2565_to_fp16)[name = tensor("tensor_173_cast")]; + tensor text_encoder_text_model_encoder_layers_28_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1231943808)))]; + tensor text_encoder_text_model_encoder_layers_28_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1235220672)))]; + tensor tensor_169_cast = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_k_proj_weight_to_fp16, x = hidden_states_169_cast)[name = tensor("tensor_169_cast")]; + tensor var_2570 = const()[name = tensor("op_2570"), val = tensor([1, -1, 20, 64])]; + tensor var_2571_cast = reshape(shape = var_2570, x = tensor_169_cast)[name = tensor("op_2571_cast")]; + tensor var_2572_perm_0 = const()[name = tensor("op_2572_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_28_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1235223296)))]; + tensor text_encoder_text_model_encoder_layers_28_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1238500160)))]; + tensor tensor_171_cast = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_v_proj_weight_to_fp16, x = hidden_states_169_cast)[name = tensor("tensor_171_cast")]; + tensor var_2577 = const()[name = tensor("op_2577"), val = tensor([1, -1, 20, 64])]; + tensor var_2578_cast = reshape(shape = var_2577, x = tensor_171_cast)[name = tensor("op_2578_cast")]; + tensor var_2579_perm_0 = const()[name = tensor("op_2579_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2586 = const()[name = tensor("op_2586"), val = tensor([1, 77, 20, 64])]; + tensor var_2587_cast = reshape(shape = var_2586, x = tensor_173_cast)[name = tensor("op_2587_cast")]; + tensor var_2588_perm_0 = const()[name = tensor("op_2588_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2590 = const()[name = tensor("op_2590"), val = tensor([20, -1, 64])]; + tensor transpose_18 = transpose(perm = var_2588_perm_0, x = var_2587_cast)[name = tensor("transpose_18")]; + tensor query_states_57_cast = reshape(shape = var_2590, x = transpose_18)[name = tensor("query_states_57_cast")]; + tensor var_2592 = const()[name = tensor("op_2592"), val = tensor([20, -1, 64])]; + tensor transpose_20 = transpose(perm = var_2572_perm_0, x = var_2571_cast)[name = tensor("transpose_20")]; + tensor key_states_115_cast = reshape(shape = var_2592, x = transpose_20)[name = tensor("key_states_115_cast")]; + tensor var_2594 = const()[name = tensor("op_2594"), val = tensor([20, -1, 64])]; + tensor transpose_19 = transpose(perm = var_2579_perm_0, x = var_2578_cast)[name = tensor("transpose_19")]; + tensor value_states_115_cast = reshape(shape = var_2594, x = transpose_19)[name = tensor("value_states_115_cast")]; + tensor var_2597_perm_0 = const()[name = tensor("op_2597_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_169_transpose_x_0 = const()[name = tensor("attn_weights_169_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_169_transpose_y_0 = const()[name = tensor("attn_weights_169_transpose_y_0"), val = tensor(false)]; + tensor transpose_17 = transpose(perm = var_2597_perm_0, x = key_states_115_cast)[name = tensor("transpose_17")]; + tensor attn_weights_169_cast = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = query_states_57_cast, y = transpose_17)[name = tensor("attn_weights_169_cast")]; + tensor var_2599 = const()[name = tensor("op_2599"), val = tensor([1, 20, 77, 77])]; + tensor var_2600_cast = reshape(shape = var_2599, x = attn_weights_169_cast)[name = tensor("op_2600_cast")]; + tensor attn_weights_171_cast = add(x = var_2600_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_171_cast")]; + tensor var_2605 = const()[name = tensor("op_2605"), val = tensor([20, 77, 77])]; + tensor input_453_cast = reshape(shape = var_2605, x = attn_weights_171_cast)[name = tensor("input_453_cast")]; + tensor input_455_cast = softmax(axis = var_5, x = input_453_cast)[name = tensor("input_455_cast")]; + tensor attn_output_169_transpose_x_0 = const()[name = tensor("attn_output_169_transpose_x_0"), val = tensor(false)]; + tensor attn_output_169_transpose_y_0 = const()[name = tensor("attn_output_169_transpose_y_0"), val = tensor(false)]; + tensor attn_output_169_cast = matmul(transpose_x = attn_output_169_transpose_x_0, transpose_y = attn_output_169_transpose_y_0, x = input_455_cast, y = value_states_115_cast)[name = tensor("attn_output_169_cast")]; + tensor var_2610 = const()[name = tensor("op_2610"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_171_cast = reshape(shape = var_2610, x = attn_output_169_cast)[name = tensor("attn_output_171_cast")]; + tensor attn_output_173_perm_0 = const()[name = tensor("attn_output_173_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2613 = const()[name = tensor("op_2613"), val = tensor([1, 77, 1280])]; + tensor transpose_16 = transpose(perm = attn_output_173_perm_0, x = attn_output_171_cast)[name = tensor("transpose_16")]; + tensor input_457_cast = reshape(shape = var_2613, x = transpose_16)[name = tensor("input_457_cast")]; + tensor text_encoder_text_model_encoder_layers_28_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1238502784)))]; + tensor text_encoder_text_model_encoder_layers_28_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1241779648)))]; + tensor hidden_states_171_cast = linear(bias = text_encoder_text_model_encoder_layers_28_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_self_attn_out_proj_weight_to_fp16, x = input_457_cast)[name = tensor("hidden_states_171_cast")]; + tensor input_459_cast = add(x = input_451_cast, y = hidden_states_171_cast)[name = tensor("input_459_cast")]; + tensor input_461_axes_0 = const()[name = tensor("input_461_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_28_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1241782272)))]; + tensor text_encoder_text_model_encoder_layers_28_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1241784896)))]; + tensor input_461_cast = layer_norm(axes = input_461_axes_0, beta = text_encoder_text_model_encoder_layers_28_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_28_layer_norm2_weight_to_fp16, x = input_459_cast)[name = tensor("input_461_cast")]; + tensor text_encoder_text_model_encoder_layers_28_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1241787520)))]; + tensor text_encoder_text_model_encoder_layers_28_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1254894784)))]; + tensor input_463_cast = linear(bias = text_encoder_text_model_encoder_layers_28_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_mlp_fc1_weight_to_fp16, x = input_461_cast)[name = tensor("input_463_cast")]; + tensor input_465_mode_0 = const()[name = tensor("input_465_mode_0"), val = tensor("EXACT")]; + tensor input_465_cast = gelu(mode = input_465_mode_0, x = input_463_cast)[name = tensor("input_465_cast")]; + tensor text_encoder_text_model_encoder_layers_28_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1254905088)))]; + tensor text_encoder_text_model_encoder_layers_28_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_28_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1268012352)))]; + tensor hidden_states_173_cast = linear(bias = text_encoder_text_model_encoder_layers_28_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_28_mlp_fc2_weight_to_fp16, x = input_465_cast)[name = tensor("hidden_states_173_cast")]; + tensor input_467_cast = add(x = input_459_cast, y = hidden_states_173_cast)[name = tensor("input_467_cast")]; + tensor hidden_states_175_axes_0 = const()[name = tensor("hidden_states_175_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_29_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1268014976)))]; + tensor text_encoder_text_model_encoder_layers_29_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1268017600)))]; + tensor hidden_states_175_cast = layer_norm(axes = hidden_states_175_axes_0, beta = text_encoder_text_model_encoder_layers_29_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_29_layer_norm1_weight_to_fp16, x = input_467_cast)[name = tensor("hidden_states_175_cast")]; + tensor text_encoder_text_model_encoder_layers_29_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1268020224)))]; + tensor text_encoder_text_model_encoder_layers_29_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271297088)))]; + tensor var_2651_cast = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_q_proj_weight_to_fp16, x = hidden_states_175_cast)[name = tensor("op_2651_cast")]; + tensor var_2652_to_fp16 = const()[name = tensor("op_2652_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_179_cast = mul(x = var_2651_cast, y = var_2652_to_fp16)[name = tensor("tensor_179_cast")]; + tensor text_encoder_text_model_encoder_layers_29_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1271299712)))]; + tensor text_encoder_text_model_encoder_layers_29_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1274576576)))]; + tensor tensor_175_cast = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_k_proj_weight_to_fp16, x = hidden_states_175_cast)[name = tensor("tensor_175_cast")]; + tensor var_2657 = const()[name = tensor("op_2657"), val = tensor([1, -1, 20, 64])]; + tensor var_2658_cast = reshape(shape = var_2657, x = tensor_175_cast)[name = tensor("op_2658_cast")]; + tensor var_2659_perm_0 = const()[name = tensor("op_2659_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_29_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1274579200)))]; + tensor text_encoder_text_model_encoder_layers_29_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1277856064)))]; + tensor tensor_177_cast = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_v_proj_weight_to_fp16, x = hidden_states_175_cast)[name = tensor("tensor_177_cast")]; + tensor var_2664 = const()[name = tensor("op_2664"), val = tensor([1, -1, 20, 64])]; + tensor var_2665_cast = reshape(shape = var_2664, x = tensor_177_cast)[name = tensor("op_2665_cast")]; + tensor var_2666_perm_0 = const()[name = tensor("op_2666_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2673 = const()[name = tensor("op_2673"), val = tensor([1, 77, 20, 64])]; + tensor var_2674_cast = reshape(shape = var_2673, x = tensor_179_cast)[name = tensor("op_2674_cast")]; + tensor var_2675_perm_0 = const()[name = tensor("op_2675_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2677 = const()[name = tensor("op_2677"), val = tensor([20, -1, 64])]; + tensor transpose_13 = transpose(perm = var_2675_perm_0, x = var_2674_cast)[name = tensor("transpose_13")]; + tensor query_states_59_cast = reshape(shape = var_2677, x = transpose_13)[name = tensor("query_states_59_cast")]; + tensor var_2679 = const()[name = tensor("op_2679"), val = tensor([20, -1, 64])]; + tensor transpose_15 = transpose(perm = var_2659_perm_0, x = var_2658_cast)[name = tensor("transpose_15")]; + tensor key_states_119_cast = reshape(shape = var_2679, x = transpose_15)[name = tensor("key_states_119_cast")]; + tensor var_2681 = const()[name = tensor("op_2681"), val = tensor([20, -1, 64])]; + tensor transpose_14 = transpose(perm = var_2666_perm_0, x = var_2665_cast)[name = tensor("transpose_14")]; + tensor value_states_119_cast = reshape(shape = var_2681, x = transpose_14)[name = tensor("value_states_119_cast")]; + tensor var_2684_perm_0 = const()[name = tensor("op_2684_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_175_transpose_x_0 = const()[name = tensor("attn_weights_175_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_175_transpose_y_0 = const()[name = tensor("attn_weights_175_transpose_y_0"), val = tensor(false)]; + tensor transpose_12 = transpose(perm = var_2684_perm_0, x = key_states_119_cast)[name = tensor("transpose_12")]; + tensor attn_weights_175_cast = matmul(transpose_x = attn_weights_175_transpose_x_0, transpose_y = attn_weights_175_transpose_y_0, x = query_states_59_cast, y = transpose_12)[name = tensor("attn_weights_175_cast")]; + tensor var_2686 = const()[name = tensor("op_2686"), val = tensor([1, 20, 77, 77])]; + tensor var_2687_cast = reshape(shape = var_2686, x = attn_weights_175_cast)[name = tensor("op_2687_cast")]; + tensor attn_weights_177_cast = add(x = var_2687_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_177_cast")]; + tensor var_2692 = const()[name = tensor("op_2692"), val = tensor([20, 77, 77])]; + tensor input_469_cast = reshape(shape = var_2692, x = attn_weights_177_cast)[name = tensor("input_469_cast")]; + tensor input_471_cast = softmax(axis = var_5, x = input_469_cast)[name = tensor("input_471_cast")]; + tensor attn_output_175_transpose_x_0 = const()[name = tensor("attn_output_175_transpose_x_0"), val = tensor(false)]; + tensor attn_output_175_transpose_y_0 = const()[name = tensor("attn_output_175_transpose_y_0"), val = tensor(false)]; + tensor attn_output_175_cast = matmul(transpose_x = attn_output_175_transpose_x_0, transpose_y = attn_output_175_transpose_y_0, x = input_471_cast, y = value_states_119_cast)[name = tensor("attn_output_175_cast")]; + tensor var_2697 = const()[name = tensor("op_2697"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_177_cast = reshape(shape = var_2697, x = attn_output_175_cast)[name = tensor("attn_output_177_cast")]; + tensor attn_output_179_perm_0 = const()[name = tensor("attn_output_179_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2700 = const()[name = tensor("op_2700"), val = tensor([1, 77, 1280])]; + tensor transpose_11 = transpose(perm = attn_output_179_perm_0, x = attn_output_177_cast)[name = tensor("transpose_11")]; + tensor input_473_cast = reshape(shape = var_2700, x = transpose_11)[name = tensor("input_473_cast")]; + tensor text_encoder_text_model_encoder_layers_29_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1277858688)))]; + tensor text_encoder_text_model_encoder_layers_29_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1281135552)))]; + tensor hidden_states_177_cast = linear(bias = text_encoder_text_model_encoder_layers_29_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_self_attn_out_proj_weight_to_fp16, x = input_473_cast)[name = tensor("hidden_states_177_cast")]; + tensor input_475_cast = add(x = input_467_cast, y = hidden_states_177_cast)[name = tensor("input_475_cast")]; + tensor input_477_axes_0 = const()[name = tensor("input_477_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_29_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1281138176)))]; + tensor text_encoder_text_model_encoder_layers_29_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1281140800)))]; + tensor input_477_cast = layer_norm(axes = input_477_axes_0, beta = text_encoder_text_model_encoder_layers_29_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_29_layer_norm2_weight_to_fp16, x = input_475_cast)[name = tensor("input_477_cast")]; + tensor text_encoder_text_model_encoder_layers_29_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1281143424)))]; + tensor text_encoder_text_model_encoder_layers_29_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1294250688)))]; + tensor input_479_cast = linear(bias = text_encoder_text_model_encoder_layers_29_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_mlp_fc1_weight_to_fp16, x = input_477_cast)[name = tensor("input_479_cast")]; + tensor input_481_mode_0 = const()[name = tensor("input_481_mode_0"), val = tensor("EXACT")]; + tensor input_481_cast = gelu(mode = input_481_mode_0, x = input_479_cast)[name = tensor("input_481_cast")]; + tensor text_encoder_text_model_encoder_layers_29_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1294260992)))]; + tensor text_encoder_text_model_encoder_layers_29_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_29_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1307368256)))]; + tensor hidden_states_179_cast = linear(bias = text_encoder_text_model_encoder_layers_29_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_29_mlp_fc2_weight_to_fp16, x = input_481_cast)[name = tensor("hidden_states_179_cast")]; + tensor input_483_cast = add(x = input_475_cast, y = hidden_states_179_cast)[name = tensor("input_483_cast")]; + tensor hidden_states_181_axes_0 = const()[name = tensor("hidden_states_181_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_30_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1307370880)))]; + tensor text_encoder_text_model_encoder_layers_30_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1307373504)))]; + tensor hidden_states_181_cast = layer_norm(axes = hidden_states_181_axes_0, beta = text_encoder_text_model_encoder_layers_30_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_30_layer_norm1_weight_to_fp16, x = input_483_cast)[name = tensor("hidden_states_181_cast")]; + tensor text_encoder_text_model_encoder_layers_30_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1307376128)))]; + tensor text_encoder_text_model_encoder_layers_30_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1310652992)))]; + tensor var_2738_cast = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_q_proj_weight_to_fp16, x = hidden_states_181_cast)[name = tensor("op_2738_cast")]; + tensor var_2739_to_fp16 = const()[name = tensor("op_2739_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_185_cast = mul(x = var_2738_cast, y = var_2739_to_fp16)[name = tensor("tensor_185_cast")]; + tensor text_encoder_text_model_encoder_layers_30_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1310655616)))]; + tensor text_encoder_text_model_encoder_layers_30_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1313932480)))]; + tensor tensor_181_cast = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_k_proj_weight_to_fp16, x = hidden_states_181_cast)[name = tensor("tensor_181_cast")]; + tensor var_2744 = const()[name = tensor("op_2744"), val = tensor([1, -1, 20, 64])]; + tensor var_2745_cast = reshape(shape = var_2744, x = tensor_181_cast)[name = tensor("op_2745_cast")]; + tensor var_2746_perm_0 = const()[name = tensor("op_2746_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_30_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1313935104)))]; + tensor text_encoder_text_model_encoder_layers_30_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1317211968)))]; + tensor tensor_183_cast = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_v_proj_weight_to_fp16, x = hidden_states_181_cast)[name = tensor("tensor_183_cast")]; + tensor var_2751 = const()[name = tensor("op_2751"), val = tensor([1, -1, 20, 64])]; + tensor var_2752_cast = reshape(shape = var_2751, x = tensor_183_cast)[name = tensor("op_2752_cast")]; + tensor var_2753_perm_0 = const()[name = tensor("op_2753_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2760 = const()[name = tensor("op_2760"), val = tensor([1, 77, 20, 64])]; + tensor var_2761_cast = reshape(shape = var_2760, x = tensor_185_cast)[name = tensor("op_2761_cast")]; + tensor var_2762_perm_0 = const()[name = tensor("op_2762_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2764 = const()[name = tensor("op_2764"), val = tensor([20, -1, 64])]; + tensor transpose_8 = transpose(perm = var_2762_perm_0, x = var_2761_cast)[name = tensor("transpose_8")]; + tensor query_states_61_cast = reshape(shape = var_2764, x = transpose_8)[name = tensor("query_states_61_cast")]; + tensor var_2766 = const()[name = tensor("op_2766"), val = tensor([20, -1, 64])]; + tensor transpose_10 = transpose(perm = var_2746_perm_0, x = var_2745_cast)[name = tensor("transpose_10")]; + tensor key_states_123_cast = reshape(shape = var_2766, x = transpose_10)[name = tensor("key_states_123_cast")]; + tensor var_2768 = const()[name = tensor("op_2768"), val = tensor([20, -1, 64])]; + tensor transpose_9 = transpose(perm = var_2753_perm_0, x = var_2752_cast)[name = tensor("transpose_9")]; + tensor value_states_123_cast = reshape(shape = var_2768, x = transpose_9)[name = tensor("value_states_123_cast")]; + tensor var_2771_perm_0 = const()[name = tensor("op_2771_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_181_transpose_x_0 = const()[name = tensor("attn_weights_181_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_181_transpose_y_0 = const()[name = tensor("attn_weights_181_transpose_y_0"), val = tensor(false)]; + tensor transpose_7 = transpose(perm = var_2771_perm_0, x = key_states_123_cast)[name = tensor("transpose_7")]; + tensor attn_weights_181_cast = matmul(transpose_x = attn_weights_181_transpose_x_0, transpose_y = attn_weights_181_transpose_y_0, x = query_states_61_cast, y = transpose_7)[name = tensor("attn_weights_181_cast")]; + tensor var_2773 = const()[name = tensor("op_2773"), val = tensor([1, 20, 77, 77])]; + tensor var_2774_cast = reshape(shape = var_2773, x = attn_weights_181_cast)[name = tensor("op_2774_cast")]; + tensor attn_weights_183_cast = add(x = var_2774_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_183_cast")]; + tensor var_2779 = const()[name = tensor("op_2779"), val = tensor([20, 77, 77])]; + tensor input_485_cast = reshape(shape = var_2779, x = attn_weights_183_cast)[name = tensor("input_485_cast")]; + tensor input_487_cast = softmax(axis = var_5, x = input_485_cast)[name = tensor("input_487_cast")]; + tensor attn_output_181_transpose_x_0 = const()[name = tensor("attn_output_181_transpose_x_0"), val = tensor(false)]; + tensor attn_output_181_transpose_y_0 = const()[name = tensor("attn_output_181_transpose_y_0"), val = tensor(false)]; + tensor attn_output_181_cast = matmul(transpose_x = attn_output_181_transpose_x_0, transpose_y = attn_output_181_transpose_y_0, x = input_487_cast, y = value_states_123_cast)[name = tensor("attn_output_181_cast")]; + tensor var_2784 = const()[name = tensor("op_2784"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_183_cast = reshape(shape = var_2784, x = attn_output_181_cast)[name = tensor("attn_output_183_cast")]; + tensor attn_output_185_perm_0 = const()[name = tensor("attn_output_185_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2787 = const()[name = tensor("op_2787"), val = tensor([1, 77, 1280])]; + tensor transpose_6 = transpose(perm = attn_output_185_perm_0, x = attn_output_183_cast)[name = tensor("transpose_6")]; + tensor input_489_cast = reshape(shape = var_2787, x = transpose_6)[name = tensor("input_489_cast")]; + tensor text_encoder_text_model_encoder_layers_30_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1317214592)))]; + tensor text_encoder_text_model_encoder_layers_30_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1320491456)))]; + tensor hidden_states_183_cast = linear(bias = text_encoder_text_model_encoder_layers_30_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_self_attn_out_proj_weight_to_fp16, x = input_489_cast)[name = tensor("hidden_states_183_cast")]; + tensor input_491_cast = add(x = input_483_cast, y = hidden_states_183_cast)[name = tensor("input_491_cast")]; + tensor input_493_axes_0 = const()[name = tensor("input_493_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_30_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1320494080)))]; + tensor text_encoder_text_model_encoder_layers_30_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1320496704)))]; + tensor input_493_cast = layer_norm(axes = input_493_axes_0, beta = text_encoder_text_model_encoder_layers_30_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_30_layer_norm2_weight_to_fp16, x = input_491_cast)[name = tensor("input_493_cast")]; + tensor text_encoder_text_model_encoder_layers_30_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1320499328)))]; + tensor text_encoder_text_model_encoder_layers_30_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1333606592)))]; + tensor input_495_cast = linear(bias = text_encoder_text_model_encoder_layers_30_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_mlp_fc1_weight_to_fp16, x = input_493_cast)[name = tensor("input_495_cast")]; + tensor input_497_mode_0 = const()[name = tensor("input_497_mode_0"), val = tensor("EXACT")]; + tensor input_497_cast = gelu(mode = input_497_mode_0, x = input_495_cast)[name = tensor("input_497_cast")]; + tensor text_encoder_text_model_encoder_layers_30_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1333616896)))]; + tensor text_encoder_text_model_encoder_layers_30_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_30_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1346724160)))]; + tensor hidden_states_185_cast = linear(bias = text_encoder_text_model_encoder_layers_30_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_30_mlp_fc2_weight_to_fp16, x = input_497_cast)[name = tensor("hidden_states_185_cast")]; + tensor input_499_cast = add(x = input_491_cast, y = hidden_states_185_cast)[name = tensor("input_499_cast")]; + tensor input_499_cast_to_fp32_dtype_0 = const()[name = tensor("input_499_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor hidden_states_187_axes_0 = const()[name = tensor("hidden_states_187_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_31_layer_norm1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_layer_norm1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1346726784)))]; + tensor text_encoder_text_model_encoder_layers_31_layer_norm1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_layer_norm1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1346729408)))]; + tensor hidden_states_187_cast = layer_norm(axes = hidden_states_187_axes_0, beta = text_encoder_text_model_encoder_layers_31_layer_norm1_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_31_layer_norm1_weight_to_fp16, x = input_499_cast)[name = tensor("hidden_states_187_cast")]; + tensor text_encoder_text_model_encoder_layers_31_self_attn_q_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_self_attn_q_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1346732032)))]; + tensor text_encoder_text_model_encoder_layers_31_self_attn_q_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_self_attn_q_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1350008896)))]; + tensor var_2825_cast = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_q_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_q_proj_weight_to_fp16, x = hidden_states_187_cast)[name = tensor("op_2825_cast")]; + tensor var_2826_to_fp16 = const()[name = tensor("op_2826_to_fp16"), val = tensor(0x1p-3)]; + tensor tensor_cast = mul(x = var_2825_cast, y = var_2826_to_fp16)[name = tensor("tensor_cast")]; + tensor text_encoder_text_model_encoder_layers_31_self_attn_k_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_self_attn_k_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1350011520)))]; + tensor text_encoder_text_model_encoder_layers_31_self_attn_k_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_self_attn_k_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1353288384)))]; + tensor tensor_187_cast = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_k_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_k_proj_weight_to_fp16, x = hidden_states_187_cast)[name = tensor("tensor_187_cast")]; + tensor var_2831 = const()[name = tensor("op_2831"), val = tensor([1, -1, 20, 64])]; + tensor var_2832_cast = reshape(shape = var_2831, x = tensor_187_cast)[name = tensor("op_2832_cast")]; + tensor var_2833_perm_0 = const()[name = tensor("op_2833_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor text_encoder_text_model_encoder_layers_31_self_attn_v_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_self_attn_v_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1353291008)))]; + tensor text_encoder_text_model_encoder_layers_31_self_attn_v_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_self_attn_v_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1356567872)))]; + tensor tensor_189_cast = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_v_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_v_proj_weight_to_fp16, x = hidden_states_187_cast)[name = tensor("tensor_189_cast")]; + tensor var_2838 = const()[name = tensor("op_2838"), val = tensor([1, -1, 20, 64])]; + tensor var_2839_cast = reshape(shape = var_2838, x = tensor_189_cast)[name = tensor("op_2839_cast")]; + tensor var_2840_perm_0 = const()[name = tensor("op_2840_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2847 = const()[name = tensor("op_2847"), val = tensor([1, 77, 20, 64])]; + tensor var_2848_cast = reshape(shape = var_2847, x = tensor_cast)[name = tensor("op_2848_cast")]; + tensor var_2849_perm_0 = const()[name = tensor("op_2849_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2851 = const()[name = tensor("op_2851"), val = tensor([20, -1, 64])]; + tensor transpose_3 = transpose(perm = var_2849_perm_0, x = var_2848_cast)[name = tensor("transpose_3")]; + tensor query_states_cast = reshape(shape = var_2851, x = transpose_3)[name = tensor("query_states_cast")]; + tensor var_2853 = const()[name = tensor("op_2853"), val = tensor([20, -1, 64])]; + tensor transpose_5 = transpose(perm = var_2833_perm_0, x = var_2832_cast)[name = tensor("transpose_5")]; + tensor key_states_cast = reshape(shape = var_2853, x = transpose_5)[name = tensor("key_states_cast")]; + tensor var_2855 = const()[name = tensor("op_2855"), val = tensor([20, -1, 64])]; + tensor transpose_4 = transpose(perm = var_2840_perm_0, x = var_2839_cast)[name = tensor("transpose_4")]; + tensor value_states_cast = reshape(shape = var_2855, x = transpose_4)[name = tensor("value_states_cast")]; + tensor var_2858_perm_0 = const()[name = tensor("op_2858_perm_0"), val = tensor([0, 2, 1])]; + tensor attn_weights_187_transpose_x_0 = const()[name = tensor("attn_weights_187_transpose_x_0"), val = tensor(false)]; + tensor attn_weights_187_transpose_y_0 = const()[name = tensor("attn_weights_187_transpose_y_0"), val = tensor(false)]; + tensor transpose_2 = transpose(perm = var_2858_perm_0, x = key_states_cast)[name = tensor("transpose_2")]; + tensor attn_weights_187_cast = matmul(transpose_x = attn_weights_187_transpose_x_0, transpose_y = attn_weights_187_transpose_y_0, x = query_states_cast, y = transpose_2)[name = tensor("attn_weights_187_cast")]; + tensor var_2860 = const()[name = tensor("op_2860"), val = tensor([1, 20, 77, 77])]; + tensor var_2861_cast = reshape(shape = var_2860, x = attn_weights_187_cast)[name = tensor("op_2861_cast")]; + tensor attn_weights_189_cast = add(x = var_2861_cast, y = causal_attention_mask_to_fp16)[name = tensor("attn_weights_189_cast")]; + tensor var_2866 = const()[name = tensor("op_2866"), val = tensor([20, 77, 77])]; + tensor input_501_cast = reshape(shape = var_2866, x = attn_weights_189_cast)[name = tensor("input_501_cast")]; + tensor input_503_cast = softmax(axis = var_5, x = input_501_cast)[name = tensor("input_503_cast")]; + tensor attn_output_187_transpose_x_0 = const()[name = tensor("attn_output_187_transpose_x_0"), val = tensor(false)]; + tensor attn_output_187_transpose_y_0 = const()[name = tensor("attn_output_187_transpose_y_0"), val = tensor(false)]; + tensor attn_output_187_cast = matmul(transpose_x = attn_output_187_transpose_x_0, transpose_y = attn_output_187_transpose_y_0, x = input_503_cast, y = value_states_cast)[name = tensor("attn_output_187_cast")]; + tensor var_2871 = const()[name = tensor("op_2871"), val = tensor([1, 20, 77, 64])]; + tensor attn_output_189_cast = reshape(shape = var_2871, x = attn_output_187_cast)[name = tensor("attn_output_189_cast")]; + tensor attn_output_perm_0 = const()[name = tensor("attn_output_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_2874 = const()[name = tensor("op_2874"), val = tensor([1, 77, 1280])]; + tensor transpose_1 = transpose(perm = attn_output_perm_0, x = attn_output_189_cast)[name = tensor("transpose_1")]; + tensor input_505_cast = reshape(shape = var_2874, x = transpose_1)[name = tensor("input_505_cast")]; + tensor text_encoder_text_model_encoder_layers_31_self_attn_out_proj_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_self_attn_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1356570496)))]; + tensor text_encoder_text_model_encoder_layers_31_self_attn_out_proj_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_self_attn_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1359847360)))]; + tensor hidden_states_189_cast = linear(bias = text_encoder_text_model_encoder_layers_31_self_attn_out_proj_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_self_attn_out_proj_weight_to_fp16, x = input_505_cast)[name = tensor("hidden_states_189_cast")]; + tensor input_507_cast = add(x = input_499_cast, y = hidden_states_189_cast)[name = tensor("input_507_cast")]; + tensor input_509_axes_0 = const()[name = tensor("input_509_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_encoder_layers_31_layer_norm2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_layer_norm2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1359849984)))]; + tensor text_encoder_text_model_encoder_layers_31_layer_norm2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_layer_norm2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1359852608)))]; + tensor input_509_cast = layer_norm(axes = input_509_axes_0, beta = text_encoder_text_model_encoder_layers_31_layer_norm2_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_encoder_layers_31_layer_norm2_weight_to_fp16, x = input_507_cast)[name = tensor("input_509_cast")]; + tensor text_encoder_text_model_encoder_layers_31_mlp_fc1_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_mlp_fc1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1359855232)))]; + tensor text_encoder_text_model_encoder_layers_31_mlp_fc1_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_mlp_fc1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1372962496)))]; + tensor input_511_cast = linear(bias = text_encoder_text_model_encoder_layers_31_mlp_fc1_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_mlp_fc1_weight_to_fp16, x = input_509_cast)[name = tensor("input_511_cast")]; + tensor input_513_mode_0 = const()[name = tensor("input_513_mode_0"), val = tensor("EXACT")]; + tensor input_513_cast = gelu(mode = input_513_mode_0, x = input_511_cast)[name = tensor("input_513_cast")]; + tensor text_encoder_text_model_encoder_layers_31_mlp_fc2_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_mlp_fc2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1372972800)))]; + tensor text_encoder_text_model_encoder_layers_31_mlp_fc2_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_encoder_layers_31_mlp_fc2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1386080064)))]; + tensor hidden_states_cast = linear(bias = text_encoder_text_model_encoder_layers_31_mlp_fc2_bias_to_fp16, weight = text_encoder_text_model_encoder_layers_31_mlp_fc2_weight_to_fp16, x = input_513_cast)[name = tensor("hidden_states_cast")]; + tensor input_515_cast = add(x = input_507_cast, y = hidden_states_cast)[name = tensor("input_515_cast")]; + tensor last_hidden_state_axes_0 = const()[name = tensor("last_hidden_state_axes_0"), val = tensor([-1])]; + tensor text_encoder_text_model_final_layer_norm_weight_to_fp16 = const()[name = tensor("text_encoder_text_model_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1386082688)))]; + tensor text_encoder_text_model_final_layer_norm_bias_to_fp16 = const()[name = tensor("text_encoder_text_model_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1386085312)))]; + tensor last_hidden_state_cast = layer_norm(axes = last_hidden_state_axes_0, beta = text_encoder_text_model_final_layer_norm_bias_to_fp16, epsilon = var_12_to_fp16, gamma = text_encoder_text_model_final_layer_norm_weight_to_fp16, x = input_515_cast)[name = tensor("last_hidden_state_cast")]; + tensor var_2902 = const()[name = tensor("op_2902"), val = tensor([0])]; + tensor var_2904 = reduce_argmax(axis = var_5, keep_dims = var_6, x = cast_1322)[name = tensor("op_2904")]; + tensor stack_0_axis_0 = const()[name = tensor("stack_0_axis_0"), val = tensor(1)]; + tensor stack_0 = stack(axis = stack_0_axis_0, values = (var_2902, var_2904))[name = tensor("stack_0")]; + tensor input_transpose_batch_dims_0 = const()[name = tensor("input_transpose_batch_dims_0"), val = tensor(0)]; + tensor input_transpose_cast = gather_nd(batch_dims = input_transpose_batch_dims_0, indices = stack_0, x = last_hidden_state_cast)[name = tensor("input_transpose_cast")]; + tensor text_encoder_text_projection_weight_to_fp16 = const()[name = tensor("text_encoder_text_projection_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1386087936)))]; + tensor var_2911_bias_0_to_fp16 = const()[name = tensor("op_2911_bias_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1389364800)))]; + tensor var_2911_cast = linear(bias = var_2911_bias_0_to_fp16, weight = text_encoder_text_projection_weight_to_fp16, x = input_transpose_cast)[name = tensor("op_2911_cast")]; + tensor var_2911_cast_to_fp32_dtype_0 = const()[name = tensor("op_2911_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor pooled_outputs = cast(dtype = var_2911_cast_to_fp32_dtype_0, x = var_2911_cast)[name = tensor("cast_325")]; + tensor hidden_embeds = cast(dtype = input_499_cast_to_fp32_dtype_0, x = input_499_cast)[name = tensor("cast_359")]; + } -> (hidden_embeds, pooled_outputs); +} \ No newline at end of file diff --git a/compiled/TextEncoder2.mlmodelc/weights/weight.bin b/compiled/TextEncoder2.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..4404ced8325eb5e803c3b4b2be80bf0b4517dce4 --- /dev/null +++ b/compiled/TextEncoder2.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8bd1fc0bcce11cff685648387b0060e0b6ecfce6c34e580e1ae904cae5903363 +size 1389367424 diff --git a/compiled/Unet.mlmodelc/analytics/coremldata.bin b/compiled/Unet.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..069df79e644d85c00535a9d29279158d4ade60b3 --- /dev/null +++ b/compiled/Unet.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:df3831a13c76df3097f4ae659ef46a7f6604f93b45c1a5f8d14e362424d15e1f +size 207 diff --git a/compiled/Unet.mlmodelc/coremldata.bin b/compiled/Unet.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..6ec4882d93c41f4d859f419572182f8503ec75fc --- /dev/null +++ b/compiled/Unet.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8aea3887ffdc8e059925f3981259e1cd3227b827e5f91edff613c73ac0ea16f6 +size 1338 diff --git a/compiled/Unet.mlmodelc/metadata.json b/compiled/Unet.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..407d72bda6f5e1042451654f69cc456161f98322 --- /dev/null +++ b/compiled/Unet.mlmodelc/metadata.json @@ -0,0 +1,122 @@ +[ + { + "shortDescription" : "Stable Diffusion generates images conditioned on text or other images as input through the diffusion process. Please refer to https:\/\/arxiv.org\/abs\/2112.10752 for details.", + "metadataOutputVersion" : "3.0", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32)", + "shortDescription" : "Same shape and dtype as the `sample` input. The predicted noise to facilitate the reverse diffusion (denoising) process", + "shape" : "[]", + "name" : "noise_pred", + "type" : "MultiArray" + } + ], + "version" : "diffusers\/stable-diffusion-xl-base-1.0", + "modelParameters" : [ + + ], + "author" : "Please refer to the Model Card available at huggingface.co\/diffusers\/stable-diffusion-xl-base-1.0", + "specificationVersion" : 7, + "storagePrecision" : "Float16", + "license" : "OpenRAIL (https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license)", + "mlProgramOperationTypeHistogram" : { + "UpsampleNearestNeighbor" : 2, + "Ios16.reduceMean" : 512, + "Ios16.sin" : 2, + "Ios16.softmax" : 140, + "Split" : 70, + "Ios16.add" : 722, + "Concat" : 14, + "Ios16.realDiv" : 46, + "Ios16.square" : 46, + "ExpandDims" : 6, + "Ios16.sub" : 256, + "Ios16.cast" : 1, + "Ios16.conv" : 794, + "Ios16.gelu" : 70, + "Ios16.matmul" : 280, + "Ios16.reshape" : 675, + "Ios16.batchNorm" : 46, + "Ios16.rsqrt" : 210, + "Ios16.silu" : 38, + "Ios16.sqrt" : 46, + "SliceByIndex" : 4, + "Ios16.mul" : 842, + "Ios16.cos" : 2 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 2 × 4 × 128 × 128)", + "shortDescription" : "The low resolution latent feature maps being denoised through reverse diffusion", + "shape" : "[2, 4, 128, 128]", + "name" : "sample", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 2)", + "shortDescription" : "A value emitted by the associated scheduler object to condition the model on a given noise schedule", + "shape" : "[2]", + "name" : "timestep", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 2 × 2048 × 1 × 77)", + "shortDescription" : "Output embeddings from the associated text_encoder model to condition to generated image on text. A maximum of 77 tokens (~40 words) are allowed. Longer text is truncated. Shorter text does not reduce computation.", + "shape" : "[2, 2048, 1, 77]", + "name" : "encoder_hidden_states", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 12)", + "shortDescription" : "", + "shape" : "[12]", + "name" : "time_ids", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 2 × 1280)", + "shortDescription" : "", + "shape" : "[2, 1280]", + "name" : "text_embeds", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.version" : "7.0b1", + "com.github.apple.coremltools.source" : "torch==2.0.1+cu117", + "com.github.apple.ml-stable-diffusion.version" : "1.0.0" + }, + "generatedClassName" : "Stable_Diffusion_version_diffusers_stable_diffusion_xl_base_1_0_unet", + "method" : "predict" + } +] \ No newline at end of file diff --git a/compiled/Unet.mlmodelc/model.mil b/compiled/Unet.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..dc722a2b1b1cd7dbc8c623cccf84287591dab2bf --- /dev/null +++ b/compiled/Unet.mlmodelc/model.mil @@ -0,0 +1,12327 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.33.4"}, {"coremlc-version", "1839.0.0"}, {"coremltools-component-torch", "2.0.1+cu117"}, {"coremltools-version", "7.0b1"}})] +{ + func main(tensor encoder_hidden_states, tensor sample, tensor text_embeds, tensor time_ids, tensor timestep) { + tensor var_24 = const()[name = tensor("op_24"), val = tensor(-1)]; + tensor var_41_axes_0 = const()[name = tensor("op_41_axes_0"), val = tensor([1])]; + tensor var_41_cast = expand_dims(axes = var_41_axes_0, x = timestep)[name = tensor("op_41_cast")]; + tensor var_43_to_fp16 = const()[name = tensor("op_43_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_3_cast = mul(x = var_41_cast, y = var_43_to_fp16)[name = tensor("emb_3_cast")]; + tensor var_48_cast = sin(x = emb_3_cast)[name = tensor("op_48_cast")]; + tensor var_49_cast = cos(x = emb_3_cast)[name = tensor("op_49_cast")]; + tensor emb_7_interleave_0 = const()[name = tensor("emb_7_interleave_0"), val = tensor(false)]; + tensor emb_7_cast = concat(axis = var_24, interleave = emb_7_interleave_0, values = (var_48_cast, var_49_cast))[name = tensor("emb_7_cast")]; + tensor var_53_begin_0 = const()[name = tensor("op_53_begin_0"), val = tensor([0, 160])]; + tensor var_53_end_0 = const()[name = tensor("op_53_end_0"), val = tensor([2, 320])]; + tensor var_53_end_mask_0 = const()[name = tensor("op_53_end_mask_0"), val = tensor([true, true])]; + tensor var_53_cast = slice_by_index(begin = var_53_begin_0, end = var_53_end_0, end_mask = var_53_end_mask_0, x = emb_7_cast)[name = tensor("op_53_cast")]; + tensor var_55_begin_0 = const()[name = tensor("op_55_begin_0"), val = tensor([0, 0])]; + tensor var_55_end_0 = const()[name = tensor("op_55_end_0"), val = tensor([2, 160])]; + tensor var_55_end_mask_0 = const()[name = tensor("op_55_end_mask_0"), val = tensor([true, false])]; + tensor var_55_cast = slice_by_index(begin = var_55_begin_0, end = var_55_end_0, end_mask = var_55_end_mask_0, x = emb_7_cast)[name = tensor("op_55_cast")]; + tensor sample_3_interleave_0 = const()[name = tensor("sample_3_interleave_0"), val = tensor(false)]; + tensor sample_3_cast = concat(axis = var_24, interleave = sample_3_interleave_0, values = (var_53_cast, var_55_cast))[name = tensor("sample_3_cast")]; + tensor var_58 = const()[name = tensor("op_58"), val = tensor(1)]; + tensor var_65_axes_0 = const()[name = tensor("op_65_axes_0"), val = tensor([-1])]; + tensor var_65_cast = expand_dims(axes = var_65_axes_0, x = sample_3_cast)[name = tensor("op_65_cast")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1_cast = expand_dims(axes = input_1_axes_0, x = var_65_cast)[name = tensor("input_1_cast")]; + tensor var_69 = const()[name = tensor("op_69"), val = tensor([1, 1])]; + tensor var_71 = const()[name = tensor("op_71"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_1_weight_to_fp16 = const()[name = tensor("time_embedding_linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; + tensor time_embedding_linear_1_bias_to_fp16 = const()[name = tensor("time_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(819712)))]; + tensor input_3_cast = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_71, groups = var_58, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_69, weight = time_embedding_linear_1_weight_to_fp16, x = input_1_cast)[name = tensor("input_3_cast")]; + tensor input_5_cast = silu(x = input_3_cast)[name = tensor("input_5_cast")]; + tensor var_77 = const()[name = tensor("op_77"), val = tensor([1, 1])]; + tensor var_79 = const()[name = tensor("op_79"), val = tensor([1, 1])]; + tensor emb_pad_type_0 = const()[name = tensor("emb_pad_type_0"), val = tensor("custom")]; + tensor emb_pad_0 = const()[name = tensor("emb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_2_weight_to_fp16 = const()[name = tensor("time_embedding_linear_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822336)))]; + tensor time_embedding_linear_2_bias_to_fp16 = const()[name = tensor("time_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4099200)))]; + tensor emb_cast = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_79, groups = var_58, pad = emb_pad_0, pad_type = emb_pad_type_0, strides = var_77, weight = time_embedding_linear_2_weight_to_fp16, x = input_5_cast)[name = tensor("emb_cast")]; + tensor var_85 = const()[name = tensor("op_85"), val = tensor(-1)]; + tensor var_102_axes_0 = const()[name = tensor("op_102_axes_0"), val = tensor([1])]; + tensor var_102_cast = expand_dims(axes = var_102_axes_0, x = time_ids)[name = tensor("op_102_cast")]; + tensor var_104_to_fp16 = const()[name = tensor("op_104_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4101824)))]; + tensor emb_11_cast = mul(x = var_102_cast, y = var_104_to_fp16)[name = tensor("emb_11_cast")]; + tensor var_109_cast = sin(x = emb_11_cast)[name = tensor("op_109_cast")]; + tensor var_110_cast = cos(x = emb_11_cast)[name = tensor("op_110_cast")]; + tensor emb_15_interleave_0 = const()[name = tensor("emb_15_interleave_0"), val = tensor(false)]; + tensor emb_15_cast = concat(axis = var_85, interleave = emb_15_interleave_0, values = (var_109_cast, var_110_cast))[name = tensor("emb_15_cast")]; + tensor var_114_begin_0 = const()[name = tensor("op_114_begin_0"), val = tensor([0, 128])]; + tensor var_114_end_0 = const()[name = tensor("op_114_end_0"), val = tensor([12, 256])]; + tensor var_114_end_mask_0 = const()[name = tensor("op_114_end_mask_0"), val = tensor([true, true])]; + tensor var_114_cast = slice_by_index(begin = var_114_begin_0, end = var_114_end_0, end_mask = var_114_end_mask_0, x = emb_15_cast)[name = tensor("op_114_cast")]; + tensor var_116_begin_0 = const()[name = tensor("op_116_begin_0"), val = tensor([0, 0])]; + tensor var_116_end_0 = const()[name = tensor("op_116_end_0"), val = tensor([12, 128])]; + tensor var_116_end_mask_0 = const()[name = tensor("op_116_end_mask_0"), val = tensor([true, false])]; + tensor var_116_cast = slice_by_index(begin = var_116_begin_0, end = var_116_end_0, end_mask = var_116_end_mask_0, x = emb_15_cast)[name = tensor("op_116_cast")]; + tensor time_embeds_1_interleave_0 = const()[name = tensor("time_embeds_1_interleave_0"), val = tensor(false)]; + tensor time_embeds_1_cast = concat(axis = var_85, interleave = time_embeds_1_interleave_0, values = (var_114_cast, var_116_cast))[name = tensor("time_embeds_1_cast")]; + tensor var_124 = const()[name = tensor("op_124"), val = tensor([2, -1])]; + tensor time_embeds_cast = reshape(shape = var_124, x = time_embeds_1_cast)[name = tensor("time_embeds_cast")]; + tensor var_127 = const()[name = tensor("op_127"), val = tensor(-1)]; + tensor sample_interleave_0 = const()[name = tensor("sample_interleave_0"), val = tensor(false)]; + tensor sample_cast = concat(axis = var_127, interleave = sample_interleave_0, values = (text_embeds, time_embeds_cast))[name = tensor("sample_cast")]; + tensor var_129 = const()[name = tensor("op_129"), val = tensor(1)]; + tensor var_136_axes_0 = const()[name = tensor("op_136_axes_0"), val = tensor([-1])]; + tensor var_136_cast = expand_dims(axes = var_136_axes_0, x = sample_cast)[name = tensor("op_136_cast")]; + tensor input_7_axes_0 = const()[name = tensor("input_7_axes_0"), val = tensor([-1])]; + tensor input_7_cast = expand_dims(axes = input_7_axes_0, x = var_136_cast)[name = tensor("input_7_cast")]; + tensor var_140 = const()[name = tensor("op_140"), val = tensor([1, 1])]; + tensor var_142 = const()[name = tensor("op_142"), val = tensor([1, 1])]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor add_embedding_linear_1_weight_to_fp16 = const()[name = tensor("add_embedding_linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4102144)))]; + tensor add_embedding_linear_1_bias_to_fp16 = const()[name = tensor("add_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11311168)))]; + tensor input_9_cast = conv(bias = add_embedding_linear_1_bias_to_fp16, dilations = var_142, groups = var_129, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_140, weight = add_embedding_linear_1_weight_to_fp16, x = input_7_cast)[name = tensor("input_9_cast")]; + tensor input_11_cast = silu(x = input_9_cast)[name = tensor("input_11_cast")]; + tensor var_148 = const()[name = tensor("op_148"), val = tensor([1, 1])]; + tensor var_150 = const()[name = tensor("op_150"), val = tensor([1, 1])]; + tensor aug_emb_pad_type_0 = const()[name = tensor("aug_emb_pad_type_0"), val = tensor("custom")]; + tensor aug_emb_pad_0 = const()[name = tensor("aug_emb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor add_embedding_linear_2_weight_to_fp16 = const()[name = tensor("add_embedding_linear_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11313792)))]; + tensor add_embedding_linear_2_bias_to_fp16 = const()[name = tensor("add_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14590656)))]; + tensor aug_emb_cast = conv(bias = add_embedding_linear_2_bias_to_fp16, dilations = var_150, groups = var_129, pad = aug_emb_pad_0, pad_type = aug_emb_pad_type_0, strides = var_148, weight = add_embedding_linear_2_weight_to_fp16, x = input_11_cast)[name = tensor("aug_emb_cast")]; + tensor input_19_cast = add(x = emb_cast, y = aug_emb_cast)[name = tensor("input_19_cast")]; + tensor var_158 = const()[name = tensor("op_158"), val = tensor(1)]; + tensor var_161 = const()[name = tensor("op_161"), val = tensor([1, 1])]; + tensor var_163 = const()[name = tensor("op_163"), val = tensor([1, 1])]; + tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_in_weight_to_fp16 = const()[name = tensor("conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14593280)))]; + tensor conv_in_bias_to_fp16 = const()[name = tensor("conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14616384)))]; + tensor input_13_cast = conv(bias = conv_in_bias_to_fp16, dilations = var_163, groups = var_158, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_161, weight = conv_in_weight_to_fp16, x = sample)[name = tensor("input_13_cast")]; + tensor var_172 = const()[name = tensor("op_172"), val = tensor(1)]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([2, 32, 10, 128, 128])]; + tensor reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_13_cast)[name = tensor("reshape_0_cast")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast)[name = tensor("reduce_mean_0_cast")]; + tensor sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast)[name = tensor("sub_0_cast")]; + tensor square_0_cast = square(x = sub_0_cast)[name = tensor("square_0_cast")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast)[name = tensor("reduce_mean_2_cast")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_0_cast = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast")]; + tensor sqrt_0_cast = sqrt(x = add_0_cast)[name = tensor("sqrt_0_cast")]; + tensor real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast)[name = tensor("real_div_0_cast")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([2, 320, 128, 128])]; + tensor reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast)[name = tensor("reshape_1_cast")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14617088)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14617792)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14618496)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14619200)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast)[name = tensor("add_1_cast")]; + tensor input_17_cast = silu(x = add_1_cast)[name = tensor("input_17_cast")]; + tensor var_190 = const()[name = tensor("op_190"), val = tensor([1, 1])]; + tensor var_192 = const()[name = tensor("op_192"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14619904)))]; + tensor down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16463168)))]; + tensor hidden_states_1_cast = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_192, groups = var_172, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_190, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_17_cast)[name = tensor("hidden_states_1_cast")]; + tensor input_21_cast = silu(x = input_19_cast)[name = tensor("input_21_cast")]; + tensor var_198 = const()[name = tensor("op_198"), val = tensor([1, 1])]; + tensor var_200 = const()[name = tensor("op_200"), val = tensor([1, 1])]; + tensor temb_1_pad_type_0 = const()[name = tensor("temb_1_pad_type_0"), val = tensor("custom")]; + tensor temb_1_pad_0 = const()[name = tensor("temb_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16463872)))]; + tensor down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17283136)))]; + tensor temb_1_cast = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_200, groups = var_172, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_198, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_1_cast")]; + tensor input_23_cast = add(x = hidden_states_1_cast, y = temb_1_cast)[name = tensor("input_23_cast")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([2, 32, 10, 128, 128])]; + tensor reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_23_cast)[name = tensor("reshape_4_cast")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast)[name = tensor("reduce_mean_3_cast")]; + tensor sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast)[name = tensor("sub_2_cast")]; + tensor square_1_cast = square(x = sub_2_cast)[name = tensor("square_1_cast")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast)[name = tensor("reduce_mean_5_cast")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_2_cast = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast")]; + tensor sqrt_1_cast = sqrt(x = add_2_cast)[name = tensor("sqrt_1_cast")]; + tensor real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast)[name = tensor("real_div_1_cast")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([2, 320, 128, 128])]; + tensor reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast)[name = tensor("reshape_5_cast")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17283840)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17284544)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast)[name = tensor("add_3_cast")]; + tensor input_27_cast = silu(x = add_3_cast)[name = tensor("input_27_cast")]; + tensor var_210 = const()[name = tensor("op_210"), val = tensor([1, 1])]; + tensor var_212 = const()[name = tensor("op_212"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17285248)))]; + tensor down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19128512)))]; + tensor hidden_states_3_cast = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_212, groups = var_172, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_210, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_27_cast)[name = tensor("hidden_states_3_cast")]; + tensor input_29_cast = add(x = input_13_cast, y = hidden_states_3_cast)[name = tensor("input_29_cast")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([2, 32, 10, 128, 128])]; + tensor reshape_8_cast = reshape(shape = reshape_8_shape_0, x = input_29_cast)[name = tensor("reshape_8_cast")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast)[name = tensor("reduce_mean_6_cast")]; + tensor sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast)[name = tensor("sub_4_cast")]; + tensor square_2_cast = square(x = sub_4_cast)[name = tensor("square_2_cast")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast)[name = tensor("reduce_mean_8_cast")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_4_cast = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast")]; + tensor sqrt_2_cast = sqrt(x = add_4_cast)[name = tensor("sqrt_2_cast")]; + tensor real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast)[name = tensor("real_div_2_cast")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([2, 320, 128, 128])]; + tensor reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast)[name = tensor("reshape_9_cast")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19129216)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19129920)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast)[name = tensor("add_5_cast")]; + tensor input_33_cast = silu(x = add_5_cast)[name = tensor("input_33_cast")]; + tensor var_227 = const()[name = tensor("op_227"), val = tensor([1, 1])]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_type_0 = const()[name = tensor("hidden_states_5_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_5_pad_0 = const()[name = tensor("hidden_states_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19130624)))]; + tensor down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20973888)))]; + tensor hidden_states_5_cast = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_229, groups = var_172, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_227, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_33_cast)[name = tensor("hidden_states_5_cast")]; + tensor var_235 = const()[name = tensor("op_235"), val = tensor([1, 1])]; + tensor var_237 = const()[name = tensor("op_237"), val = tensor([1, 1])]; + tensor temb_3_pad_type_0 = const()[name = tensor("temb_3_pad_type_0"), val = tensor("custom")]; + tensor temb_3_pad_0 = const()[name = tensor("temb_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20974592)))]; + tensor down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21793856)))]; + tensor temb_3_cast = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_237, groups = var_172, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_235, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_3_cast")]; + tensor input_37_cast = add(x = hidden_states_5_cast, y = temb_3_cast)[name = tensor("input_37_cast")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([2, 32, 10, 128, 128])]; + tensor reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_37_cast)[name = tensor("reshape_12_cast")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast)[name = tensor("reduce_mean_9_cast")]; + tensor sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast)[name = tensor("sub_6_cast")]; + tensor square_3_cast = square(x = sub_6_cast)[name = tensor("square_3_cast")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast)[name = tensor("reduce_mean_11_cast")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_6_cast = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast")]; + tensor sqrt_3_cast = sqrt(x = add_6_cast)[name = tensor("sqrt_3_cast")]; + tensor real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast)[name = tensor("real_div_3_cast")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([2, 320, 128, 128])]; + tensor reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast)[name = tensor("reshape_13_cast")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21794560)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21795264)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast)[name = tensor("add_7_cast")]; + tensor input_41_cast = silu(x = add_7_cast)[name = tensor("input_41_cast")]; + tensor var_247 = const()[name = tensor("op_247"), val = tensor([1, 1])]; + tensor var_249 = const()[name = tensor("op_249"), val = tensor([1, 1])]; + tensor hidden_states_7_pad_type_0 = const()[name = tensor("hidden_states_7_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21795968)))]; + tensor down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23639232)))]; + tensor hidden_states_7_cast = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_249, groups = var_172, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_247, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_41_cast)[name = tensor("hidden_states_7_cast")]; + tensor input_43_cast = add(x = input_29_cast, y = hidden_states_7_cast)[name = tensor("input_43_cast")]; + tensor var_256 = const()[name = tensor("op_256"), val = tensor([2, 2])]; + tensor var_258 = const()[name = tensor("op_258"), val = tensor([1, 1])]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23639936)))]; + tensor down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25483200)))]; + tensor input_45_cast = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_258, groups = var_172, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_256, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = input_43_cast)[name = tensor("input_45_cast")]; + tensor var_266 = const()[name = tensor("op_266"), val = tensor(3)]; + tensor var_277 = const()[name = tensor("op_277"), val = tensor(true)]; + tensor var_282 = const()[name = tensor("op_282"), val = tensor(1)]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_45_cast)[name = tensor("reshape_16_cast")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12_cast = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast)[name = tensor("reduce_mean_12_cast")]; + tensor sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast)[name = tensor("sub_8_cast")]; + tensor square_4_cast = square(x = sub_8_cast)[name = tensor("square_4_cast")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14_cast = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast)[name = tensor("reduce_mean_14_cast")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_8_cast = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast")]; + tensor sqrt_4_cast = sqrt(x = add_8_cast)[name = tensor("sqrt_4_cast")]; + tensor real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast)[name = tensor("real_div_4_cast")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast)[name = tensor("reshape_17_cast")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25483904)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25484608)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast)[name = tensor("add_9_cast")]; + tensor input_49_cast = silu(x = add_9_cast)[name = tensor("input_49_cast")]; + tensor var_305 = const()[name = tensor("op_305"), val = tensor([1, 1])]; + tensor var_307 = const()[name = tensor("op_307"), val = tensor([1, 1])]; + tensor hidden_states_9_pad_type_0 = const()[name = tensor("hidden_states_9_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_9_pad_0 = const()[name = tensor("hidden_states_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25485312)))]; + tensor down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29171776)))]; + tensor hidden_states_9_cast = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_307, groups = var_282, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_305, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_49_cast)[name = tensor("hidden_states_9_cast")]; + tensor var_313 = const()[name = tensor("op_313"), val = tensor([1, 1])]; + tensor var_315 = const()[name = tensor("op_315"), val = tensor([1, 1])]; + tensor temb_5_pad_type_0 = const()[name = tensor("temb_5_pad_type_0"), val = tensor("custom")]; + tensor temb_5_pad_0 = const()[name = tensor("temb_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29173120)))]; + tensor down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30811584)))]; + tensor temb_5_cast = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_315, groups = var_282, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_313, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_5_cast")]; + tensor input_53_cast = add(x = hidden_states_9_cast, y = temb_5_cast)[name = tensor("input_53_cast")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_20_cast = reshape(shape = reshape_20_shape_0, x = input_53_cast)[name = tensor("reshape_20_cast")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15_cast = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast)[name = tensor("reduce_mean_15_cast")]; + tensor sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast)[name = tensor("sub_10_cast")]; + tensor square_5_cast = square(x = sub_10_cast)[name = tensor("square_5_cast")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17_cast = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast)[name = tensor("reduce_mean_17_cast")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_10_cast = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast")]; + tensor sqrt_5_cast = sqrt(x = add_10_cast)[name = tensor("sqrt_5_cast")]; + tensor real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast)[name = tensor("real_div_5_cast")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast)[name = tensor("reshape_21_cast")]; + tensor add_11_mean_0_to_fp16 = const()[name = tensor("add_11_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30812928)))]; + tensor add_11_variance_0_to_fp16 = const()[name = tensor("add_11_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30814272)))]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30815616)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30816960)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast)[name = tensor("add_11_cast")]; + tensor input_57_cast = silu(x = add_11_cast)[name = tensor("input_57_cast")]; + tensor var_325 = const()[name = tensor("op_325"), val = tensor([1, 1])]; + tensor var_327 = const()[name = tensor("op_327"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_type_0 = const()[name = tensor("hidden_states_11_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_11_pad_0 = const()[name = tensor("hidden_states_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30818304)))]; + tensor down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38191168)))]; + tensor hidden_states_11_cast = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_327, groups = var_282, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_325, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_57_cast)[name = tensor("hidden_states_11_cast")]; + tensor var_332 = const()[name = tensor("op_332"), val = tensor([1, 1])]; + tensor var_334 = const()[name = tensor("op_334"), val = tensor([1, 1])]; + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("custom")]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38192512)))]; + tensor down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38602176)))]; + tensor x_1_cast = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_334, groups = var_282, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_332, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_45_cast)[name = tensor("x_1_cast")]; + tensor hidden_states_13_cast = add(x = x_1_cast, y = hidden_states_11_cast)[name = tensor("hidden_states_13_cast")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_24_cast = reshape(shape = reshape_24_shape_0, x = hidden_states_13_cast)[name = tensor("reshape_24_cast")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18_cast = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast)[name = tensor("reduce_mean_18_cast")]; + tensor sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast)[name = tensor("sub_12_cast")]; + tensor square_6_cast = square(x = sub_12_cast)[name = tensor("square_6_cast")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20_cast = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast)[name = tensor("reduce_mean_20_cast")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_12_cast = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast")]; + tensor sqrt_6_cast = sqrt(x = add_12_cast)[name = tensor("sqrt_6_cast")]; + tensor real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast)[name = tensor("real_div_6_cast")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast)[name = tensor("reshape_25_cast")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38603520)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38604864)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_25_cast)[name = tensor("add_13_cast")]; + tensor var_356 = const()[name = tensor("op_356"), val = tensor([1, 1])]; + tensor var_358 = const()[name = tensor("op_358"), val = tensor([1, 1])]; + tensor hidden_states_15_pad_type_0 = const()[name = tensor("hidden_states_15_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_15_pad_0 = const()[name = tensor("hidden_states_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38606208)))]; + tensor down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39425472)))]; + tensor hidden_states_15_cast = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_358, groups = var_282, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = var_356, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_13_cast)[name = tensor("hidden_states_15_cast")]; + tensor var_363 = const()[name = tensor("op_363"), val = tensor([2, 640, 1, 4096])]; + tensor inputs_1_cast = reshape(shape = var_363, x = hidden_states_15_cast)[name = tensor("inputs_1_cast")]; + tensor var_373 = const()[name = tensor("op_373"), val = tensor([1])]; + tensor channels_mean_1_cast = reduce_mean(axes = var_373, keep_dims = var_277, x = inputs_1_cast)[name = tensor("channels_mean_1_cast")]; + tensor zero_mean_1_cast = sub(x = inputs_1_cast, y = channels_mean_1_cast)[name = tensor("zero_mean_1_cast")]; + tensor zero_mean_sq_1_cast = mul(x = zero_mean_1_cast, y = zero_mean_1_cast)[name = tensor("zero_mean_sq_1_cast")]; + tensor var_377 = const()[name = tensor("op_377"), val = tensor([1])]; + tensor var_378_cast = reduce_mean(axes = var_377, keep_dims = var_277, x = zero_mean_sq_1_cast)[name = tensor("op_378_cast")]; + tensor var_379_to_fp16 = const()[name = tensor("op_379_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_380_cast = add(x = var_378_cast, y = var_379_to_fp16)[name = tensor("op_380_cast")]; + tensor denom_1_epsilon_0_to_fp16 = const()[name = tensor("denom_1_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_1_cast = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_380_cast)[name = tensor("denom_1_cast")]; + tensor out_1_cast = mul(x = zero_mean_1_cast, y = denom_1_cast)[name = tensor("out_1_cast")]; + tensor var_384_to_fp16 = const()[name = tensor("op_384_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39426816)))]; + tensor var_385_cast = add(x = out_1_cast, y = var_384_to_fp16)[name = tensor("op_385_cast")]; + tensor var_387_to_fp16 = const()[name = tensor("op_387_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39428160)))]; + tensor hidden_states_17_cast = mul(x = var_385_cast, y = var_387_to_fp16)[name = tensor("hidden_states_17_cast")]; + tensor var_394 = const()[name = tensor("op_394"), val = tensor([1, 1])]; + tensor var_396 = const()[name = tensor("op_396"), val = tensor([1, 1])]; + tensor q_1_pad_type_0 = const()[name = tensor("q_1_pad_type_0"), val = tensor("custom")]; + tensor q_1_pad_0 = const()[name = tensor("q_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39429504)))]; + tensor q_1_cast = conv(dilations = var_396, groups = var_282, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_394, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_17_cast)[name = tensor("q_1_cast")]; + tensor var_400 = const()[name = tensor("op_400"), val = tensor([1, 1])]; + tensor var_402 = const()[name = tensor("op_402"), val = tensor([1, 1])]; + tensor k_1_pad_type_0 = const()[name = tensor("k_1_pad_type_0"), val = tensor("custom")]; + tensor k_1_pad_0 = const()[name = tensor("k_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40248768)))]; + tensor k_1_cast = conv(dilations = var_402, groups = var_282, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_400, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_17_cast)[name = tensor("k_1_cast")]; + tensor var_406 = const()[name = tensor("op_406"), val = tensor([1, 1])]; + tensor var_408 = const()[name = tensor("op_408"), val = tensor([1, 1])]; + tensor v_1_pad_type_0 = const()[name = tensor("v_1_pad_type_0"), val = tensor("custom")]; + tensor v_1_pad_0 = const()[name = tensor("v_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41068032)))]; + tensor v_1_cast = conv(dilations = var_408, groups = var_282, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_406, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_17_cast)[name = tensor("v_1_cast")]; + tensor var_412 = const()[name = tensor("op_412"), val = tensor([2, 10, 64, -1])]; + tensor var_413_cast = reshape(shape = var_412, x = q_1_cast)[name = tensor("op_413_cast")]; + tensor var_414 = const()[name = tensor("op_414"), val = tensor([2, 10, 64, -1])]; + tensor var_415_cast = reshape(shape = var_414, x = k_1_cast)[name = tensor("op_415_cast")]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([2, 10, 64, -1])]; + tensor var_417_cast = reshape(shape = var_416, x = v_1_cast)[name = tensor("op_417_cast")]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_413_cast, y = var_415_cast)[name = tensor("attn_weights_1_cast")]; + tensor var_273_to_fp16 = const()[name = tensor("op_273_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_3_cast = mul(x = attn_weights_1_cast, y = var_273_to_fp16)[name = tensor("attn_weights_3_cast")]; + tensor var_421_cast = softmax(axis = var_266, x = attn_weights_3_cast)[name = tensor("op_421_cast")]; + tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; + tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; + tensor attn_1_cast = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_417_cast, y = var_421_cast)[name = tensor("attn_1_cast")]; + tensor var_425 = const()[name = tensor("op_425"), val = tensor([2, 640, 1, -1])]; + tensor input_61_cast = reshape(shape = var_425, x = attn_1_cast)[name = tensor("input_61_cast")]; + tensor var_430 = const()[name = tensor("op_430"), val = tensor([1, 1])]; + tensor var_432 = const()[name = tensor("op_432"), val = tensor([1, 1])]; + tensor var_434_pad_type_0 = const()[name = tensor("op_434_pad_type_0"), val = tensor("custom")]; + tensor var_434_pad_0 = const()[name = tensor("op_434_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41887296)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42706560)))]; + tensor var_434_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_432, groups = var_282, pad = var_434_pad_0, pad_type = var_434_pad_type_0, strides = var_430, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_61_cast)[name = tensor("op_434_cast")]; + tensor inputs_3_cast = add(x = var_434_cast, y = inputs_1_cast)[name = tensor("inputs_3_cast")]; + tensor var_438 = const()[name = tensor("op_438"), val = tensor([1])]; + tensor channels_mean_3_cast = reduce_mean(axes = var_438, keep_dims = var_277, x = inputs_3_cast)[name = tensor("channels_mean_3_cast")]; + tensor zero_mean_3_cast = sub(x = inputs_3_cast, y = channels_mean_3_cast)[name = tensor("zero_mean_3_cast")]; + tensor zero_mean_sq_3_cast = mul(x = zero_mean_3_cast, y = zero_mean_3_cast)[name = tensor("zero_mean_sq_3_cast")]; + tensor var_442 = const()[name = tensor("op_442"), val = tensor([1])]; + tensor var_443_cast = reduce_mean(axes = var_442, keep_dims = var_277, x = zero_mean_sq_3_cast)[name = tensor("op_443_cast")]; + tensor var_444_to_fp16 = const()[name = tensor("op_444_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_445_cast = add(x = var_443_cast, y = var_444_to_fp16)[name = tensor("op_445_cast")]; + tensor denom_3_epsilon_0_to_fp16 = const()[name = tensor("denom_3_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_3_cast = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_445_cast)[name = tensor("denom_3_cast")]; + tensor out_3_cast = mul(x = zero_mean_3_cast, y = denom_3_cast)[name = tensor("out_3_cast")]; + tensor var_449_to_fp16 = const()[name = tensor("op_449_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42707904)))]; + tensor var_450_cast = add(x = out_3_cast, y = var_449_to_fp16)[name = tensor("op_450_cast")]; + tensor var_452_to_fp16 = const()[name = tensor("op_452_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42709248)))]; + tensor hidden_states_19_cast = mul(x = var_450_cast, y = var_452_to_fp16)[name = tensor("hidden_states_19_cast")]; + tensor var_459 = const()[name = tensor("op_459"), val = tensor([1, 1])]; + tensor var_461 = const()[name = tensor("op_461"), val = tensor([1, 1])]; + tensor q_3_pad_type_0 = const()[name = tensor("q_3_pad_type_0"), val = tensor("custom")]; + tensor q_3_pad_0 = const()[name = tensor("q_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42710592)))]; + tensor q_3_cast = conv(dilations = var_461, groups = var_282, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_459, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_19_cast)[name = tensor("q_3_cast")]; + tensor var_465 = const()[name = tensor("op_465"), val = tensor([1, 1])]; + tensor var_467 = const()[name = tensor("op_467"), val = tensor([1, 1])]; + tensor k_3_pad_type_0 = const()[name = tensor("k_3_pad_type_0"), val = tensor("custom")]; + tensor k_3_pad_0 = const()[name = tensor("k_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43529856)))]; + tensor k_3_cast = conv(dilations = var_467, groups = var_282, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_465, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_3_cast")]; + tensor var_471 = const()[name = tensor("op_471"), val = tensor([1, 1])]; + tensor var_473 = const()[name = tensor("op_473"), val = tensor([1, 1])]; + tensor v_3_pad_type_0 = const()[name = tensor("v_3_pad_type_0"), val = tensor("custom")]; + tensor v_3_pad_0 = const()[name = tensor("v_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46151360)))]; + tensor v_3_cast = conv(dilations = var_473, groups = var_282, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_471, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_3_cast")]; + tensor var_477 = const()[name = tensor("op_477"), val = tensor([2, 10, 64, -1])]; + tensor var_478_cast = reshape(shape = var_477, x = q_3_cast)[name = tensor("op_478_cast")]; + tensor var_479 = const()[name = tensor("op_479"), val = tensor([2, 10, 64, -1])]; + tensor var_480_cast = reshape(shape = var_479, x = k_3_cast)[name = tensor("op_480_cast")]; + tensor var_481 = const()[name = tensor("op_481"), val = tensor([2, 10, 64, -1])]; + tensor var_482_cast = reshape(shape = var_481, x = v_3_cast)[name = tensor("op_482_cast")]; + tensor attn_weights_5_transpose_x_0 = const()[name = tensor("attn_weights_5_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_5_transpose_y_0 = const()[name = tensor("attn_weights_5_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_5_cast = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_478_cast, y = var_480_cast)[name = tensor("attn_weights_5_cast")]; + tensor attn_weights_7_cast = mul(x = attn_weights_5_cast, y = var_273_to_fp16)[name = tensor("attn_weights_7_cast")]; + tensor var_486_cast = softmax(axis = var_266, x = attn_weights_7_cast)[name = tensor("op_486_cast")]; + tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; + tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; + tensor attn_3_cast = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_482_cast, y = var_486_cast)[name = tensor("attn_3_cast")]; + tensor var_490 = const()[name = tensor("op_490"), val = tensor([2, 640, 1, -1])]; + tensor input_63_cast = reshape(shape = var_490, x = attn_3_cast)[name = tensor("input_63_cast")]; + tensor var_495 = const()[name = tensor("op_495"), val = tensor([1, 1])]; + tensor var_497 = const()[name = tensor("op_497"), val = tensor([1, 1])]; + tensor var_499_pad_type_0 = const()[name = tensor("op_499_pad_type_0"), val = tensor("custom")]; + tensor var_499_pad_0 = const()[name = tensor("op_499_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48772864)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49592128)))]; + tensor var_499_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_497, groups = var_282, pad = var_499_pad_0, pad_type = var_499_pad_type_0, strides = var_495, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_63_cast)[name = tensor("op_499_cast")]; + tensor inputs_5_cast = add(x = var_499_cast, y = inputs_3_cast)[name = tensor("inputs_5_cast")]; + tensor var_503 = const()[name = tensor("op_503"), val = tensor([1])]; + tensor channels_mean_5_cast = reduce_mean(axes = var_503, keep_dims = var_277, x = inputs_5_cast)[name = tensor("channels_mean_5_cast")]; + tensor zero_mean_5_cast = sub(x = inputs_5_cast, y = channels_mean_5_cast)[name = tensor("zero_mean_5_cast")]; + tensor zero_mean_sq_5_cast = mul(x = zero_mean_5_cast, y = zero_mean_5_cast)[name = tensor("zero_mean_sq_5_cast")]; + tensor var_507 = const()[name = tensor("op_507"), val = tensor([1])]; + tensor var_508_cast = reduce_mean(axes = var_507, keep_dims = var_277, x = zero_mean_sq_5_cast)[name = tensor("op_508_cast")]; + tensor var_509_to_fp16 = const()[name = tensor("op_509_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_510_cast = add(x = var_508_cast, y = var_509_to_fp16)[name = tensor("op_510_cast")]; + tensor denom_5_epsilon_0_to_fp16 = const()[name = tensor("denom_5_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_5_cast = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_510_cast)[name = tensor("denom_5_cast")]; + tensor out_5_cast = mul(x = zero_mean_5_cast, y = denom_5_cast)[name = tensor("out_5_cast")]; + tensor var_514_to_fp16 = const()[name = tensor("op_514_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49593472)))]; + tensor var_515_cast = add(x = out_5_cast, y = var_514_to_fp16)[name = tensor("op_515_cast")]; + tensor var_517_to_fp16 = const()[name = tensor("op_517_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49594816)))]; + tensor input_65_cast = mul(x = var_515_cast, y = var_517_to_fp16)[name = tensor("input_65_cast")]; + tensor var_525 = const()[name = tensor("op_525"), val = tensor([1, 1])]; + tensor var_527 = const()[name = tensor("op_527"), val = tensor([1, 1])]; + tensor var_529_pad_type_0 = const()[name = tensor("op_529_pad_type_0"), val = tensor("custom")]; + tensor var_529_pad_0 = const()[name = tensor("op_529_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49596160)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56149824)))]; + tensor var_529_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_527, groups = var_282, pad = var_529_pad_0, pad_type = var_529_pad_type_0, strides = var_525, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_65_cast)[name = tensor("op_529_cast")]; + tensor var_530_split_sizes_0 = const()[name = tensor("op_530_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_530_axis_0 = const()[name = tensor("op_530_axis_0"), val = tensor(1)]; + tensor var_530_cast_0, tensor var_530_cast_1 = split(axis = var_530_axis_0, split_sizes = var_530_split_sizes_0, x = var_529_cast)[name = tensor("op_530_cast")]; + tensor var_532_mode_0 = const()[name = tensor("op_532_mode_0"), val = tensor("EXACT")]; + tensor var_532_cast = gelu(mode = var_532_mode_0, x = var_530_cast_1)[name = tensor("op_532_cast")]; + tensor input_67_cast = mul(x = var_530_cast_0, y = var_532_cast)[name = tensor("input_67_cast")]; + tensor var_536 = const()[name = tensor("op_536"), val = tensor([1, 1])]; + tensor var_538 = const()[name = tensor("op_538"), val = tensor([1, 1])]; + tensor var_540_pad_type_0 = const()[name = tensor("op_540_pad_type_0"), val = tensor("custom")]; + tensor var_540_pad_0 = const()[name = tensor("op_540_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56160128)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59436992)))]; + tensor var_540_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_538, groups = var_282, pad = var_540_pad_0, pad_type = var_540_pad_type_0, strides = var_536, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_67_cast)[name = tensor("op_540_cast")]; + tensor inputs_7_cast = add(x = var_540_cast, y = inputs_5_cast)[name = tensor("inputs_7_cast")]; + tensor var_550 = const()[name = tensor("op_550"), val = tensor([1])]; + tensor channels_mean_7_cast = reduce_mean(axes = var_550, keep_dims = var_277, x = inputs_7_cast)[name = tensor("channels_mean_7_cast")]; + tensor zero_mean_7_cast = sub(x = inputs_7_cast, y = channels_mean_7_cast)[name = tensor("zero_mean_7_cast")]; + tensor zero_mean_sq_7_cast = mul(x = zero_mean_7_cast, y = zero_mean_7_cast)[name = tensor("zero_mean_sq_7_cast")]; + tensor var_554 = const()[name = tensor("op_554"), val = tensor([1])]; + tensor var_555_cast = reduce_mean(axes = var_554, keep_dims = var_277, x = zero_mean_sq_7_cast)[name = tensor("op_555_cast")]; + tensor var_556_to_fp16 = const()[name = tensor("op_556_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_557_cast = add(x = var_555_cast, y = var_556_to_fp16)[name = tensor("op_557_cast")]; + tensor denom_7_epsilon_0_to_fp16 = const()[name = tensor("denom_7_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_7_cast = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_557_cast)[name = tensor("denom_7_cast")]; + tensor out_7_cast = mul(x = zero_mean_7_cast, y = denom_7_cast)[name = tensor("out_7_cast")]; + tensor var_561_to_fp16 = const()[name = tensor("op_561_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59438336)))]; + tensor var_562_cast = add(x = out_7_cast, y = var_561_to_fp16)[name = tensor("op_562_cast")]; + tensor var_564_to_fp16 = const()[name = tensor("op_564_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59439680)))]; + tensor hidden_states_23_cast = mul(x = var_562_cast, y = var_564_to_fp16)[name = tensor("hidden_states_23_cast")]; + tensor var_571 = const()[name = tensor("op_571"), val = tensor([1, 1])]; + tensor var_573 = const()[name = tensor("op_573"), val = tensor([1, 1])]; + tensor q_5_pad_type_0 = const()[name = tensor("q_5_pad_type_0"), val = tensor("custom")]; + tensor q_5_pad_0 = const()[name = tensor("q_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59441024)))]; + tensor q_5_cast = conv(dilations = var_573, groups = var_282, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_571, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("q_5_cast")]; + tensor var_577 = const()[name = tensor("op_577"), val = tensor([1, 1])]; + tensor var_579 = const()[name = tensor("op_579"), val = tensor([1, 1])]; + tensor k_5_pad_type_0 = const()[name = tensor("k_5_pad_type_0"), val = tensor("custom")]; + tensor k_5_pad_0 = const()[name = tensor("k_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60260288)))]; + tensor k_5_cast = conv(dilations = var_579, groups = var_282, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_577, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("k_5_cast")]; + tensor var_583 = const()[name = tensor("op_583"), val = tensor([1, 1])]; + tensor var_585 = const()[name = tensor("op_585"), val = tensor([1, 1])]; + tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("custom")]; + tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61079552)))]; + tensor v_5_cast = conv(dilations = var_585, groups = var_282, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_583, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("v_5_cast")]; + tensor var_589 = const()[name = tensor("op_589"), val = tensor([2, 10, 64, -1])]; + tensor var_590_cast = reshape(shape = var_589, x = q_5_cast)[name = tensor("op_590_cast")]; + tensor var_591 = const()[name = tensor("op_591"), val = tensor([2, 10, 64, -1])]; + tensor var_592_cast = reshape(shape = var_591, x = k_5_cast)[name = tensor("op_592_cast")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([2, 10, 64, -1])]; + tensor var_594_cast = reshape(shape = var_593, x = v_5_cast)[name = tensor("op_594_cast")]; + tensor attn_weights_9_transpose_x_0 = const()[name = tensor("attn_weights_9_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_9_transpose_y_0 = const()[name = tensor("attn_weights_9_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_9_cast = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_590_cast, y = var_592_cast)[name = tensor("attn_weights_9_cast")]; + tensor attn_weights_11_cast = mul(x = attn_weights_9_cast, y = var_273_to_fp16)[name = tensor("attn_weights_11_cast")]; + tensor var_598_cast = softmax(axis = var_266, x = attn_weights_11_cast)[name = tensor("op_598_cast")]; + tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; + tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; + tensor attn_5_cast = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_594_cast, y = var_598_cast)[name = tensor("attn_5_cast")]; + tensor var_602 = const()[name = tensor("op_602"), val = tensor([2, 640, 1, -1])]; + tensor input_69_cast = reshape(shape = var_602, x = attn_5_cast)[name = tensor("input_69_cast")]; + tensor var_607 = const()[name = tensor("op_607"), val = tensor([1, 1])]; + tensor var_609 = const()[name = tensor("op_609"), val = tensor([1, 1])]; + tensor var_611_pad_type_0 = const()[name = tensor("op_611_pad_type_0"), val = tensor("custom")]; + tensor var_611_pad_0 = const()[name = tensor("op_611_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61898816)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62718080)))]; + tensor var_611_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_609, groups = var_282, pad = var_611_pad_0, pad_type = var_611_pad_type_0, strides = var_607, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_69_cast)[name = tensor("op_611_cast")]; + tensor inputs_9_cast = add(x = var_611_cast, y = inputs_7_cast)[name = tensor("inputs_9_cast")]; + tensor var_615 = const()[name = tensor("op_615"), val = tensor([1])]; + tensor channels_mean_9_cast = reduce_mean(axes = var_615, keep_dims = var_277, x = inputs_9_cast)[name = tensor("channels_mean_9_cast")]; + tensor zero_mean_9_cast = sub(x = inputs_9_cast, y = channels_mean_9_cast)[name = tensor("zero_mean_9_cast")]; + tensor zero_mean_sq_9_cast = mul(x = zero_mean_9_cast, y = zero_mean_9_cast)[name = tensor("zero_mean_sq_9_cast")]; + tensor var_619 = const()[name = tensor("op_619"), val = tensor([1])]; + tensor var_620_cast = reduce_mean(axes = var_619, keep_dims = var_277, x = zero_mean_sq_9_cast)[name = tensor("op_620_cast")]; + tensor var_621_to_fp16 = const()[name = tensor("op_621_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_622_cast = add(x = var_620_cast, y = var_621_to_fp16)[name = tensor("op_622_cast")]; + tensor denom_9_epsilon_0_to_fp16 = const()[name = tensor("denom_9_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_9_cast = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_622_cast)[name = tensor("denom_9_cast")]; + tensor out_9_cast = mul(x = zero_mean_9_cast, y = denom_9_cast)[name = tensor("out_9_cast")]; + tensor var_626_to_fp16 = const()[name = tensor("op_626_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62719424)))]; + tensor var_627_cast = add(x = out_9_cast, y = var_626_to_fp16)[name = tensor("op_627_cast")]; + tensor var_629_to_fp16 = const()[name = tensor("op_629_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62720768)))]; + tensor hidden_states_25_cast = mul(x = var_627_cast, y = var_629_to_fp16)[name = tensor("hidden_states_25_cast")]; + tensor var_636 = const()[name = tensor("op_636"), val = tensor([1, 1])]; + tensor var_638 = const()[name = tensor("op_638"), val = tensor([1, 1])]; + tensor q_7_pad_type_0 = const()[name = tensor("q_7_pad_type_0"), val = tensor("custom")]; + tensor q_7_pad_0 = const()[name = tensor("q_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62722112)))]; + tensor q_7_cast = conv(dilations = var_638, groups = var_282, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_636, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_25_cast)[name = tensor("q_7_cast")]; + tensor var_642 = const()[name = tensor("op_642"), val = tensor([1, 1])]; + tensor var_644 = const()[name = tensor("op_644"), val = tensor([1, 1])]; + tensor k_7_pad_type_0 = const()[name = tensor("k_7_pad_type_0"), val = tensor("custom")]; + tensor k_7_pad_0 = const()[name = tensor("k_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63541376)))]; + tensor k_7_cast = conv(dilations = var_644, groups = var_282, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_642, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_7_cast")]; + tensor var_648 = const()[name = tensor("op_648"), val = tensor([1, 1])]; + tensor var_650 = const()[name = tensor("op_650"), val = tensor([1, 1])]; + tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("custom")]; + tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66162880)))]; + tensor v_7_cast = conv(dilations = var_650, groups = var_282, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_648, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_7_cast")]; + tensor var_654 = const()[name = tensor("op_654"), val = tensor([2, 10, 64, -1])]; + tensor var_655_cast = reshape(shape = var_654, x = q_7_cast)[name = tensor("op_655_cast")]; + tensor var_656 = const()[name = tensor("op_656"), val = tensor([2, 10, 64, -1])]; + tensor var_657_cast = reshape(shape = var_656, x = k_7_cast)[name = tensor("op_657_cast")]; + tensor var_658 = const()[name = tensor("op_658"), val = tensor([2, 10, 64, -1])]; + tensor var_659_cast = reshape(shape = var_658, x = v_7_cast)[name = tensor("op_659_cast")]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_655_cast, y = var_657_cast)[name = tensor("attn_weights_13_cast")]; + tensor attn_weights_15_cast = mul(x = attn_weights_13_cast, y = var_273_to_fp16)[name = tensor("attn_weights_15_cast")]; + tensor var_663_cast = softmax(axis = var_266, x = attn_weights_15_cast)[name = tensor("op_663_cast")]; + tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; + tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; + tensor attn_7_cast = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_659_cast, y = var_663_cast)[name = tensor("attn_7_cast")]; + tensor var_667 = const()[name = tensor("op_667"), val = tensor([2, 640, 1, -1])]; + tensor input_71_cast = reshape(shape = var_667, x = attn_7_cast)[name = tensor("input_71_cast")]; + tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 1])]; + tensor var_674 = const()[name = tensor("op_674"), val = tensor([1, 1])]; + tensor var_676_pad_type_0 = const()[name = tensor("op_676_pad_type_0"), val = tensor("custom")]; + tensor var_676_pad_0 = const()[name = tensor("op_676_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68784384)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69603648)))]; + tensor var_676_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_674, groups = var_282, pad = var_676_pad_0, pad_type = var_676_pad_type_0, strides = var_672, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_71_cast)[name = tensor("op_676_cast")]; + tensor inputs_11_cast = add(x = var_676_cast, y = inputs_9_cast)[name = tensor("inputs_11_cast")]; + tensor var_680 = const()[name = tensor("op_680"), val = tensor([1])]; + tensor channels_mean_11_cast = reduce_mean(axes = var_680, keep_dims = var_277, x = inputs_11_cast)[name = tensor("channels_mean_11_cast")]; + tensor zero_mean_11_cast = sub(x = inputs_11_cast, y = channels_mean_11_cast)[name = tensor("zero_mean_11_cast")]; + tensor zero_mean_sq_11_cast = mul(x = zero_mean_11_cast, y = zero_mean_11_cast)[name = tensor("zero_mean_sq_11_cast")]; + tensor var_684 = const()[name = tensor("op_684"), val = tensor([1])]; + tensor var_685_cast = reduce_mean(axes = var_684, keep_dims = var_277, x = zero_mean_sq_11_cast)[name = tensor("op_685_cast")]; + tensor var_686_to_fp16 = const()[name = tensor("op_686_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_687_cast = add(x = var_685_cast, y = var_686_to_fp16)[name = tensor("op_687_cast")]; + tensor denom_11_epsilon_0_to_fp16 = const()[name = tensor("denom_11_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_11_cast = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_687_cast)[name = tensor("denom_11_cast")]; + tensor out_11_cast = mul(x = zero_mean_11_cast, y = denom_11_cast)[name = tensor("out_11_cast")]; + tensor var_691_to_fp16 = const()[name = tensor("op_691_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69604992)))]; + tensor var_692_cast = add(x = out_11_cast, y = var_691_to_fp16)[name = tensor("op_692_cast")]; + tensor var_694_to_fp16 = const()[name = tensor("op_694_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69606336)))]; + tensor input_73_cast = mul(x = var_692_cast, y = var_694_to_fp16)[name = tensor("input_73_cast")]; + tensor var_702 = const()[name = tensor("op_702"), val = tensor([1, 1])]; + tensor var_704 = const()[name = tensor("op_704"), val = tensor([1, 1])]; + tensor var_706_pad_type_0 = const()[name = tensor("op_706_pad_type_0"), val = tensor("custom")]; + tensor var_706_pad_0 = const()[name = tensor("op_706_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69607680)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76161344)))]; + tensor var_706_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_704, groups = var_282, pad = var_706_pad_0, pad_type = var_706_pad_type_0, strides = var_702, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_73_cast)[name = tensor("op_706_cast")]; + tensor var_707_split_sizes_0 = const()[name = tensor("op_707_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_707_axis_0 = const()[name = tensor("op_707_axis_0"), val = tensor(1)]; + tensor var_707_cast_0, tensor var_707_cast_1 = split(axis = var_707_axis_0, split_sizes = var_707_split_sizes_0, x = var_706_cast)[name = tensor("op_707_cast")]; + tensor var_709_mode_0 = const()[name = tensor("op_709_mode_0"), val = tensor("EXACT")]; + tensor var_709_cast = gelu(mode = var_709_mode_0, x = var_707_cast_1)[name = tensor("op_709_cast")]; + tensor input_75_cast = mul(x = var_707_cast_0, y = var_709_cast)[name = tensor("input_75_cast")]; + tensor var_713 = const()[name = tensor("op_713"), val = tensor([1, 1])]; + tensor var_715 = const()[name = tensor("op_715"), val = tensor([1, 1])]; + tensor var_717_pad_type_0 = const()[name = tensor("op_717_pad_type_0"), val = tensor("custom")]; + tensor var_717_pad_0 = const()[name = tensor("op_717_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76171648)))]; + tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79448512)))]; + tensor var_717_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_715, groups = var_282, pad = var_717_pad_0, pad_type = var_717_pad_type_0, strides = var_713, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_75_cast)[name = tensor("op_717_cast")]; + tensor hidden_states_29_cast = add(x = var_717_cast, y = inputs_11_cast)[name = tensor("hidden_states_29_cast")]; + tensor var_719 = const()[name = tensor("op_719"), val = tensor([2, 640, 64, 64])]; + tensor input_77_cast = reshape(shape = var_719, x = hidden_states_29_cast)[name = tensor("input_77_cast")]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, 1])]; + tensor var_725 = const()[name = tensor("op_725"), val = tensor([1, 1])]; + tensor hidden_states_31_pad_type_0 = const()[name = tensor("hidden_states_31_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79449856)))]; + tensor down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80269120)))]; + tensor hidden_states_31_cast = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_725, groups = var_282, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_723, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_77_cast)[name = tensor("hidden_states_31_cast")]; + tensor input_79_cast = add(x = hidden_states_31_cast, y = hidden_states_13_cast)[name = tensor("input_79_cast")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_79_cast)[name = tensor("reshape_28_cast")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21_cast = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast)[name = tensor("reduce_mean_21_cast")]; + tensor sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast)[name = tensor("sub_14_cast")]; + tensor square_7_cast = square(x = sub_14_cast)[name = tensor("square_7_cast")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23_cast = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast)[name = tensor("reduce_mean_23_cast")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_14_cast = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast")]; + tensor sqrt_7_cast = sqrt(x = add_14_cast)[name = tensor("sqrt_7_cast")]; + tensor real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast)[name = tensor("real_div_7_cast")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast)[name = tensor("reshape_29_cast")]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80270464)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80271808)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_29_cast)[name = tensor("add_15_cast")]; + tensor input_83_cast = silu(x = add_15_cast)[name = tensor("input_83_cast")]; + tensor var_740 = const()[name = tensor("op_740"), val = tensor([1, 1])]; + tensor var_742 = const()[name = tensor("op_742"), val = tensor([1, 1])]; + tensor hidden_states_33_pad_type_0 = const()[name = tensor("hidden_states_33_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_33_pad_0 = const()[name = tensor("hidden_states_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80273152)))]; + tensor down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87646016)))]; + tensor hidden_states_33_cast = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_742, groups = var_282, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = var_740, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_83_cast)[name = tensor("hidden_states_33_cast")]; + tensor var_748 = const()[name = tensor("op_748"), val = tensor([1, 1])]; + tensor var_750 = const()[name = tensor("op_750"), val = tensor([1, 1])]; + tensor temb_7_pad_type_0 = const()[name = tensor("temb_7_pad_type_0"), val = tensor("custom")]; + tensor temb_7_pad_0 = const()[name = tensor("temb_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87647360)))]; + tensor down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89285824)))]; + tensor temb_7_cast = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_750, groups = var_282, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_748, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_7_cast")]; + tensor input_87_cast = add(x = hidden_states_33_cast, y = temb_7_cast)[name = tensor("input_87_cast")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_32_cast = reshape(shape = reshape_32_shape_0, x = input_87_cast)[name = tensor("reshape_32_cast")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24_cast = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast)[name = tensor("reduce_mean_24_cast")]; + tensor sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast)[name = tensor("sub_16_cast")]; + tensor square_8_cast = square(x = sub_16_cast)[name = tensor("square_8_cast")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26_cast = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast)[name = tensor("reduce_mean_26_cast")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_16_cast = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast")]; + tensor sqrt_8_cast = sqrt(x = add_16_cast)[name = tensor("sqrt_8_cast")]; + tensor real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast)[name = tensor("real_div_8_cast")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast)[name = tensor("reshape_33_cast")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89287168)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89288512)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_33_cast)[name = tensor("add_17_cast")]; + tensor input_91_cast = silu(x = add_17_cast)[name = tensor("input_91_cast")]; + tensor var_760 = const()[name = tensor("op_760"), val = tensor([1, 1])]; + tensor var_762 = const()[name = tensor("op_762"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89289856)))]; + tensor down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96662720)))]; + tensor hidden_states_35_cast = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_762, groups = var_282, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_760, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_91_cast)[name = tensor("hidden_states_35_cast")]; + tensor hidden_states_37_cast = add(x = input_79_cast, y = hidden_states_35_cast)[name = tensor("hidden_states_37_cast")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_36_cast = reshape(shape = reshape_36_shape_0, x = hidden_states_37_cast)[name = tensor("reshape_36_cast")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27_cast = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast)[name = tensor("reduce_mean_27_cast")]; + tensor sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast)[name = tensor("sub_18_cast")]; + tensor square_9_cast = square(x = sub_18_cast)[name = tensor("square_9_cast")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29_cast = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast)[name = tensor("reduce_mean_29_cast")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_18_cast = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast")]; + tensor sqrt_9_cast = sqrt(x = add_18_cast)[name = tensor("sqrt_9_cast")]; + tensor real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast)[name = tensor("real_div_9_cast")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast)[name = tensor("reshape_37_cast")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96664064)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96665408)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_37_cast)[name = tensor("add_19_cast")]; + tensor var_784 = const()[name = tensor("op_784"), val = tensor([1, 1])]; + tensor var_786 = const()[name = tensor("op_786"), val = tensor([1, 1])]; + tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96666752)))]; + tensor down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97486016)))]; + tensor hidden_states_39_cast = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_786, groups = var_282, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_784, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_19_cast)[name = tensor("hidden_states_39_cast")]; + tensor var_791 = const()[name = tensor("op_791"), val = tensor([2, 640, 1, 4096])]; + tensor inputs_13_cast = reshape(shape = var_791, x = hidden_states_39_cast)[name = tensor("inputs_13_cast")]; + tensor var_801 = const()[name = tensor("op_801"), val = tensor([1])]; + tensor channels_mean_13_cast = reduce_mean(axes = var_801, keep_dims = var_277, x = inputs_13_cast)[name = tensor("channels_mean_13_cast")]; + tensor zero_mean_13_cast = sub(x = inputs_13_cast, y = channels_mean_13_cast)[name = tensor("zero_mean_13_cast")]; + tensor zero_mean_sq_13_cast = mul(x = zero_mean_13_cast, y = zero_mean_13_cast)[name = tensor("zero_mean_sq_13_cast")]; + tensor var_805 = const()[name = tensor("op_805"), val = tensor([1])]; + tensor var_806_cast = reduce_mean(axes = var_805, keep_dims = var_277, x = zero_mean_sq_13_cast)[name = tensor("op_806_cast")]; + tensor var_807_to_fp16 = const()[name = tensor("op_807_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_808_cast = add(x = var_806_cast, y = var_807_to_fp16)[name = tensor("op_808_cast")]; + tensor denom_13_epsilon_0_to_fp16 = const()[name = tensor("denom_13_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_13_cast = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_808_cast)[name = tensor("denom_13_cast")]; + tensor out_13_cast = mul(x = zero_mean_13_cast, y = denom_13_cast)[name = tensor("out_13_cast")]; + tensor var_812_to_fp16 = const()[name = tensor("op_812_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97487360)))]; + tensor var_813_cast = add(x = out_13_cast, y = var_812_to_fp16)[name = tensor("op_813_cast")]; + tensor var_815_to_fp16 = const()[name = tensor("op_815_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97488704)))]; + tensor hidden_states_41_cast = mul(x = var_813_cast, y = var_815_to_fp16)[name = tensor("hidden_states_41_cast")]; + tensor var_822 = const()[name = tensor("op_822"), val = tensor([1, 1])]; + tensor var_824 = const()[name = tensor("op_824"), val = tensor([1, 1])]; + tensor q_9_pad_type_0 = const()[name = tensor("q_9_pad_type_0"), val = tensor("custom")]; + tensor q_9_pad_0 = const()[name = tensor("q_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97490048)))]; + tensor q_9_cast = conv(dilations = var_824, groups = var_282, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_822, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_41_cast)[name = tensor("q_9_cast")]; + tensor var_828 = const()[name = tensor("op_828"), val = tensor([1, 1])]; + tensor var_830 = const()[name = tensor("op_830"), val = tensor([1, 1])]; + tensor k_9_pad_type_0 = const()[name = tensor("k_9_pad_type_0"), val = tensor("custom")]; + tensor k_9_pad_0 = const()[name = tensor("k_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98309312)))]; + tensor k_9_cast = conv(dilations = var_830, groups = var_282, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_828, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_41_cast)[name = tensor("k_9_cast")]; + tensor var_834 = const()[name = tensor("op_834"), val = tensor([1, 1])]; + tensor var_836 = const()[name = tensor("op_836"), val = tensor([1, 1])]; + tensor v_9_pad_type_0 = const()[name = tensor("v_9_pad_type_0"), val = tensor("custom")]; + tensor v_9_pad_0 = const()[name = tensor("v_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99128576)))]; + tensor v_9_cast = conv(dilations = var_836, groups = var_282, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_834, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_41_cast)[name = tensor("v_9_cast")]; + tensor var_840 = const()[name = tensor("op_840"), val = tensor([2, 10, 64, -1])]; + tensor var_841_cast = reshape(shape = var_840, x = q_9_cast)[name = tensor("op_841_cast")]; + tensor var_842 = const()[name = tensor("op_842"), val = tensor([2, 10, 64, -1])]; + tensor var_843_cast = reshape(shape = var_842, x = k_9_cast)[name = tensor("op_843_cast")]; + tensor var_844 = const()[name = tensor("op_844"), val = tensor([2, 10, 64, -1])]; + tensor var_845_cast = reshape(shape = var_844, x = v_9_cast)[name = tensor("op_845_cast")]; + tensor attn_weights_17_transpose_x_0 = const()[name = tensor("attn_weights_17_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_17_transpose_y_0 = const()[name = tensor("attn_weights_17_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_17_cast = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_841_cast, y = var_843_cast)[name = tensor("attn_weights_17_cast")]; + tensor attn_weights_19_cast = mul(x = attn_weights_17_cast, y = var_273_to_fp16)[name = tensor("attn_weights_19_cast")]; + tensor var_849_cast = softmax(axis = var_266, x = attn_weights_19_cast)[name = tensor("op_849_cast")]; + tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; + tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; + tensor attn_9_cast = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_845_cast, y = var_849_cast)[name = tensor("attn_9_cast")]; + tensor var_853 = const()[name = tensor("op_853"), val = tensor([2, 640, 1, -1])]; + tensor input_95_cast = reshape(shape = var_853, x = attn_9_cast)[name = tensor("input_95_cast")]; + tensor var_858 = const()[name = tensor("op_858"), val = tensor([1, 1])]; + tensor var_860 = const()[name = tensor("op_860"), val = tensor([1, 1])]; + tensor var_862_pad_type_0 = const()[name = tensor("op_862_pad_type_0"), val = tensor("custom")]; + tensor var_862_pad_0 = const()[name = tensor("op_862_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99947840)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100767104)))]; + tensor var_862_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_860, groups = var_282, pad = var_862_pad_0, pad_type = var_862_pad_type_0, strides = var_858, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_95_cast)[name = tensor("op_862_cast")]; + tensor inputs_15_cast = add(x = var_862_cast, y = inputs_13_cast)[name = tensor("inputs_15_cast")]; + tensor var_866 = const()[name = tensor("op_866"), val = tensor([1])]; + tensor channels_mean_15_cast = reduce_mean(axes = var_866, keep_dims = var_277, x = inputs_15_cast)[name = tensor("channels_mean_15_cast")]; + tensor zero_mean_15_cast = sub(x = inputs_15_cast, y = channels_mean_15_cast)[name = tensor("zero_mean_15_cast")]; + tensor zero_mean_sq_15_cast = mul(x = zero_mean_15_cast, y = zero_mean_15_cast)[name = tensor("zero_mean_sq_15_cast")]; + tensor var_870 = const()[name = tensor("op_870"), val = tensor([1])]; + tensor var_871_cast = reduce_mean(axes = var_870, keep_dims = var_277, x = zero_mean_sq_15_cast)[name = tensor("op_871_cast")]; + tensor var_872_to_fp16 = const()[name = tensor("op_872_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_873_cast = add(x = var_871_cast, y = var_872_to_fp16)[name = tensor("op_873_cast")]; + tensor denom_15_epsilon_0_to_fp16 = const()[name = tensor("denom_15_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_15_cast = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_873_cast)[name = tensor("denom_15_cast")]; + tensor out_15_cast = mul(x = zero_mean_15_cast, y = denom_15_cast)[name = tensor("out_15_cast")]; + tensor var_877_to_fp16 = const()[name = tensor("op_877_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100768448)))]; + tensor var_878_cast = add(x = out_15_cast, y = var_877_to_fp16)[name = tensor("op_878_cast")]; + tensor var_880_to_fp16 = const()[name = tensor("op_880_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100769792)))]; + tensor hidden_states_43_cast = mul(x = var_878_cast, y = var_880_to_fp16)[name = tensor("hidden_states_43_cast")]; + tensor var_887 = const()[name = tensor("op_887"), val = tensor([1, 1])]; + tensor var_889 = const()[name = tensor("op_889"), val = tensor([1, 1])]; + tensor q_11_pad_type_0 = const()[name = tensor("q_11_pad_type_0"), val = tensor("custom")]; + tensor q_11_pad_0 = const()[name = tensor("q_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100771136)))]; + tensor q_11_cast = conv(dilations = var_889, groups = var_282, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_887, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_43_cast)[name = tensor("q_11_cast")]; + tensor var_893 = const()[name = tensor("op_893"), val = tensor([1, 1])]; + tensor var_895 = const()[name = tensor("op_895"), val = tensor([1, 1])]; + tensor k_11_pad_type_0 = const()[name = tensor("k_11_pad_type_0"), val = tensor("custom")]; + tensor k_11_pad_0 = const()[name = tensor("k_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101590400)))]; + tensor k_11_cast = conv(dilations = var_895, groups = var_282, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_893, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_11_cast")]; + tensor var_899 = const()[name = tensor("op_899"), val = tensor([1, 1])]; + tensor var_901 = const()[name = tensor("op_901"), val = tensor([1, 1])]; + tensor v_11_pad_type_0 = const()[name = tensor("v_11_pad_type_0"), val = tensor("custom")]; + tensor v_11_pad_0 = const()[name = tensor("v_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104211904)))]; + tensor v_11_cast = conv(dilations = var_901, groups = var_282, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_899, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_11_cast")]; + tensor var_905 = const()[name = tensor("op_905"), val = tensor([2, 10, 64, -1])]; + tensor var_906_cast = reshape(shape = var_905, x = q_11_cast)[name = tensor("op_906_cast")]; + tensor var_907 = const()[name = tensor("op_907"), val = tensor([2, 10, 64, -1])]; + tensor var_908_cast = reshape(shape = var_907, x = k_11_cast)[name = tensor("op_908_cast")]; + tensor var_909 = const()[name = tensor("op_909"), val = tensor([2, 10, 64, -1])]; + tensor var_910_cast = reshape(shape = var_909, x = v_11_cast)[name = tensor("op_910_cast")]; + tensor attn_weights_21_transpose_x_0 = const()[name = tensor("attn_weights_21_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_21_transpose_y_0 = const()[name = tensor("attn_weights_21_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_21_cast = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_906_cast, y = var_908_cast)[name = tensor("attn_weights_21_cast")]; + tensor attn_weights_23_cast = mul(x = attn_weights_21_cast, y = var_273_to_fp16)[name = tensor("attn_weights_23_cast")]; + tensor var_914_cast = softmax(axis = var_266, x = attn_weights_23_cast)[name = tensor("op_914_cast")]; + tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; + tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; + tensor attn_11_cast = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_910_cast, y = var_914_cast)[name = tensor("attn_11_cast")]; + tensor var_918 = const()[name = tensor("op_918"), val = tensor([2, 640, 1, -1])]; + tensor input_97_cast = reshape(shape = var_918, x = attn_11_cast)[name = tensor("input_97_cast")]; + tensor var_923 = const()[name = tensor("op_923"), val = tensor([1, 1])]; + tensor var_925 = const()[name = tensor("op_925"), val = tensor([1, 1])]; + tensor var_927_pad_type_0 = const()[name = tensor("op_927_pad_type_0"), val = tensor("custom")]; + tensor var_927_pad_0 = const()[name = tensor("op_927_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106833408)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107652672)))]; + tensor var_927_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_925, groups = var_282, pad = var_927_pad_0, pad_type = var_927_pad_type_0, strides = var_923, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_97_cast)[name = tensor("op_927_cast")]; + tensor inputs_17_cast = add(x = var_927_cast, y = inputs_15_cast)[name = tensor("inputs_17_cast")]; + tensor var_931 = const()[name = tensor("op_931"), val = tensor([1])]; + tensor channels_mean_17_cast = reduce_mean(axes = var_931, keep_dims = var_277, x = inputs_17_cast)[name = tensor("channels_mean_17_cast")]; + tensor zero_mean_17_cast = sub(x = inputs_17_cast, y = channels_mean_17_cast)[name = tensor("zero_mean_17_cast")]; + tensor zero_mean_sq_17_cast = mul(x = zero_mean_17_cast, y = zero_mean_17_cast)[name = tensor("zero_mean_sq_17_cast")]; + tensor var_935 = const()[name = tensor("op_935"), val = tensor([1])]; + tensor var_936_cast = reduce_mean(axes = var_935, keep_dims = var_277, x = zero_mean_sq_17_cast)[name = tensor("op_936_cast")]; + tensor var_937_to_fp16 = const()[name = tensor("op_937_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_938_cast = add(x = var_936_cast, y = var_937_to_fp16)[name = tensor("op_938_cast")]; + tensor denom_17_epsilon_0_to_fp16 = const()[name = tensor("denom_17_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_17_cast = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_938_cast)[name = tensor("denom_17_cast")]; + tensor out_17_cast = mul(x = zero_mean_17_cast, y = denom_17_cast)[name = tensor("out_17_cast")]; + tensor var_942_to_fp16 = const()[name = tensor("op_942_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107654016)))]; + tensor var_943_cast = add(x = out_17_cast, y = var_942_to_fp16)[name = tensor("op_943_cast")]; + tensor var_945_to_fp16 = const()[name = tensor("op_945_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107655360)))]; + tensor input_99_cast = mul(x = var_943_cast, y = var_945_to_fp16)[name = tensor("input_99_cast")]; + tensor var_953 = const()[name = tensor("op_953"), val = tensor([1, 1])]; + tensor var_955 = const()[name = tensor("op_955"), val = tensor([1, 1])]; + tensor var_957_pad_type_0 = const()[name = tensor("op_957_pad_type_0"), val = tensor("custom")]; + tensor var_957_pad_0 = const()[name = tensor("op_957_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107656704)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(114210368)))]; + tensor var_957_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_955, groups = var_282, pad = var_957_pad_0, pad_type = var_957_pad_type_0, strides = var_953, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_99_cast)[name = tensor("op_957_cast")]; + tensor var_958_split_sizes_0 = const()[name = tensor("op_958_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_958_axis_0 = const()[name = tensor("op_958_axis_0"), val = tensor(1)]; + tensor var_958_cast_0, tensor var_958_cast_1 = split(axis = var_958_axis_0, split_sizes = var_958_split_sizes_0, x = var_957_cast)[name = tensor("op_958_cast")]; + tensor var_960_mode_0 = const()[name = tensor("op_960_mode_0"), val = tensor("EXACT")]; + tensor var_960_cast = gelu(mode = var_960_mode_0, x = var_958_cast_1)[name = tensor("op_960_cast")]; + tensor input_101_cast = mul(x = var_958_cast_0, y = var_960_cast)[name = tensor("input_101_cast")]; + tensor var_964 = const()[name = tensor("op_964"), val = tensor([1, 1])]; + tensor var_966 = const()[name = tensor("op_966"), val = tensor([1, 1])]; + tensor var_968_pad_type_0 = const()[name = tensor("op_968_pad_type_0"), val = tensor("custom")]; + tensor var_968_pad_0 = const()[name = tensor("op_968_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(114220672)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117497536)))]; + tensor var_968_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_966, groups = var_282, pad = var_968_pad_0, pad_type = var_968_pad_type_0, strides = var_964, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_101_cast)[name = tensor("op_968_cast")]; + tensor inputs_19_cast = add(x = var_968_cast, y = inputs_17_cast)[name = tensor("inputs_19_cast")]; + tensor var_978 = const()[name = tensor("op_978"), val = tensor([1])]; + tensor channels_mean_19_cast = reduce_mean(axes = var_978, keep_dims = var_277, x = inputs_19_cast)[name = tensor("channels_mean_19_cast")]; + tensor zero_mean_19_cast = sub(x = inputs_19_cast, y = channels_mean_19_cast)[name = tensor("zero_mean_19_cast")]; + tensor zero_mean_sq_19_cast = mul(x = zero_mean_19_cast, y = zero_mean_19_cast)[name = tensor("zero_mean_sq_19_cast")]; + tensor var_982 = const()[name = tensor("op_982"), val = tensor([1])]; + tensor var_983_cast = reduce_mean(axes = var_982, keep_dims = var_277, x = zero_mean_sq_19_cast)[name = tensor("op_983_cast")]; + tensor var_984_to_fp16 = const()[name = tensor("op_984_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_985_cast = add(x = var_983_cast, y = var_984_to_fp16)[name = tensor("op_985_cast")]; + tensor denom_19_epsilon_0_to_fp16 = const()[name = tensor("denom_19_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_19_cast = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_985_cast)[name = tensor("denom_19_cast")]; + tensor out_19_cast = mul(x = zero_mean_19_cast, y = denom_19_cast)[name = tensor("out_19_cast")]; + tensor var_989_to_fp16 = const()[name = tensor("op_989_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117498880)))]; + tensor var_990_cast = add(x = out_19_cast, y = var_989_to_fp16)[name = tensor("op_990_cast")]; + tensor var_992_to_fp16 = const()[name = tensor("op_992_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117500224)))]; + tensor hidden_states_47_cast = mul(x = var_990_cast, y = var_992_to_fp16)[name = tensor("hidden_states_47_cast")]; + tensor var_999 = const()[name = tensor("op_999"), val = tensor([1, 1])]; + tensor var_1001 = const()[name = tensor("op_1001"), val = tensor([1, 1])]; + tensor q_13_pad_type_0 = const()[name = tensor("q_13_pad_type_0"), val = tensor("custom")]; + tensor q_13_pad_0 = const()[name = tensor("q_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117501568)))]; + tensor q_13_cast = conv(dilations = var_1001, groups = var_282, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_999, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_47_cast)[name = tensor("q_13_cast")]; + tensor var_1005 = const()[name = tensor("op_1005"), val = tensor([1, 1])]; + tensor var_1007 = const()[name = tensor("op_1007"), val = tensor([1, 1])]; + tensor k_13_pad_type_0 = const()[name = tensor("k_13_pad_type_0"), val = tensor("custom")]; + tensor k_13_pad_0 = const()[name = tensor("k_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118320832)))]; + tensor k_13_cast = conv(dilations = var_1007, groups = var_282, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1005, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_47_cast)[name = tensor("k_13_cast")]; + tensor var_1011 = const()[name = tensor("op_1011"), val = tensor([1, 1])]; + tensor var_1013 = const()[name = tensor("op_1013"), val = tensor([1, 1])]; + tensor v_13_pad_type_0 = const()[name = tensor("v_13_pad_type_0"), val = tensor("custom")]; + tensor v_13_pad_0 = const()[name = tensor("v_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119140096)))]; + tensor v_13_cast = conv(dilations = var_1013, groups = var_282, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1011, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_47_cast)[name = tensor("v_13_cast")]; + tensor var_1017 = const()[name = tensor("op_1017"), val = tensor([2, 10, 64, -1])]; + tensor var_1018_cast = reshape(shape = var_1017, x = q_13_cast)[name = tensor("op_1018_cast")]; + tensor var_1019 = const()[name = tensor("op_1019"), val = tensor([2, 10, 64, -1])]; + tensor var_1020_cast = reshape(shape = var_1019, x = k_13_cast)[name = tensor("op_1020_cast")]; + tensor var_1021 = const()[name = tensor("op_1021"), val = tensor([2, 10, 64, -1])]; + tensor var_1022_cast = reshape(shape = var_1021, x = v_13_cast)[name = tensor("op_1022_cast")]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1018_cast, y = var_1020_cast)[name = tensor("attn_weights_25_cast")]; + tensor attn_weights_27_cast = mul(x = attn_weights_25_cast, y = var_273_to_fp16)[name = tensor("attn_weights_27_cast")]; + tensor var_1026_cast = softmax(axis = var_266, x = attn_weights_27_cast)[name = tensor("op_1026_cast")]; + tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; + tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; + tensor attn_13_cast = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1022_cast, y = var_1026_cast)[name = tensor("attn_13_cast")]; + tensor var_1030 = const()[name = tensor("op_1030"), val = tensor([2, 640, 1, -1])]; + tensor input_103_cast = reshape(shape = var_1030, x = attn_13_cast)[name = tensor("input_103_cast")]; + tensor var_1035 = const()[name = tensor("op_1035"), val = tensor([1, 1])]; + tensor var_1037 = const()[name = tensor("op_1037"), val = tensor([1, 1])]; + tensor var_1039_pad_type_0 = const()[name = tensor("op_1039_pad_type_0"), val = tensor("custom")]; + tensor var_1039_pad_0 = const()[name = tensor("op_1039_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119959360)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120778624)))]; + tensor var_1039_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1037, groups = var_282, pad = var_1039_pad_0, pad_type = var_1039_pad_type_0, strides = var_1035, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_103_cast)[name = tensor("op_1039_cast")]; + tensor inputs_21_cast = add(x = var_1039_cast, y = inputs_19_cast)[name = tensor("inputs_21_cast")]; + tensor var_1043 = const()[name = tensor("op_1043"), val = tensor([1])]; + tensor channels_mean_21_cast = reduce_mean(axes = var_1043, keep_dims = var_277, x = inputs_21_cast)[name = tensor("channels_mean_21_cast")]; + tensor zero_mean_21_cast = sub(x = inputs_21_cast, y = channels_mean_21_cast)[name = tensor("zero_mean_21_cast")]; + tensor zero_mean_sq_21_cast = mul(x = zero_mean_21_cast, y = zero_mean_21_cast)[name = tensor("zero_mean_sq_21_cast")]; + tensor var_1047 = const()[name = tensor("op_1047"), val = tensor([1])]; + tensor var_1048_cast = reduce_mean(axes = var_1047, keep_dims = var_277, x = zero_mean_sq_21_cast)[name = tensor("op_1048_cast")]; + tensor var_1049_to_fp16 = const()[name = tensor("op_1049_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1050_cast = add(x = var_1048_cast, y = var_1049_to_fp16)[name = tensor("op_1050_cast")]; + tensor denom_21_epsilon_0_to_fp16 = const()[name = tensor("denom_21_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_21_cast = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_1050_cast)[name = tensor("denom_21_cast")]; + tensor out_21_cast = mul(x = zero_mean_21_cast, y = denom_21_cast)[name = tensor("out_21_cast")]; + tensor var_1054_to_fp16 = const()[name = tensor("op_1054_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120779968)))]; + tensor var_1055_cast = add(x = out_21_cast, y = var_1054_to_fp16)[name = tensor("op_1055_cast")]; + tensor var_1057_to_fp16 = const()[name = tensor("op_1057_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120781312)))]; + tensor hidden_states_49_cast = mul(x = var_1055_cast, y = var_1057_to_fp16)[name = tensor("hidden_states_49_cast")]; + tensor var_1064 = const()[name = tensor("op_1064"), val = tensor([1, 1])]; + tensor var_1066 = const()[name = tensor("op_1066"), val = tensor([1, 1])]; + tensor q_15_pad_type_0 = const()[name = tensor("q_15_pad_type_0"), val = tensor("custom")]; + tensor q_15_pad_0 = const()[name = tensor("q_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120782656)))]; + tensor q_15_cast = conv(dilations = var_1066, groups = var_282, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1064, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_49_cast)[name = tensor("q_15_cast")]; + tensor var_1070 = const()[name = tensor("op_1070"), val = tensor([1, 1])]; + tensor var_1072 = const()[name = tensor("op_1072"), val = tensor([1, 1])]; + tensor k_15_pad_type_0 = const()[name = tensor("k_15_pad_type_0"), val = tensor("custom")]; + tensor k_15_pad_0 = const()[name = tensor("k_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121601920)))]; + tensor k_15_cast = conv(dilations = var_1072, groups = var_282, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1070, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_15_cast")]; + tensor var_1076 = const()[name = tensor("op_1076"), val = tensor([1, 1])]; + tensor var_1078 = const()[name = tensor("op_1078"), val = tensor([1, 1])]; + tensor v_15_pad_type_0 = const()[name = tensor("v_15_pad_type_0"), val = tensor("custom")]; + tensor v_15_pad_0 = const()[name = tensor("v_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124223424)))]; + tensor v_15_cast = conv(dilations = var_1078, groups = var_282, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1076, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_15_cast")]; + tensor var_1082 = const()[name = tensor("op_1082"), val = tensor([2, 10, 64, -1])]; + tensor var_1083_cast = reshape(shape = var_1082, x = q_15_cast)[name = tensor("op_1083_cast")]; + tensor var_1084 = const()[name = tensor("op_1084"), val = tensor([2, 10, 64, -1])]; + tensor var_1085_cast = reshape(shape = var_1084, x = k_15_cast)[name = tensor("op_1085_cast")]; + tensor var_1086 = const()[name = tensor("op_1086"), val = tensor([2, 10, 64, -1])]; + tensor var_1087_cast = reshape(shape = var_1086, x = v_15_cast)[name = tensor("op_1087_cast")]; + tensor attn_weights_29_transpose_x_0 = const()[name = tensor("attn_weights_29_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_29_transpose_y_0 = const()[name = tensor("attn_weights_29_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_29_cast = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1083_cast, y = var_1085_cast)[name = tensor("attn_weights_29_cast")]; + tensor attn_weights_31_cast = mul(x = attn_weights_29_cast, y = var_273_to_fp16)[name = tensor("attn_weights_31_cast")]; + tensor var_1091_cast = softmax(axis = var_266, x = attn_weights_31_cast)[name = tensor("op_1091_cast")]; + tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; + tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; + tensor attn_15_cast = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1087_cast, y = var_1091_cast)[name = tensor("attn_15_cast")]; + tensor var_1095 = const()[name = tensor("op_1095"), val = tensor([2, 640, 1, -1])]; + tensor input_105_cast = reshape(shape = var_1095, x = attn_15_cast)[name = tensor("input_105_cast")]; + tensor var_1100 = const()[name = tensor("op_1100"), val = tensor([1, 1])]; + tensor var_1102 = const()[name = tensor("op_1102"), val = tensor([1, 1])]; + tensor var_1104_pad_type_0 = const()[name = tensor("op_1104_pad_type_0"), val = tensor("custom")]; + tensor var_1104_pad_0 = const()[name = tensor("op_1104_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126844928)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127664192)))]; + tensor var_1104_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1102, groups = var_282, pad = var_1104_pad_0, pad_type = var_1104_pad_type_0, strides = var_1100, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_105_cast)[name = tensor("op_1104_cast")]; + tensor inputs_23_cast = add(x = var_1104_cast, y = inputs_21_cast)[name = tensor("inputs_23_cast")]; + tensor var_1108 = const()[name = tensor("op_1108"), val = tensor([1])]; + tensor channels_mean_23_cast = reduce_mean(axes = var_1108, keep_dims = var_277, x = inputs_23_cast)[name = tensor("channels_mean_23_cast")]; + tensor zero_mean_23_cast = sub(x = inputs_23_cast, y = channels_mean_23_cast)[name = tensor("zero_mean_23_cast")]; + tensor zero_mean_sq_23_cast = mul(x = zero_mean_23_cast, y = zero_mean_23_cast)[name = tensor("zero_mean_sq_23_cast")]; + tensor var_1112 = const()[name = tensor("op_1112"), val = tensor([1])]; + tensor var_1113_cast = reduce_mean(axes = var_1112, keep_dims = var_277, x = zero_mean_sq_23_cast)[name = tensor("op_1113_cast")]; + tensor var_1114_to_fp16 = const()[name = tensor("op_1114_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1115_cast = add(x = var_1113_cast, y = var_1114_to_fp16)[name = tensor("op_1115_cast")]; + tensor denom_23_epsilon_0_to_fp16 = const()[name = tensor("denom_23_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_23_cast = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1115_cast)[name = tensor("denom_23_cast")]; + tensor out_23_cast = mul(x = zero_mean_23_cast, y = denom_23_cast)[name = tensor("out_23_cast")]; + tensor var_1119_to_fp16 = const()[name = tensor("op_1119_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127665536)))]; + tensor var_1120_cast = add(x = out_23_cast, y = var_1119_to_fp16)[name = tensor("op_1120_cast")]; + tensor var_1122_to_fp16 = const()[name = tensor("op_1122_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127666880)))]; + tensor input_107_cast = mul(x = var_1120_cast, y = var_1122_to_fp16)[name = tensor("input_107_cast")]; + tensor var_1130 = const()[name = tensor("op_1130"), val = tensor([1, 1])]; + tensor var_1132 = const()[name = tensor("op_1132"), val = tensor([1, 1])]; + tensor var_1134_pad_type_0 = const()[name = tensor("op_1134_pad_type_0"), val = tensor("custom")]; + tensor var_1134_pad_0 = const()[name = tensor("op_1134_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127668224)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134221888)))]; + tensor var_1134_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_1132, groups = var_282, pad = var_1134_pad_0, pad_type = var_1134_pad_type_0, strides = var_1130, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_107_cast)[name = tensor("op_1134_cast")]; + tensor var_1135_split_sizes_0 = const()[name = tensor("op_1135_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_1135_axis_0 = const()[name = tensor("op_1135_axis_0"), val = tensor(1)]; + tensor var_1135_cast_0, tensor var_1135_cast_1 = split(axis = var_1135_axis_0, split_sizes = var_1135_split_sizes_0, x = var_1134_cast)[name = tensor("op_1135_cast")]; + tensor var_1137_mode_0 = const()[name = tensor("op_1137_mode_0"), val = tensor("EXACT")]; + tensor var_1137_cast = gelu(mode = var_1137_mode_0, x = var_1135_cast_1)[name = tensor("op_1137_cast")]; + tensor input_109_cast = mul(x = var_1135_cast_0, y = var_1137_cast)[name = tensor("input_109_cast")]; + tensor var_1141 = const()[name = tensor("op_1141"), val = tensor([1, 1])]; + tensor var_1143 = const()[name = tensor("op_1143"), val = tensor([1, 1])]; + tensor var_1145_pad_type_0 = const()[name = tensor("op_1145_pad_type_0"), val = tensor("custom")]; + tensor var_1145_pad_0 = const()[name = tensor("op_1145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134232192)))]; + tensor down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137509056)))]; + tensor var_1145_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1143, groups = var_282, pad = var_1145_pad_0, pad_type = var_1145_pad_type_0, strides = var_1141, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_109_cast)[name = tensor("op_1145_cast")]; + tensor hidden_states_53_cast = add(x = var_1145_cast, y = inputs_23_cast)[name = tensor("hidden_states_53_cast")]; + tensor var_1147 = const()[name = tensor("op_1147"), val = tensor([2, 640, 64, 64])]; + tensor input_111_cast = reshape(shape = var_1147, x = hidden_states_53_cast)[name = tensor("input_111_cast")]; + tensor var_1151 = const()[name = tensor("op_1151"), val = tensor([1, 1])]; + tensor var_1153 = const()[name = tensor("op_1153"), val = tensor([1, 1])]; + tensor hidden_states_55_pad_type_0 = const()[name = tensor("hidden_states_55_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_55_pad_0 = const()[name = tensor("hidden_states_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137510400)))]; + tensor down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138329664)))]; + tensor hidden_states_55_cast = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1153, groups = var_282, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_1151, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_111_cast)[name = tensor("hidden_states_55_cast")]; + tensor input_113_cast = add(x = hidden_states_55_cast, y = hidden_states_37_cast)[name = tensor("input_113_cast")]; + tensor var_1160 = const()[name = tensor("op_1160"), val = tensor([2, 2])]; + tensor var_1162 = const()[name = tensor("op_1162"), val = tensor([1, 1])]; + tensor input_115_pad_type_0 = const()[name = tensor("input_115_pad_type_0"), val = tensor("custom")]; + tensor input_115_pad_0 = const()[name = tensor("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138331008)))]; + tensor down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145703872)))]; + tensor input_115_cast = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1162, groups = var_282, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = var_1160, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = input_113_cast)[name = tensor("input_115_cast")]; + tensor var_1170 = const()[name = tensor("op_1170"), val = tensor(3)]; + tensor var_1181 = const()[name = tensor("op_1181"), val = tensor(true)]; + tensor var_1186 = const()[name = tensor("op_1186"), val = tensor(1)]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_40_cast = reshape(shape = reshape_40_shape_0, x = input_115_cast)[name = tensor("reshape_40_cast")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30_cast = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast)[name = tensor("reduce_mean_30_cast")]; + tensor sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast)[name = tensor("sub_20_cast")]; + tensor square_10_cast = square(x = sub_20_cast)[name = tensor("square_10_cast")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32_cast = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast)[name = tensor("reduce_mean_32_cast")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_20_cast = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast")]; + tensor sqrt_10_cast = sqrt(x = add_20_cast)[name = tensor("sqrt_10_cast")]; + tensor real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast)[name = tensor("real_div_10_cast")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast)[name = tensor("reshape_41_cast")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145705216)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145706560)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_41_cast)[name = tensor("add_21_cast")]; + tensor input_119_cast = silu(x = add_21_cast)[name = tensor("input_119_cast")]; + tensor var_1207 = const()[name = tensor("op_1207"), val = tensor([1, 1])]; + tensor var_1209 = const()[name = tensor("op_1209"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_type_0 = const()[name = tensor("hidden_states_57_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_57_pad_0 = const()[name = tensor("hidden_states_57_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145707904)))]; + tensor down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160453568)))]; + tensor hidden_states_57_cast = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1209, groups = var_1186, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_1207, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_119_cast)[name = tensor("hidden_states_57_cast")]; + tensor var_1215 = const()[name = tensor("op_1215"), val = tensor([1, 1])]; + tensor var_1217 = const()[name = tensor("op_1217"), val = tensor([1, 1])]; + tensor temb_9_pad_type_0 = const()[name = tensor("temb_9_pad_type_0"), val = tensor("custom")]; + tensor temb_9_pad_0 = const()[name = tensor("temb_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160456192)))]; + tensor down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163733056)))]; + tensor temb_9_cast = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1217, groups = var_1186, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1215, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_9_cast")]; + tensor input_123_cast = add(x = hidden_states_57_cast, y = temb_9_cast)[name = tensor("input_123_cast")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_44_cast = reshape(shape = reshape_44_shape_0, x = input_123_cast)[name = tensor("reshape_44_cast")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33_cast = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast)[name = tensor("reduce_mean_33_cast")]; + tensor sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast)[name = tensor("sub_22_cast")]; + tensor square_11_cast = square(x = sub_22_cast)[name = tensor("square_11_cast")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35_cast = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast)[name = tensor("reduce_mean_35_cast")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_22_cast = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast")]; + tensor sqrt_11_cast = sqrt(x = add_22_cast)[name = tensor("sqrt_11_cast")]; + tensor real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast)[name = tensor("real_div_11_cast")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast)[name = tensor("reshape_45_cast")]; + tensor add_23_mean_0_to_fp16 = const()[name = tensor("add_23_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163735680)))]; + tensor add_23_variance_0_to_fp16 = const()[name = tensor("add_23_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163738304)))]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163740928)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163743552)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_45_cast)[name = tensor("add_23_cast")]; + tensor input_127_cast = silu(x = add_23_cast)[name = tensor("input_127_cast")]; + tensor var_1227 = const()[name = tensor("op_1227"), val = tensor([1, 1])]; + tensor var_1229 = const()[name = tensor("op_1229"), val = tensor([1, 1])]; + tensor hidden_states_59_pad_type_0 = const()[name = tensor("hidden_states_59_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_59_pad_0 = const()[name = tensor("hidden_states_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163746176)))]; + tensor down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193237440)))]; + tensor hidden_states_59_cast = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1229, groups = var_1186, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = var_1227, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_127_cast)[name = tensor("hidden_states_59_cast")]; + tensor var_1234 = const()[name = tensor("op_1234"), val = tensor([1, 1])]; + tensor var_1236 = const()[name = tensor("op_1236"), val = tensor([1, 1])]; + tensor x_3_pad_type_0 = const()[name = tensor("x_3_pad_type_0"), val = tensor("custom")]; + tensor x_3_pad_0 = const()[name = tensor("x_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193240064)))]; + tensor down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194878528)))]; + tensor x_3_cast = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1236, groups = var_1186, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1234, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_115_cast)[name = tensor("x_3_cast")]; + tensor hidden_states_61_cast = add(x = x_3_cast, y = hidden_states_59_cast)[name = tensor("hidden_states_61_cast")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_48_cast = reshape(shape = reshape_48_shape_0, x = hidden_states_61_cast)[name = tensor("reshape_48_cast")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36_cast = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast)[name = tensor("reduce_mean_36_cast")]; + tensor sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast)[name = tensor("sub_24_cast")]; + tensor square_12_cast = square(x = sub_24_cast)[name = tensor("square_12_cast")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38_cast = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast)[name = tensor("reduce_mean_38_cast")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_24_cast = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast")]; + tensor sqrt_12_cast = sqrt(x = add_24_cast)[name = tensor("sqrt_12_cast")]; + tensor real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast)[name = tensor("real_div_12_cast")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast)[name = tensor("reshape_49_cast")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194881152)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194883776)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_49_cast)[name = tensor("add_25_cast")]; + tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([1, 1])]; + tensor var_1276 = const()[name = tensor("op_1276"), val = tensor([1, 1])]; + tensor hidden_states_63_pad_type_0 = const()[name = tensor("hidden_states_63_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_63_pad_0 = const()[name = tensor("hidden_states_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194886400)))]; + tensor down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198163264)))]; + tensor hidden_states_63_cast = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1276, groups = var_1186, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = var_1274, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_25_cast)[name = tensor("hidden_states_63_cast")]; + tensor var_1281 = const()[name = tensor("op_1281"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_25_cast = reshape(shape = var_1281, x = hidden_states_63_cast)[name = tensor("inputs_25_cast")]; + tensor var_1291 = const()[name = tensor("op_1291"), val = tensor([1])]; + tensor channels_mean_25_cast = reduce_mean(axes = var_1291, keep_dims = var_1181, x = inputs_25_cast)[name = tensor("channels_mean_25_cast")]; + tensor zero_mean_25_cast = sub(x = inputs_25_cast, y = channels_mean_25_cast)[name = tensor("zero_mean_25_cast")]; + tensor zero_mean_sq_25_cast = mul(x = zero_mean_25_cast, y = zero_mean_25_cast)[name = tensor("zero_mean_sq_25_cast")]; + tensor var_1295 = const()[name = tensor("op_1295"), val = tensor([1])]; + tensor var_1296_cast = reduce_mean(axes = var_1295, keep_dims = var_1181, x = zero_mean_sq_25_cast)[name = tensor("op_1296_cast")]; + tensor var_1297_to_fp16 = const()[name = tensor("op_1297_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1298_cast = add(x = var_1296_cast, y = var_1297_to_fp16)[name = tensor("op_1298_cast")]; + tensor denom_25_epsilon_0_to_fp16 = const()[name = tensor("denom_25_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_25_cast = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1298_cast)[name = tensor("denom_25_cast")]; + tensor out_25_cast = mul(x = zero_mean_25_cast, y = denom_25_cast)[name = tensor("out_25_cast")]; + tensor var_1302_to_fp16 = const()[name = tensor("op_1302_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198165888)))]; + tensor var_1303_cast = add(x = out_25_cast, y = var_1302_to_fp16)[name = tensor("op_1303_cast")]; + tensor var_1305_to_fp16 = const()[name = tensor("op_1305_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198168512)))]; + tensor hidden_states_65_cast = mul(x = var_1303_cast, y = var_1305_to_fp16)[name = tensor("hidden_states_65_cast")]; + tensor var_1312 = const()[name = tensor("op_1312"), val = tensor([1, 1])]; + tensor var_1314 = const()[name = tensor("op_1314"), val = tensor([1, 1])]; + tensor q_17_pad_type_0 = const()[name = tensor("q_17_pad_type_0"), val = tensor("custom")]; + tensor q_17_pad_0 = const()[name = tensor("q_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198171136)))]; + tensor q_17_cast = conv(dilations = var_1314, groups = var_1186, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1312, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_65_cast)[name = tensor("q_17_cast")]; + tensor var_1318 = const()[name = tensor("op_1318"), val = tensor([1, 1])]; + tensor var_1320 = const()[name = tensor("op_1320"), val = tensor([1, 1])]; + tensor k_17_pad_type_0 = const()[name = tensor("k_17_pad_type_0"), val = tensor("custom")]; + tensor k_17_pad_0 = const()[name = tensor("k_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201448000)))]; + tensor k_17_cast = conv(dilations = var_1320, groups = var_1186, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1318, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_65_cast)[name = tensor("k_17_cast")]; + tensor var_1324 = const()[name = tensor("op_1324"), val = tensor([1, 1])]; + tensor var_1326 = const()[name = tensor("op_1326"), val = tensor([1, 1])]; + tensor v_17_pad_type_0 = const()[name = tensor("v_17_pad_type_0"), val = tensor("custom")]; + tensor v_17_pad_0 = const()[name = tensor("v_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204724864)))]; + tensor v_17_cast = conv(dilations = var_1326, groups = var_1186, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1324, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_65_cast)[name = tensor("v_17_cast")]; + tensor var_1330 = const()[name = tensor("op_1330"), val = tensor([2, 20, 64, -1])]; + tensor var_1331_cast = reshape(shape = var_1330, x = q_17_cast)[name = tensor("op_1331_cast")]; + tensor var_1332 = const()[name = tensor("op_1332"), val = tensor([2, 20, 64, -1])]; + tensor var_1333_cast = reshape(shape = var_1332, x = k_17_cast)[name = tensor("op_1333_cast")]; + tensor var_1334 = const()[name = tensor("op_1334"), val = tensor([2, 20, 64, -1])]; + tensor var_1335_cast = reshape(shape = var_1334, x = v_17_cast)[name = tensor("op_1335_cast")]; + tensor attn_weights_33_transpose_x_0 = const()[name = tensor("attn_weights_33_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_33_transpose_y_0 = const()[name = tensor("attn_weights_33_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_33_cast = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1331_cast, y = var_1333_cast)[name = tensor("attn_weights_33_cast")]; + tensor var_1177_to_fp16 = const()[name = tensor("op_1177_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_35_cast = mul(x = attn_weights_33_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_35_cast")]; + tensor var_1339_cast = softmax(axis = var_1170, x = attn_weights_35_cast)[name = tensor("op_1339_cast")]; + tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; + tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; + tensor attn_17_cast = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1335_cast, y = var_1339_cast)[name = tensor("attn_17_cast")]; + tensor var_1343 = const()[name = tensor("op_1343"), val = tensor([2, 1280, 1, -1])]; + tensor input_131_cast = reshape(shape = var_1343, x = attn_17_cast)[name = tensor("input_131_cast")]; + tensor var_1348 = const()[name = tensor("op_1348"), val = tensor([1, 1])]; + tensor var_1350 = const()[name = tensor("op_1350"), val = tensor([1, 1])]; + tensor var_1352_pad_type_0 = const()[name = tensor("op_1352_pad_type_0"), val = tensor("custom")]; + tensor var_1352_pad_0 = const()[name = tensor("op_1352_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208001728)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211278592)))]; + tensor var_1352_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1350, groups = var_1186, pad = var_1352_pad_0, pad_type = var_1352_pad_type_0, strides = var_1348, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_131_cast)[name = tensor("op_1352_cast")]; + tensor inputs_27_cast = add(x = var_1352_cast, y = inputs_25_cast)[name = tensor("inputs_27_cast")]; + tensor var_1356 = const()[name = tensor("op_1356"), val = tensor([1])]; + tensor channels_mean_27_cast = reduce_mean(axes = var_1356, keep_dims = var_1181, x = inputs_27_cast)[name = tensor("channels_mean_27_cast")]; + tensor zero_mean_27_cast = sub(x = inputs_27_cast, y = channels_mean_27_cast)[name = tensor("zero_mean_27_cast")]; + tensor zero_mean_sq_27_cast = mul(x = zero_mean_27_cast, y = zero_mean_27_cast)[name = tensor("zero_mean_sq_27_cast")]; + tensor var_1360 = const()[name = tensor("op_1360"), val = tensor([1])]; + tensor var_1361_cast = reduce_mean(axes = var_1360, keep_dims = var_1181, x = zero_mean_sq_27_cast)[name = tensor("op_1361_cast")]; + tensor var_1362_to_fp16 = const()[name = tensor("op_1362_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1363_cast = add(x = var_1361_cast, y = var_1362_to_fp16)[name = tensor("op_1363_cast")]; + tensor denom_27_epsilon_0_to_fp16 = const()[name = tensor("denom_27_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_27_cast = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1363_cast)[name = tensor("denom_27_cast")]; + tensor out_27_cast = mul(x = zero_mean_27_cast, y = denom_27_cast)[name = tensor("out_27_cast")]; + tensor var_1367_to_fp16 = const()[name = tensor("op_1367_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211281216)))]; + tensor var_1368_cast = add(x = out_27_cast, y = var_1367_to_fp16)[name = tensor("op_1368_cast")]; + tensor var_1370_to_fp16 = const()[name = tensor("op_1370_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211283840)))]; + tensor hidden_states_67_cast = mul(x = var_1368_cast, y = var_1370_to_fp16)[name = tensor("hidden_states_67_cast")]; + tensor var_1377 = const()[name = tensor("op_1377"), val = tensor([1, 1])]; + tensor var_1379 = const()[name = tensor("op_1379"), val = tensor([1, 1])]; + tensor q_19_pad_type_0 = const()[name = tensor("q_19_pad_type_0"), val = tensor("custom")]; + tensor q_19_pad_0 = const()[name = tensor("q_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211286464)))]; + tensor q_19_cast = conv(dilations = var_1379, groups = var_1186, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1377, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_67_cast)[name = tensor("q_19_cast")]; + tensor var_1383 = const()[name = tensor("op_1383"), val = tensor([1, 1])]; + tensor var_1385 = const()[name = tensor("op_1385"), val = tensor([1, 1])]; + tensor k_19_pad_type_0 = const()[name = tensor("k_19_pad_type_0"), val = tensor("custom")]; + tensor k_19_pad_0 = const()[name = tensor("k_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214563328)))]; + tensor k_19_cast = conv(dilations = var_1385, groups = var_1186, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1383, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_19_cast")]; + tensor var_1389 = const()[name = tensor("op_1389"), val = tensor([1, 1])]; + tensor var_1391 = const()[name = tensor("op_1391"), val = tensor([1, 1])]; + tensor v_19_pad_type_0 = const()[name = tensor("v_19_pad_type_0"), val = tensor("custom")]; + tensor v_19_pad_0 = const()[name = tensor("v_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219806272)))]; + tensor v_19_cast = conv(dilations = var_1391, groups = var_1186, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1389, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_19_cast")]; + tensor var_1395 = const()[name = tensor("op_1395"), val = tensor([2, 20, 64, -1])]; + tensor var_1396_cast = reshape(shape = var_1395, x = q_19_cast)[name = tensor("op_1396_cast")]; + tensor var_1397 = const()[name = tensor("op_1397"), val = tensor([2, 20, 64, -1])]; + tensor var_1398_cast = reshape(shape = var_1397, x = k_19_cast)[name = tensor("op_1398_cast")]; + tensor var_1399 = const()[name = tensor("op_1399"), val = tensor([2, 20, 64, -1])]; + tensor var_1400_cast = reshape(shape = var_1399, x = v_19_cast)[name = tensor("op_1400_cast")]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1396_cast, y = var_1398_cast)[name = tensor("attn_weights_37_cast")]; + tensor attn_weights_39_cast = mul(x = attn_weights_37_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_39_cast")]; + tensor var_1404_cast = softmax(axis = var_1170, x = attn_weights_39_cast)[name = tensor("op_1404_cast")]; + tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; + tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; + tensor attn_19_cast = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1400_cast, y = var_1404_cast)[name = tensor("attn_19_cast")]; + tensor var_1408 = const()[name = tensor("op_1408"), val = tensor([2, 1280, 1, -1])]; + tensor input_133_cast = reshape(shape = var_1408, x = attn_19_cast)[name = tensor("input_133_cast")]; + tensor var_1413 = const()[name = tensor("op_1413"), val = tensor([1, 1])]; + tensor var_1415 = const()[name = tensor("op_1415"), val = tensor([1, 1])]; + tensor var_1417_pad_type_0 = const()[name = tensor("op_1417_pad_type_0"), val = tensor("custom")]; + tensor var_1417_pad_0 = const()[name = tensor("op_1417_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(225049216)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228326080)))]; + tensor var_1417_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1415, groups = var_1186, pad = var_1417_pad_0, pad_type = var_1417_pad_type_0, strides = var_1413, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_133_cast)[name = tensor("op_1417_cast")]; + tensor inputs_29_cast = add(x = var_1417_cast, y = inputs_27_cast)[name = tensor("inputs_29_cast")]; + tensor var_1421 = const()[name = tensor("op_1421"), val = tensor([1])]; + tensor channels_mean_29_cast = reduce_mean(axes = var_1421, keep_dims = var_1181, x = inputs_29_cast)[name = tensor("channels_mean_29_cast")]; + tensor zero_mean_29_cast = sub(x = inputs_29_cast, y = channels_mean_29_cast)[name = tensor("zero_mean_29_cast")]; + tensor zero_mean_sq_29_cast = mul(x = zero_mean_29_cast, y = zero_mean_29_cast)[name = tensor("zero_mean_sq_29_cast")]; + tensor var_1425 = const()[name = tensor("op_1425"), val = tensor([1])]; + tensor var_1426_cast = reduce_mean(axes = var_1425, keep_dims = var_1181, x = zero_mean_sq_29_cast)[name = tensor("op_1426_cast")]; + tensor var_1427_to_fp16 = const()[name = tensor("op_1427_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1428_cast = add(x = var_1426_cast, y = var_1427_to_fp16)[name = tensor("op_1428_cast")]; + tensor denom_29_epsilon_0_to_fp16 = const()[name = tensor("denom_29_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_29_cast = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1428_cast)[name = tensor("denom_29_cast")]; + tensor out_29_cast = mul(x = zero_mean_29_cast, y = denom_29_cast)[name = tensor("out_29_cast")]; + tensor var_1432_to_fp16 = const()[name = tensor("op_1432_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228328704)))]; + tensor var_1433_cast = add(x = out_29_cast, y = var_1432_to_fp16)[name = tensor("op_1433_cast")]; + tensor var_1435_to_fp16 = const()[name = tensor("op_1435_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228331328)))]; + tensor input_135_cast = mul(x = var_1433_cast, y = var_1435_to_fp16)[name = tensor("input_135_cast")]; + tensor var_1443 = const()[name = tensor("op_1443"), val = tensor([1, 1])]; + tensor var_1445 = const()[name = tensor("op_1445"), val = tensor([1, 1])]; + tensor var_1447_pad_type_0 = const()[name = tensor("op_1447_pad_type_0"), val = tensor("custom")]; + tensor var_1447_pad_0 = const()[name = tensor("op_1447_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228333952)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254548416)))]; + tensor var_1447_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1445, groups = var_1186, pad = var_1447_pad_0, pad_type = var_1447_pad_type_0, strides = var_1443, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_135_cast)[name = tensor("op_1447_cast")]; + tensor var_1448_split_sizes_0 = const()[name = tensor("op_1448_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1448_axis_0 = const()[name = tensor("op_1448_axis_0"), val = tensor(1)]; + tensor var_1448_cast_0, tensor var_1448_cast_1 = split(axis = var_1448_axis_0, split_sizes = var_1448_split_sizes_0, x = var_1447_cast)[name = tensor("op_1448_cast")]; + tensor var_1450_mode_0 = const()[name = tensor("op_1450_mode_0"), val = tensor("EXACT")]; + tensor var_1450_cast = gelu(mode = var_1450_mode_0, x = var_1448_cast_1)[name = tensor("op_1450_cast")]; + tensor input_137_cast = mul(x = var_1448_cast_0, y = var_1450_cast)[name = tensor("input_137_cast")]; + tensor var_1454 = const()[name = tensor("op_1454"), val = tensor([1, 1])]; + tensor var_1456 = const()[name = tensor("op_1456"), val = tensor([1, 1])]; + tensor var_1458_pad_type_0 = const()[name = tensor("op_1458_pad_type_0"), val = tensor("custom")]; + tensor var_1458_pad_0 = const()[name = tensor("op_1458_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254568960)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267676224)))]; + tensor var_1458_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1456, groups = var_1186, pad = var_1458_pad_0, pad_type = var_1458_pad_type_0, strides = var_1454, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_137_cast)[name = tensor("op_1458_cast")]; + tensor inputs_31_cast = add(x = var_1458_cast, y = inputs_29_cast)[name = tensor("inputs_31_cast")]; + tensor var_1468 = const()[name = tensor("op_1468"), val = tensor([1])]; + tensor channels_mean_31_cast = reduce_mean(axes = var_1468, keep_dims = var_1181, x = inputs_31_cast)[name = tensor("channels_mean_31_cast")]; + tensor zero_mean_31_cast = sub(x = inputs_31_cast, y = channels_mean_31_cast)[name = tensor("zero_mean_31_cast")]; + tensor zero_mean_sq_31_cast = mul(x = zero_mean_31_cast, y = zero_mean_31_cast)[name = tensor("zero_mean_sq_31_cast")]; + tensor var_1472 = const()[name = tensor("op_1472"), val = tensor([1])]; + tensor var_1473_cast = reduce_mean(axes = var_1472, keep_dims = var_1181, x = zero_mean_sq_31_cast)[name = tensor("op_1473_cast")]; + tensor var_1474_to_fp16 = const()[name = tensor("op_1474_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1475_cast = add(x = var_1473_cast, y = var_1474_to_fp16)[name = tensor("op_1475_cast")]; + tensor denom_31_epsilon_0_to_fp16 = const()[name = tensor("denom_31_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_31_cast = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1475_cast)[name = tensor("denom_31_cast")]; + tensor out_31_cast = mul(x = zero_mean_31_cast, y = denom_31_cast)[name = tensor("out_31_cast")]; + tensor var_1479_to_fp16 = const()[name = tensor("op_1479_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267678848)))]; + tensor var_1480_cast = add(x = out_31_cast, y = var_1479_to_fp16)[name = tensor("op_1480_cast")]; + tensor var_1482_to_fp16 = const()[name = tensor("op_1482_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267681472)))]; + tensor hidden_states_71_cast = mul(x = var_1480_cast, y = var_1482_to_fp16)[name = tensor("hidden_states_71_cast")]; + tensor var_1489 = const()[name = tensor("op_1489"), val = tensor([1, 1])]; + tensor var_1491 = const()[name = tensor("op_1491"), val = tensor([1, 1])]; + tensor q_21_pad_type_0 = const()[name = tensor("q_21_pad_type_0"), val = tensor("custom")]; + tensor q_21_pad_0 = const()[name = tensor("q_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267684096)))]; + tensor q_21_cast = conv(dilations = var_1491, groups = var_1186, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1489, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_71_cast)[name = tensor("q_21_cast")]; + tensor var_1495 = const()[name = tensor("op_1495"), val = tensor([1, 1])]; + tensor var_1497 = const()[name = tensor("op_1497"), val = tensor([1, 1])]; + tensor k_21_pad_type_0 = const()[name = tensor("k_21_pad_type_0"), val = tensor("custom")]; + tensor k_21_pad_0 = const()[name = tensor("k_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270960960)))]; + tensor k_21_cast = conv(dilations = var_1497, groups = var_1186, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1495, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_71_cast)[name = tensor("k_21_cast")]; + tensor var_1501 = const()[name = tensor("op_1501"), val = tensor([1, 1])]; + tensor var_1503 = const()[name = tensor("op_1503"), val = tensor([1, 1])]; + tensor v_21_pad_type_0 = const()[name = tensor("v_21_pad_type_0"), val = tensor("custom")]; + tensor v_21_pad_0 = const()[name = tensor("v_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(274237824)))]; + tensor v_21_cast = conv(dilations = var_1503, groups = var_1186, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1501, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_71_cast)[name = tensor("v_21_cast")]; + tensor var_1507 = const()[name = tensor("op_1507"), val = tensor([2, 20, 64, -1])]; + tensor var_1508_cast = reshape(shape = var_1507, x = q_21_cast)[name = tensor("op_1508_cast")]; + tensor var_1509 = const()[name = tensor("op_1509"), val = tensor([2, 20, 64, -1])]; + tensor var_1510_cast = reshape(shape = var_1509, x = k_21_cast)[name = tensor("op_1510_cast")]; + tensor var_1511 = const()[name = tensor("op_1511"), val = tensor([2, 20, 64, -1])]; + tensor var_1512_cast = reshape(shape = var_1511, x = v_21_cast)[name = tensor("op_1512_cast")]; + tensor attn_weights_41_transpose_x_0 = const()[name = tensor("attn_weights_41_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_41_transpose_y_0 = const()[name = tensor("attn_weights_41_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_41_cast = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1508_cast, y = var_1510_cast)[name = tensor("attn_weights_41_cast")]; + tensor attn_weights_43_cast = mul(x = attn_weights_41_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_43_cast")]; + tensor var_1516_cast = softmax(axis = var_1170, x = attn_weights_43_cast)[name = tensor("op_1516_cast")]; + tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; + tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; + tensor attn_21_cast = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1512_cast, y = var_1516_cast)[name = tensor("attn_21_cast")]; + tensor var_1520 = const()[name = tensor("op_1520"), val = tensor([2, 1280, 1, -1])]; + tensor input_139_cast = reshape(shape = var_1520, x = attn_21_cast)[name = tensor("input_139_cast")]; + tensor var_1525 = const()[name = tensor("op_1525"), val = tensor([1, 1])]; + tensor var_1527 = const()[name = tensor("op_1527"), val = tensor([1, 1])]; + tensor var_1529_pad_type_0 = const()[name = tensor("op_1529_pad_type_0"), val = tensor("custom")]; + tensor var_1529_pad_0 = const()[name = tensor("op_1529_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277514688)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280791552)))]; + tensor var_1529_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1527, groups = var_1186, pad = var_1529_pad_0, pad_type = var_1529_pad_type_0, strides = var_1525, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_139_cast)[name = tensor("op_1529_cast")]; + tensor inputs_33_cast = add(x = var_1529_cast, y = inputs_31_cast)[name = tensor("inputs_33_cast")]; + tensor var_1533 = const()[name = tensor("op_1533"), val = tensor([1])]; + tensor channels_mean_33_cast = reduce_mean(axes = var_1533, keep_dims = var_1181, x = inputs_33_cast)[name = tensor("channels_mean_33_cast")]; + tensor zero_mean_33_cast = sub(x = inputs_33_cast, y = channels_mean_33_cast)[name = tensor("zero_mean_33_cast")]; + tensor zero_mean_sq_33_cast = mul(x = zero_mean_33_cast, y = zero_mean_33_cast)[name = tensor("zero_mean_sq_33_cast")]; + tensor var_1537 = const()[name = tensor("op_1537"), val = tensor([1])]; + tensor var_1538_cast = reduce_mean(axes = var_1537, keep_dims = var_1181, x = zero_mean_sq_33_cast)[name = tensor("op_1538_cast")]; + tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1540_cast = add(x = var_1538_cast, y = var_1539_to_fp16)[name = tensor("op_1540_cast")]; + tensor denom_33_epsilon_0_to_fp16 = const()[name = tensor("denom_33_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_33_cast = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1540_cast)[name = tensor("denom_33_cast")]; + tensor out_33_cast = mul(x = zero_mean_33_cast, y = denom_33_cast)[name = tensor("out_33_cast")]; + tensor var_1544_to_fp16 = const()[name = tensor("op_1544_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280794176)))]; + tensor var_1545_cast = add(x = out_33_cast, y = var_1544_to_fp16)[name = tensor("op_1545_cast")]; + tensor var_1547_to_fp16 = const()[name = tensor("op_1547_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280796800)))]; + tensor hidden_states_73_cast = mul(x = var_1545_cast, y = var_1547_to_fp16)[name = tensor("hidden_states_73_cast")]; + tensor var_1554 = const()[name = tensor("op_1554"), val = tensor([1, 1])]; + tensor var_1556 = const()[name = tensor("op_1556"), val = tensor([1, 1])]; + tensor q_23_pad_type_0 = const()[name = tensor("q_23_pad_type_0"), val = tensor("custom")]; + tensor q_23_pad_0 = const()[name = tensor("q_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280799424)))]; + tensor q_23_cast = conv(dilations = var_1556, groups = var_1186, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1554, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_73_cast)[name = tensor("q_23_cast")]; + tensor var_1560 = const()[name = tensor("op_1560"), val = tensor([1, 1])]; + tensor var_1562 = const()[name = tensor("op_1562"), val = tensor([1, 1])]; + tensor k_23_pad_type_0 = const()[name = tensor("k_23_pad_type_0"), val = tensor("custom")]; + tensor k_23_pad_0 = const()[name = tensor("k_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284076288)))]; + tensor k_23_cast = conv(dilations = var_1562, groups = var_1186, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1560, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_23_cast")]; + tensor var_1566 = const()[name = tensor("op_1566"), val = tensor([1, 1])]; + tensor var_1568 = const()[name = tensor("op_1568"), val = tensor([1, 1])]; + tensor v_23_pad_type_0 = const()[name = tensor("v_23_pad_type_0"), val = tensor("custom")]; + tensor v_23_pad_0 = const()[name = tensor("v_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289319232)))]; + tensor v_23_cast = conv(dilations = var_1568, groups = var_1186, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1566, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_23_cast")]; + tensor var_1572 = const()[name = tensor("op_1572"), val = tensor([2, 20, 64, -1])]; + tensor var_1573_cast = reshape(shape = var_1572, x = q_23_cast)[name = tensor("op_1573_cast")]; + tensor var_1574 = const()[name = tensor("op_1574"), val = tensor([2, 20, 64, -1])]; + tensor var_1575_cast = reshape(shape = var_1574, x = k_23_cast)[name = tensor("op_1575_cast")]; + tensor var_1576 = const()[name = tensor("op_1576"), val = tensor([2, 20, 64, -1])]; + tensor var_1577_cast = reshape(shape = var_1576, x = v_23_cast)[name = tensor("op_1577_cast")]; + tensor attn_weights_45_transpose_x_0 = const()[name = tensor("attn_weights_45_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_45_transpose_y_0 = const()[name = tensor("attn_weights_45_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_45_cast = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1573_cast, y = var_1575_cast)[name = tensor("attn_weights_45_cast")]; + tensor attn_weights_47_cast = mul(x = attn_weights_45_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_47_cast")]; + tensor var_1581_cast = softmax(axis = var_1170, x = attn_weights_47_cast)[name = tensor("op_1581_cast")]; + tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; + tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; + tensor attn_23_cast = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1577_cast, y = var_1581_cast)[name = tensor("attn_23_cast")]; + tensor var_1585 = const()[name = tensor("op_1585"), val = tensor([2, 1280, 1, -1])]; + tensor input_141_cast = reshape(shape = var_1585, x = attn_23_cast)[name = tensor("input_141_cast")]; + tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1, 1])]; + tensor var_1592 = const()[name = tensor("op_1592"), val = tensor([1, 1])]; + tensor var_1594_pad_type_0 = const()[name = tensor("op_1594_pad_type_0"), val = tensor("custom")]; + tensor var_1594_pad_0 = const()[name = tensor("op_1594_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294562176)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297839040)))]; + tensor var_1594_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1592, groups = var_1186, pad = var_1594_pad_0, pad_type = var_1594_pad_type_0, strides = var_1590, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_141_cast)[name = tensor("op_1594_cast")]; + tensor inputs_35_cast = add(x = var_1594_cast, y = inputs_33_cast)[name = tensor("inputs_35_cast")]; + tensor var_1598 = const()[name = tensor("op_1598"), val = tensor([1])]; + tensor channels_mean_35_cast = reduce_mean(axes = var_1598, keep_dims = var_1181, x = inputs_35_cast)[name = tensor("channels_mean_35_cast")]; + tensor zero_mean_35_cast = sub(x = inputs_35_cast, y = channels_mean_35_cast)[name = tensor("zero_mean_35_cast")]; + tensor zero_mean_sq_35_cast = mul(x = zero_mean_35_cast, y = zero_mean_35_cast)[name = tensor("zero_mean_sq_35_cast")]; + tensor var_1602 = const()[name = tensor("op_1602"), val = tensor([1])]; + tensor var_1603_cast = reduce_mean(axes = var_1602, keep_dims = var_1181, x = zero_mean_sq_35_cast)[name = tensor("op_1603_cast")]; + tensor var_1604_to_fp16 = const()[name = tensor("op_1604_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1605_cast = add(x = var_1603_cast, y = var_1604_to_fp16)[name = tensor("op_1605_cast")]; + tensor denom_35_epsilon_0_to_fp16 = const()[name = tensor("denom_35_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_35_cast = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1605_cast)[name = tensor("denom_35_cast")]; + tensor out_35_cast = mul(x = zero_mean_35_cast, y = denom_35_cast)[name = tensor("out_35_cast")]; + tensor var_1609_to_fp16 = const()[name = tensor("op_1609_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297841664)))]; + tensor var_1610_cast = add(x = out_35_cast, y = var_1609_to_fp16)[name = tensor("op_1610_cast")]; + tensor var_1612_to_fp16 = const()[name = tensor("op_1612_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297844288)))]; + tensor input_143_cast = mul(x = var_1610_cast, y = var_1612_to_fp16)[name = tensor("input_143_cast")]; + tensor var_1620 = const()[name = tensor("op_1620"), val = tensor([1, 1])]; + tensor var_1622 = const()[name = tensor("op_1622"), val = tensor([1, 1])]; + tensor var_1624_pad_type_0 = const()[name = tensor("op_1624_pad_type_0"), val = tensor("custom")]; + tensor var_1624_pad_0 = const()[name = tensor("op_1624_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297846912)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324061376)))]; + tensor var_1624_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_1622, groups = var_1186, pad = var_1624_pad_0, pad_type = var_1624_pad_type_0, strides = var_1620, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_143_cast)[name = tensor("op_1624_cast")]; + tensor var_1625_split_sizes_0 = const()[name = tensor("op_1625_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1625_axis_0 = const()[name = tensor("op_1625_axis_0"), val = tensor(1)]; + tensor var_1625_cast_0, tensor var_1625_cast_1 = split(axis = var_1625_axis_0, split_sizes = var_1625_split_sizes_0, x = var_1624_cast)[name = tensor("op_1625_cast")]; + tensor var_1627_mode_0 = const()[name = tensor("op_1627_mode_0"), val = tensor("EXACT")]; + tensor var_1627_cast = gelu(mode = var_1627_mode_0, x = var_1625_cast_1)[name = tensor("op_1627_cast")]; + tensor input_145_cast = mul(x = var_1625_cast_0, y = var_1627_cast)[name = tensor("input_145_cast")]; + tensor var_1631 = const()[name = tensor("op_1631"), val = tensor([1, 1])]; + tensor var_1633 = const()[name = tensor("op_1633"), val = tensor([1, 1])]; + tensor var_1635_pad_type_0 = const()[name = tensor("op_1635_pad_type_0"), val = tensor("custom")]; + tensor var_1635_pad_0 = const()[name = tensor("op_1635_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324081920)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337189184)))]; + tensor var_1635_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1633, groups = var_1186, pad = var_1635_pad_0, pad_type = var_1635_pad_type_0, strides = var_1631, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_145_cast)[name = tensor("op_1635_cast")]; + tensor inputs_37_cast = add(x = var_1635_cast, y = inputs_35_cast)[name = tensor("inputs_37_cast")]; + tensor var_1645 = const()[name = tensor("op_1645"), val = tensor([1])]; + tensor channels_mean_37_cast = reduce_mean(axes = var_1645, keep_dims = var_1181, x = inputs_37_cast)[name = tensor("channels_mean_37_cast")]; + tensor zero_mean_37_cast = sub(x = inputs_37_cast, y = channels_mean_37_cast)[name = tensor("zero_mean_37_cast")]; + tensor zero_mean_sq_37_cast = mul(x = zero_mean_37_cast, y = zero_mean_37_cast)[name = tensor("zero_mean_sq_37_cast")]; + tensor var_1649 = const()[name = tensor("op_1649"), val = tensor([1])]; + tensor var_1650_cast = reduce_mean(axes = var_1649, keep_dims = var_1181, x = zero_mean_sq_37_cast)[name = tensor("op_1650_cast")]; + tensor var_1651_to_fp16 = const()[name = tensor("op_1651_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1652_cast = add(x = var_1650_cast, y = var_1651_to_fp16)[name = tensor("op_1652_cast")]; + tensor denom_37_epsilon_0_to_fp16 = const()[name = tensor("denom_37_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_37_cast = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1652_cast)[name = tensor("denom_37_cast")]; + tensor out_37_cast = mul(x = zero_mean_37_cast, y = denom_37_cast)[name = tensor("out_37_cast")]; + tensor var_1656_to_fp16 = const()[name = tensor("op_1656_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337191808)))]; + tensor var_1657_cast = add(x = out_37_cast, y = var_1656_to_fp16)[name = tensor("op_1657_cast")]; + tensor var_1659_to_fp16 = const()[name = tensor("op_1659_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337194432)))]; + tensor hidden_states_77_cast = mul(x = var_1657_cast, y = var_1659_to_fp16)[name = tensor("hidden_states_77_cast")]; + tensor var_1666 = const()[name = tensor("op_1666"), val = tensor([1, 1])]; + tensor var_1668 = const()[name = tensor("op_1668"), val = tensor([1, 1])]; + tensor q_25_pad_type_0 = const()[name = tensor("q_25_pad_type_0"), val = tensor("custom")]; + tensor q_25_pad_0 = const()[name = tensor("q_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337197056)))]; + tensor q_25_cast = conv(dilations = var_1668, groups = var_1186, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1666, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_77_cast)[name = tensor("q_25_cast")]; + tensor var_1672 = const()[name = tensor("op_1672"), val = tensor([1, 1])]; + tensor var_1674 = const()[name = tensor("op_1674"), val = tensor([1, 1])]; + tensor k_25_pad_type_0 = const()[name = tensor("k_25_pad_type_0"), val = tensor("custom")]; + tensor k_25_pad_0 = const()[name = tensor("k_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(340473920)))]; + tensor k_25_cast = conv(dilations = var_1674, groups = var_1186, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1672, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_77_cast)[name = tensor("k_25_cast")]; + tensor var_1678 = const()[name = tensor("op_1678"), val = tensor([1, 1])]; + tensor var_1680 = const()[name = tensor("op_1680"), val = tensor([1, 1])]; + tensor v_25_pad_type_0 = const()[name = tensor("v_25_pad_type_0"), val = tensor("custom")]; + tensor v_25_pad_0 = const()[name = tensor("v_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343750784)))]; + tensor v_25_cast = conv(dilations = var_1680, groups = var_1186, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1678, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_77_cast)[name = tensor("v_25_cast")]; + tensor var_1684 = const()[name = tensor("op_1684"), val = tensor([2, 20, 64, -1])]; + tensor var_1685_cast = reshape(shape = var_1684, x = q_25_cast)[name = tensor("op_1685_cast")]; + tensor var_1686 = const()[name = tensor("op_1686"), val = tensor([2, 20, 64, -1])]; + tensor var_1687_cast = reshape(shape = var_1686, x = k_25_cast)[name = tensor("op_1687_cast")]; + tensor var_1688 = const()[name = tensor("op_1688"), val = tensor([2, 20, 64, -1])]; + tensor var_1689_cast = reshape(shape = var_1688, x = v_25_cast)[name = tensor("op_1689_cast")]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1685_cast, y = var_1687_cast)[name = tensor("attn_weights_49_cast")]; + tensor attn_weights_51_cast = mul(x = attn_weights_49_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_51_cast")]; + tensor var_1693_cast = softmax(axis = var_1170, x = attn_weights_51_cast)[name = tensor("op_1693_cast")]; + tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; + tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; + tensor attn_25_cast = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1689_cast, y = var_1693_cast)[name = tensor("attn_25_cast")]; + tensor var_1697 = const()[name = tensor("op_1697"), val = tensor([2, 1280, 1, -1])]; + tensor input_147_cast = reshape(shape = var_1697, x = attn_25_cast)[name = tensor("input_147_cast")]; + tensor var_1702 = const()[name = tensor("op_1702"), val = tensor([1, 1])]; + tensor var_1704 = const()[name = tensor("op_1704"), val = tensor([1, 1])]; + tensor var_1706_pad_type_0 = const()[name = tensor("op_1706_pad_type_0"), val = tensor("custom")]; + tensor var_1706_pad_0 = const()[name = tensor("op_1706_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(347027648)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350304512)))]; + tensor var_1706_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_1704, groups = var_1186, pad = var_1706_pad_0, pad_type = var_1706_pad_type_0, strides = var_1702, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_147_cast)[name = tensor("op_1706_cast")]; + tensor inputs_39_cast = add(x = var_1706_cast, y = inputs_37_cast)[name = tensor("inputs_39_cast")]; + tensor var_1710 = const()[name = tensor("op_1710"), val = tensor([1])]; + tensor channels_mean_39_cast = reduce_mean(axes = var_1710, keep_dims = var_1181, x = inputs_39_cast)[name = tensor("channels_mean_39_cast")]; + tensor zero_mean_39_cast = sub(x = inputs_39_cast, y = channels_mean_39_cast)[name = tensor("zero_mean_39_cast")]; + tensor zero_mean_sq_39_cast = mul(x = zero_mean_39_cast, y = zero_mean_39_cast)[name = tensor("zero_mean_sq_39_cast")]; + tensor var_1714 = const()[name = tensor("op_1714"), val = tensor([1])]; + tensor var_1715_cast = reduce_mean(axes = var_1714, keep_dims = var_1181, x = zero_mean_sq_39_cast)[name = tensor("op_1715_cast")]; + tensor var_1716_to_fp16 = const()[name = tensor("op_1716_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1717_cast = add(x = var_1715_cast, y = var_1716_to_fp16)[name = tensor("op_1717_cast")]; + tensor denom_39_epsilon_0_to_fp16 = const()[name = tensor("denom_39_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_39_cast = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1717_cast)[name = tensor("denom_39_cast")]; + tensor out_39_cast = mul(x = zero_mean_39_cast, y = denom_39_cast)[name = tensor("out_39_cast")]; + tensor var_1721_to_fp16 = const()[name = tensor("op_1721_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350307136)))]; + tensor var_1722_cast = add(x = out_39_cast, y = var_1721_to_fp16)[name = tensor("op_1722_cast")]; + tensor var_1724_to_fp16 = const()[name = tensor("op_1724_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350309760)))]; + tensor hidden_states_79_cast = mul(x = var_1722_cast, y = var_1724_to_fp16)[name = tensor("hidden_states_79_cast")]; + tensor var_1731 = const()[name = tensor("op_1731"), val = tensor([1, 1])]; + tensor var_1733 = const()[name = tensor("op_1733"), val = tensor([1, 1])]; + tensor q_27_pad_type_0 = const()[name = tensor("q_27_pad_type_0"), val = tensor("custom")]; + tensor q_27_pad_0 = const()[name = tensor("q_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350312384)))]; + tensor q_27_cast = conv(dilations = var_1733, groups = var_1186, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1731, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_79_cast)[name = tensor("q_27_cast")]; + tensor var_1737 = const()[name = tensor("op_1737"), val = tensor([1, 1])]; + tensor var_1739 = const()[name = tensor("op_1739"), val = tensor([1, 1])]; + tensor k_27_pad_type_0 = const()[name = tensor("k_27_pad_type_0"), val = tensor("custom")]; + tensor k_27_pad_0 = const()[name = tensor("k_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353589248)))]; + tensor k_27_cast = conv(dilations = var_1739, groups = var_1186, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1737, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_27_cast")]; + tensor var_1743 = const()[name = tensor("op_1743"), val = tensor([1, 1])]; + tensor var_1745 = const()[name = tensor("op_1745"), val = tensor([1, 1])]; + tensor v_27_pad_type_0 = const()[name = tensor("v_27_pad_type_0"), val = tensor("custom")]; + tensor v_27_pad_0 = const()[name = tensor("v_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358832192)))]; + tensor v_27_cast = conv(dilations = var_1745, groups = var_1186, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_1743, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_27_cast")]; + tensor var_1749 = const()[name = tensor("op_1749"), val = tensor([2, 20, 64, -1])]; + tensor var_1750_cast = reshape(shape = var_1749, x = q_27_cast)[name = tensor("op_1750_cast")]; + tensor var_1751 = const()[name = tensor("op_1751"), val = tensor([2, 20, 64, -1])]; + tensor var_1752_cast = reshape(shape = var_1751, x = k_27_cast)[name = tensor("op_1752_cast")]; + tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([2, 20, 64, -1])]; + tensor var_1754_cast = reshape(shape = var_1753, x = v_27_cast)[name = tensor("op_1754_cast")]; + tensor attn_weights_53_transpose_x_0 = const()[name = tensor("attn_weights_53_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_53_transpose_y_0 = const()[name = tensor("attn_weights_53_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_53_cast = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_1750_cast, y = var_1752_cast)[name = tensor("attn_weights_53_cast")]; + tensor attn_weights_55_cast = mul(x = attn_weights_53_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_55_cast")]; + tensor var_1758_cast = softmax(axis = var_1170, x = attn_weights_55_cast)[name = tensor("op_1758_cast")]; + tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; + tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; + tensor attn_27_cast = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_1754_cast, y = var_1758_cast)[name = tensor("attn_27_cast")]; + tensor var_1762 = const()[name = tensor("op_1762"), val = tensor([2, 1280, 1, -1])]; + tensor input_149_cast = reshape(shape = var_1762, x = attn_27_cast)[name = tensor("input_149_cast")]; + tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, 1])]; + tensor var_1769 = const()[name = tensor("op_1769"), val = tensor([1, 1])]; + tensor var_1771_pad_type_0 = const()[name = tensor("op_1771_pad_type_0"), val = tensor("custom")]; + tensor var_1771_pad_0 = const()[name = tensor("op_1771_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364075136)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367352000)))]; + tensor var_1771_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_1769, groups = var_1186, pad = var_1771_pad_0, pad_type = var_1771_pad_type_0, strides = var_1767, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_149_cast)[name = tensor("op_1771_cast")]; + tensor inputs_41_cast = add(x = var_1771_cast, y = inputs_39_cast)[name = tensor("inputs_41_cast")]; + tensor var_1775 = const()[name = tensor("op_1775"), val = tensor([1])]; + tensor channels_mean_41_cast = reduce_mean(axes = var_1775, keep_dims = var_1181, x = inputs_41_cast)[name = tensor("channels_mean_41_cast")]; + tensor zero_mean_41_cast = sub(x = inputs_41_cast, y = channels_mean_41_cast)[name = tensor("zero_mean_41_cast")]; + tensor zero_mean_sq_41_cast = mul(x = zero_mean_41_cast, y = zero_mean_41_cast)[name = tensor("zero_mean_sq_41_cast")]; + tensor var_1779 = const()[name = tensor("op_1779"), val = tensor([1])]; + tensor var_1780_cast = reduce_mean(axes = var_1779, keep_dims = var_1181, x = zero_mean_sq_41_cast)[name = tensor("op_1780_cast")]; + tensor var_1781_to_fp16 = const()[name = tensor("op_1781_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1782_cast = add(x = var_1780_cast, y = var_1781_to_fp16)[name = tensor("op_1782_cast")]; + tensor denom_41_epsilon_0_to_fp16 = const()[name = tensor("denom_41_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_41_cast = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_1782_cast)[name = tensor("denom_41_cast")]; + tensor out_41_cast = mul(x = zero_mean_41_cast, y = denom_41_cast)[name = tensor("out_41_cast")]; + tensor var_1786_to_fp16 = const()[name = tensor("op_1786_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367354624)))]; + tensor var_1787_cast = add(x = out_41_cast, y = var_1786_to_fp16)[name = tensor("op_1787_cast")]; + tensor var_1789_to_fp16 = const()[name = tensor("op_1789_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367357248)))]; + tensor input_151_cast = mul(x = var_1787_cast, y = var_1789_to_fp16)[name = tensor("input_151_cast")]; + tensor var_1797 = const()[name = tensor("op_1797"), val = tensor([1, 1])]; + tensor var_1799 = const()[name = tensor("op_1799"), val = tensor([1, 1])]; + tensor var_1801_pad_type_0 = const()[name = tensor("op_1801_pad_type_0"), val = tensor("custom")]; + tensor var_1801_pad_0 = const()[name = tensor("op_1801_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367359872)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393574336)))]; + tensor var_1801_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_1799, groups = var_1186, pad = var_1801_pad_0, pad_type = var_1801_pad_type_0, strides = var_1797, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_151_cast)[name = tensor("op_1801_cast")]; + tensor var_1802_split_sizes_0 = const()[name = tensor("op_1802_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1802_axis_0 = const()[name = tensor("op_1802_axis_0"), val = tensor(1)]; + tensor var_1802_cast_0, tensor var_1802_cast_1 = split(axis = var_1802_axis_0, split_sizes = var_1802_split_sizes_0, x = var_1801_cast)[name = tensor("op_1802_cast")]; + tensor var_1804_mode_0 = const()[name = tensor("op_1804_mode_0"), val = tensor("EXACT")]; + tensor var_1804_cast = gelu(mode = var_1804_mode_0, x = var_1802_cast_1)[name = tensor("op_1804_cast")]; + tensor input_153_cast = mul(x = var_1802_cast_0, y = var_1804_cast)[name = tensor("input_153_cast")]; + tensor var_1808 = const()[name = tensor("op_1808"), val = tensor([1, 1])]; + tensor var_1810 = const()[name = tensor("op_1810"), val = tensor([1, 1])]; + tensor var_1812_pad_type_0 = const()[name = tensor("op_1812_pad_type_0"), val = tensor("custom")]; + tensor var_1812_pad_0 = const()[name = tensor("op_1812_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393594880)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406702144)))]; + tensor var_1812_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_1810, groups = var_1186, pad = var_1812_pad_0, pad_type = var_1812_pad_type_0, strides = var_1808, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_153_cast)[name = tensor("op_1812_cast")]; + tensor inputs_43_cast = add(x = var_1812_cast, y = inputs_41_cast)[name = tensor("inputs_43_cast")]; + tensor var_1822 = const()[name = tensor("op_1822"), val = tensor([1])]; + tensor channels_mean_43_cast = reduce_mean(axes = var_1822, keep_dims = var_1181, x = inputs_43_cast)[name = tensor("channels_mean_43_cast")]; + tensor zero_mean_43_cast = sub(x = inputs_43_cast, y = channels_mean_43_cast)[name = tensor("zero_mean_43_cast")]; + tensor zero_mean_sq_43_cast = mul(x = zero_mean_43_cast, y = zero_mean_43_cast)[name = tensor("zero_mean_sq_43_cast")]; + tensor var_1826 = const()[name = tensor("op_1826"), val = tensor([1])]; + tensor var_1827_cast = reduce_mean(axes = var_1826, keep_dims = var_1181, x = zero_mean_sq_43_cast)[name = tensor("op_1827_cast")]; + tensor var_1828_to_fp16 = const()[name = tensor("op_1828_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1829_cast = add(x = var_1827_cast, y = var_1828_to_fp16)[name = tensor("op_1829_cast")]; + tensor denom_43_epsilon_0_to_fp16 = const()[name = tensor("denom_43_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_43_cast = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_1829_cast)[name = tensor("denom_43_cast")]; + tensor out_43_cast = mul(x = zero_mean_43_cast, y = denom_43_cast)[name = tensor("out_43_cast")]; + tensor var_1833_to_fp16 = const()[name = tensor("op_1833_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406704768)))]; + tensor var_1834_cast = add(x = out_43_cast, y = var_1833_to_fp16)[name = tensor("op_1834_cast")]; + tensor var_1836_to_fp16 = const()[name = tensor("op_1836_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406707392)))]; + tensor hidden_states_83_cast = mul(x = var_1834_cast, y = var_1836_to_fp16)[name = tensor("hidden_states_83_cast")]; + tensor var_1843 = const()[name = tensor("op_1843"), val = tensor([1, 1])]; + tensor var_1845 = const()[name = tensor("op_1845"), val = tensor([1, 1])]; + tensor q_29_pad_type_0 = const()[name = tensor("q_29_pad_type_0"), val = tensor("custom")]; + tensor q_29_pad_0 = const()[name = tensor("q_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406710016)))]; + tensor q_29_cast = conv(dilations = var_1845, groups = var_1186, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_1843, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_83_cast)[name = tensor("q_29_cast")]; + tensor var_1849 = const()[name = tensor("op_1849"), val = tensor([1, 1])]; + tensor var_1851 = const()[name = tensor("op_1851"), val = tensor([1, 1])]; + tensor k_29_pad_type_0 = const()[name = tensor("k_29_pad_type_0"), val = tensor("custom")]; + tensor k_29_pad_0 = const()[name = tensor("k_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(409986880)))]; + tensor k_29_cast = conv(dilations = var_1851, groups = var_1186, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_1849, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_83_cast)[name = tensor("k_29_cast")]; + tensor var_1855 = const()[name = tensor("op_1855"), val = tensor([1, 1])]; + tensor var_1857 = const()[name = tensor("op_1857"), val = tensor([1, 1])]; + tensor v_29_pad_type_0 = const()[name = tensor("v_29_pad_type_0"), val = tensor("custom")]; + tensor v_29_pad_0 = const()[name = tensor("v_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413263744)))]; + tensor v_29_cast = conv(dilations = var_1857, groups = var_1186, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_1855, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_83_cast)[name = tensor("v_29_cast")]; + tensor var_1861 = const()[name = tensor("op_1861"), val = tensor([2, 20, 64, -1])]; + tensor var_1862_cast = reshape(shape = var_1861, x = q_29_cast)[name = tensor("op_1862_cast")]; + tensor var_1863 = const()[name = tensor("op_1863"), val = tensor([2, 20, 64, -1])]; + tensor var_1864_cast = reshape(shape = var_1863, x = k_29_cast)[name = tensor("op_1864_cast")]; + tensor var_1865 = const()[name = tensor("op_1865"), val = tensor([2, 20, 64, -1])]; + tensor var_1866_cast = reshape(shape = var_1865, x = v_29_cast)[name = tensor("op_1866_cast")]; + tensor attn_weights_57_transpose_x_0 = const()[name = tensor("attn_weights_57_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_57_transpose_y_0 = const()[name = tensor("attn_weights_57_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_57_cast = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_1862_cast, y = var_1864_cast)[name = tensor("attn_weights_57_cast")]; + tensor attn_weights_59_cast = mul(x = attn_weights_57_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_59_cast")]; + tensor var_1870_cast = softmax(axis = var_1170, x = attn_weights_59_cast)[name = tensor("op_1870_cast")]; + tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; + tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; + tensor attn_29_cast = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_1866_cast, y = var_1870_cast)[name = tensor("attn_29_cast")]; + tensor var_1874 = const()[name = tensor("op_1874"), val = tensor([2, 1280, 1, -1])]; + tensor input_155_cast = reshape(shape = var_1874, x = attn_29_cast)[name = tensor("input_155_cast")]; + tensor var_1879 = const()[name = tensor("op_1879"), val = tensor([1, 1])]; + tensor var_1881 = const()[name = tensor("op_1881"), val = tensor([1, 1])]; + tensor var_1883_pad_type_0 = const()[name = tensor("op_1883_pad_type_0"), val = tensor("custom")]; + tensor var_1883_pad_0 = const()[name = tensor("op_1883_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416540608)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419817472)))]; + tensor var_1883_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_1881, groups = var_1186, pad = var_1883_pad_0, pad_type = var_1883_pad_type_0, strides = var_1879, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_155_cast)[name = tensor("op_1883_cast")]; + tensor inputs_45_cast = add(x = var_1883_cast, y = inputs_43_cast)[name = tensor("inputs_45_cast")]; + tensor var_1887 = const()[name = tensor("op_1887"), val = tensor([1])]; + tensor channels_mean_45_cast = reduce_mean(axes = var_1887, keep_dims = var_1181, x = inputs_45_cast)[name = tensor("channels_mean_45_cast")]; + tensor zero_mean_45_cast = sub(x = inputs_45_cast, y = channels_mean_45_cast)[name = tensor("zero_mean_45_cast")]; + tensor zero_mean_sq_45_cast = mul(x = zero_mean_45_cast, y = zero_mean_45_cast)[name = tensor("zero_mean_sq_45_cast")]; + tensor var_1891 = const()[name = tensor("op_1891"), val = tensor([1])]; + tensor var_1892_cast = reduce_mean(axes = var_1891, keep_dims = var_1181, x = zero_mean_sq_45_cast)[name = tensor("op_1892_cast")]; + tensor var_1893_to_fp16 = const()[name = tensor("op_1893_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1894_cast = add(x = var_1892_cast, y = var_1893_to_fp16)[name = tensor("op_1894_cast")]; + tensor denom_45_epsilon_0_to_fp16 = const()[name = tensor("denom_45_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_45_cast = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_1894_cast)[name = tensor("denom_45_cast")]; + tensor out_45_cast = mul(x = zero_mean_45_cast, y = denom_45_cast)[name = tensor("out_45_cast")]; + tensor var_1898_to_fp16 = const()[name = tensor("op_1898_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419820096)))]; + tensor var_1899_cast = add(x = out_45_cast, y = var_1898_to_fp16)[name = tensor("op_1899_cast")]; + tensor var_1901_to_fp16 = const()[name = tensor("op_1901_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419822720)))]; + tensor hidden_states_85_cast = mul(x = var_1899_cast, y = var_1901_to_fp16)[name = tensor("hidden_states_85_cast")]; + tensor var_1908 = const()[name = tensor("op_1908"), val = tensor([1, 1])]; + tensor var_1910 = const()[name = tensor("op_1910"), val = tensor([1, 1])]; + tensor q_31_pad_type_0 = const()[name = tensor("q_31_pad_type_0"), val = tensor("custom")]; + tensor q_31_pad_0 = const()[name = tensor("q_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419825344)))]; + tensor q_31_cast = conv(dilations = var_1910, groups = var_1186, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_1908, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_85_cast)[name = tensor("q_31_cast")]; + tensor var_1914 = const()[name = tensor("op_1914"), val = tensor([1, 1])]; + tensor var_1916 = const()[name = tensor("op_1916"), val = tensor([1, 1])]; + tensor k_31_pad_type_0 = const()[name = tensor("k_31_pad_type_0"), val = tensor("custom")]; + tensor k_31_pad_0 = const()[name = tensor("k_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(423102208)))]; + tensor k_31_cast = conv(dilations = var_1916, groups = var_1186, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_1914, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_31_cast")]; + tensor var_1920 = const()[name = tensor("op_1920"), val = tensor([1, 1])]; + tensor var_1922 = const()[name = tensor("op_1922"), val = tensor([1, 1])]; + tensor v_31_pad_type_0 = const()[name = tensor("v_31_pad_type_0"), val = tensor("custom")]; + tensor v_31_pad_0 = const()[name = tensor("v_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428345152)))]; + tensor v_31_cast = conv(dilations = var_1922, groups = var_1186, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_1920, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_31_cast")]; + tensor var_1926 = const()[name = tensor("op_1926"), val = tensor([2, 20, 64, -1])]; + tensor var_1927_cast = reshape(shape = var_1926, x = q_31_cast)[name = tensor("op_1927_cast")]; + tensor var_1928 = const()[name = tensor("op_1928"), val = tensor([2, 20, 64, -1])]; + tensor var_1929_cast = reshape(shape = var_1928, x = k_31_cast)[name = tensor("op_1929_cast")]; + tensor var_1930 = const()[name = tensor("op_1930"), val = tensor([2, 20, 64, -1])]; + tensor var_1931_cast = reshape(shape = var_1930, x = v_31_cast)[name = tensor("op_1931_cast")]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_1927_cast, y = var_1929_cast)[name = tensor("attn_weights_61_cast")]; + tensor attn_weights_63_cast = mul(x = attn_weights_61_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_63_cast")]; + tensor var_1935_cast = softmax(axis = var_1170, x = attn_weights_63_cast)[name = tensor("op_1935_cast")]; + tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; + tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; + tensor attn_31_cast = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_1931_cast, y = var_1935_cast)[name = tensor("attn_31_cast")]; + tensor var_1939 = const()[name = tensor("op_1939"), val = tensor([2, 1280, 1, -1])]; + tensor input_157_cast = reshape(shape = var_1939, x = attn_31_cast)[name = tensor("input_157_cast")]; + tensor var_1944 = const()[name = tensor("op_1944"), val = tensor([1, 1])]; + tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([1, 1])]; + tensor var_1948_pad_type_0 = const()[name = tensor("op_1948_pad_type_0"), val = tensor("custom")]; + tensor var_1948_pad_0 = const()[name = tensor("op_1948_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433588096)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436864960)))]; + tensor var_1948_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_1946, groups = var_1186, pad = var_1948_pad_0, pad_type = var_1948_pad_type_0, strides = var_1944, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_157_cast)[name = tensor("op_1948_cast")]; + tensor inputs_47_cast = add(x = var_1948_cast, y = inputs_45_cast)[name = tensor("inputs_47_cast")]; + tensor var_1952 = const()[name = tensor("op_1952"), val = tensor([1])]; + tensor channels_mean_47_cast = reduce_mean(axes = var_1952, keep_dims = var_1181, x = inputs_47_cast)[name = tensor("channels_mean_47_cast")]; + tensor zero_mean_47_cast = sub(x = inputs_47_cast, y = channels_mean_47_cast)[name = tensor("zero_mean_47_cast")]; + tensor zero_mean_sq_47_cast = mul(x = zero_mean_47_cast, y = zero_mean_47_cast)[name = tensor("zero_mean_sq_47_cast")]; + tensor var_1956 = const()[name = tensor("op_1956"), val = tensor([1])]; + tensor var_1957_cast = reduce_mean(axes = var_1956, keep_dims = var_1181, x = zero_mean_sq_47_cast)[name = tensor("op_1957_cast")]; + tensor var_1958_to_fp16 = const()[name = tensor("op_1958_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1959_cast = add(x = var_1957_cast, y = var_1958_to_fp16)[name = tensor("op_1959_cast")]; + tensor denom_47_epsilon_0_to_fp16 = const()[name = tensor("denom_47_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_47_cast = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_1959_cast)[name = tensor("denom_47_cast")]; + tensor out_47_cast = mul(x = zero_mean_47_cast, y = denom_47_cast)[name = tensor("out_47_cast")]; + tensor var_1963_to_fp16 = const()[name = tensor("op_1963_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436867584)))]; + tensor var_1964_cast = add(x = out_47_cast, y = var_1963_to_fp16)[name = tensor("op_1964_cast")]; + tensor var_1966_to_fp16 = const()[name = tensor("op_1966_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436870208)))]; + tensor input_159_cast = mul(x = var_1964_cast, y = var_1966_to_fp16)[name = tensor("input_159_cast")]; + tensor var_1974 = const()[name = tensor("op_1974"), val = tensor([1, 1])]; + tensor var_1976 = const()[name = tensor("op_1976"), val = tensor([1, 1])]; + tensor var_1978_pad_type_0 = const()[name = tensor("op_1978_pad_type_0"), val = tensor("custom")]; + tensor var_1978_pad_0 = const()[name = tensor("op_1978_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436872832)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(463087296)))]; + tensor var_1978_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_1976, groups = var_1186, pad = var_1978_pad_0, pad_type = var_1978_pad_type_0, strides = var_1974, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_159_cast)[name = tensor("op_1978_cast")]; + tensor var_1979_split_sizes_0 = const()[name = tensor("op_1979_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1979_axis_0 = const()[name = tensor("op_1979_axis_0"), val = tensor(1)]; + tensor var_1979_cast_0, tensor var_1979_cast_1 = split(axis = var_1979_axis_0, split_sizes = var_1979_split_sizes_0, x = var_1978_cast)[name = tensor("op_1979_cast")]; + tensor var_1981_mode_0 = const()[name = tensor("op_1981_mode_0"), val = tensor("EXACT")]; + tensor var_1981_cast = gelu(mode = var_1981_mode_0, x = var_1979_cast_1)[name = tensor("op_1981_cast")]; + tensor input_161_cast = mul(x = var_1979_cast_0, y = var_1981_cast)[name = tensor("input_161_cast")]; + tensor var_1985 = const()[name = tensor("op_1985"), val = tensor([1, 1])]; + tensor var_1987 = const()[name = tensor("op_1987"), val = tensor([1, 1])]; + tensor var_1989_pad_type_0 = const()[name = tensor("op_1989_pad_type_0"), val = tensor("custom")]; + tensor var_1989_pad_0 = const()[name = tensor("op_1989_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(463107840)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476215104)))]; + tensor var_1989_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_1987, groups = var_1186, pad = var_1989_pad_0, pad_type = var_1989_pad_type_0, strides = var_1985, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_161_cast)[name = tensor("op_1989_cast")]; + tensor inputs_49_cast = add(x = var_1989_cast, y = inputs_47_cast)[name = tensor("inputs_49_cast")]; + tensor var_1999 = const()[name = tensor("op_1999"), val = tensor([1])]; + tensor channels_mean_49_cast = reduce_mean(axes = var_1999, keep_dims = var_1181, x = inputs_49_cast)[name = tensor("channels_mean_49_cast")]; + tensor zero_mean_49_cast = sub(x = inputs_49_cast, y = channels_mean_49_cast)[name = tensor("zero_mean_49_cast")]; + tensor zero_mean_sq_49_cast = mul(x = zero_mean_49_cast, y = zero_mean_49_cast)[name = tensor("zero_mean_sq_49_cast")]; + tensor var_2003 = const()[name = tensor("op_2003"), val = tensor([1])]; + tensor var_2004_cast = reduce_mean(axes = var_2003, keep_dims = var_1181, x = zero_mean_sq_49_cast)[name = tensor("op_2004_cast")]; + tensor var_2005_to_fp16 = const()[name = tensor("op_2005_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2006_cast = add(x = var_2004_cast, y = var_2005_to_fp16)[name = tensor("op_2006_cast")]; + tensor denom_49_epsilon_0_to_fp16 = const()[name = tensor("denom_49_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_49_cast = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_2006_cast)[name = tensor("denom_49_cast")]; + tensor out_49_cast = mul(x = zero_mean_49_cast, y = denom_49_cast)[name = tensor("out_49_cast")]; + tensor var_2010_to_fp16 = const()[name = tensor("op_2010_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476217728)))]; + tensor var_2011_cast = add(x = out_49_cast, y = var_2010_to_fp16)[name = tensor("op_2011_cast")]; + tensor var_2013_to_fp16 = const()[name = tensor("op_2013_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476220352)))]; + tensor hidden_states_89_cast = mul(x = var_2011_cast, y = var_2013_to_fp16)[name = tensor("hidden_states_89_cast")]; + tensor var_2020 = const()[name = tensor("op_2020"), val = tensor([1, 1])]; + tensor var_2022 = const()[name = tensor("op_2022"), val = tensor([1, 1])]; + tensor q_33_pad_type_0 = const()[name = tensor("q_33_pad_type_0"), val = tensor("custom")]; + tensor q_33_pad_0 = const()[name = tensor("q_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476222976)))]; + tensor q_33_cast = conv(dilations = var_2022, groups = var_1186, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2020, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_89_cast)[name = tensor("q_33_cast")]; + tensor var_2026 = const()[name = tensor("op_2026"), val = tensor([1, 1])]; + tensor var_2028 = const()[name = tensor("op_2028"), val = tensor([1, 1])]; + tensor k_33_pad_type_0 = const()[name = tensor("k_33_pad_type_0"), val = tensor("custom")]; + tensor k_33_pad_0 = const()[name = tensor("k_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(479499840)))]; + tensor k_33_cast = conv(dilations = var_2028, groups = var_1186, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2026, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_89_cast)[name = tensor("k_33_cast")]; + tensor var_2032 = const()[name = tensor("op_2032"), val = tensor([1, 1])]; + tensor var_2034 = const()[name = tensor("op_2034"), val = tensor([1, 1])]; + tensor v_33_pad_type_0 = const()[name = tensor("v_33_pad_type_0"), val = tensor("custom")]; + tensor v_33_pad_0 = const()[name = tensor("v_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(482776704)))]; + tensor v_33_cast = conv(dilations = var_2034, groups = var_1186, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2032, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_89_cast)[name = tensor("v_33_cast")]; + tensor var_2038 = const()[name = tensor("op_2038"), val = tensor([2, 20, 64, -1])]; + tensor var_2039_cast = reshape(shape = var_2038, x = q_33_cast)[name = tensor("op_2039_cast")]; + tensor var_2040 = const()[name = tensor("op_2040"), val = tensor([2, 20, 64, -1])]; + tensor var_2041_cast = reshape(shape = var_2040, x = k_33_cast)[name = tensor("op_2041_cast")]; + tensor var_2042 = const()[name = tensor("op_2042"), val = tensor([2, 20, 64, -1])]; + tensor var_2043_cast = reshape(shape = var_2042, x = v_33_cast)[name = tensor("op_2043_cast")]; + tensor attn_weights_65_transpose_x_0 = const()[name = tensor("attn_weights_65_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_65_transpose_y_0 = const()[name = tensor("attn_weights_65_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_65_cast = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2039_cast, y = var_2041_cast)[name = tensor("attn_weights_65_cast")]; + tensor attn_weights_67_cast = mul(x = attn_weights_65_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_67_cast")]; + tensor var_2047_cast = softmax(axis = var_1170, x = attn_weights_67_cast)[name = tensor("op_2047_cast")]; + tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; + tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; + tensor attn_33_cast = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2043_cast, y = var_2047_cast)[name = tensor("attn_33_cast")]; + tensor var_2051 = const()[name = tensor("op_2051"), val = tensor([2, 1280, 1, -1])]; + tensor input_163_cast = reshape(shape = var_2051, x = attn_33_cast)[name = tensor("input_163_cast")]; + tensor var_2056 = const()[name = tensor("op_2056"), val = tensor([1, 1])]; + tensor var_2058 = const()[name = tensor("op_2058"), val = tensor([1, 1])]; + tensor var_2060_pad_type_0 = const()[name = tensor("op_2060_pad_type_0"), val = tensor("custom")]; + tensor var_2060_pad_0 = const()[name = tensor("op_2060_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486053568)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489330432)))]; + tensor var_2060_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_2058, groups = var_1186, pad = var_2060_pad_0, pad_type = var_2060_pad_type_0, strides = var_2056, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_163_cast)[name = tensor("op_2060_cast")]; + tensor inputs_51_cast = add(x = var_2060_cast, y = inputs_49_cast)[name = tensor("inputs_51_cast")]; + tensor var_2064 = const()[name = tensor("op_2064"), val = tensor([1])]; + tensor channels_mean_51_cast = reduce_mean(axes = var_2064, keep_dims = var_1181, x = inputs_51_cast)[name = tensor("channels_mean_51_cast")]; + tensor zero_mean_51_cast = sub(x = inputs_51_cast, y = channels_mean_51_cast)[name = tensor("zero_mean_51_cast")]; + tensor zero_mean_sq_51_cast = mul(x = zero_mean_51_cast, y = zero_mean_51_cast)[name = tensor("zero_mean_sq_51_cast")]; + tensor var_2068 = const()[name = tensor("op_2068"), val = tensor([1])]; + tensor var_2069_cast = reduce_mean(axes = var_2068, keep_dims = var_1181, x = zero_mean_sq_51_cast)[name = tensor("op_2069_cast")]; + tensor var_2070_to_fp16 = const()[name = tensor("op_2070_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2071_cast = add(x = var_2069_cast, y = var_2070_to_fp16)[name = tensor("op_2071_cast")]; + tensor denom_51_epsilon_0_to_fp16 = const()[name = tensor("denom_51_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_51_cast = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2071_cast)[name = tensor("denom_51_cast")]; + tensor out_51_cast = mul(x = zero_mean_51_cast, y = denom_51_cast)[name = tensor("out_51_cast")]; + tensor var_2075_to_fp16 = const()[name = tensor("op_2075_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489333056)))]; + tensor var_2076_cast = add(x = out_51_cast, y = var_2075_to_fp16)[name = tensor("op_2076_cast")]; + tensor var_2078_to_fp16 = const()[name = tensor("op_2078_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489335680)))]; + tensor hidden_states_91_cast = mul(x = var_2076_cast, y = var_2078_to_fp16)[name = tensor("hidden_states_91_cast")]; + tensor var_2085 = const()[name = tensor("op_2085"), val = tensor([1, 1])]; + tensor var_2087 = const()[name = tensor("op_2087"), val = tensor([1, 1])]; + tensor q_35_pad_type_0 = const()[name = tensor("q_35_pad_type_0"), val = tensor("custom")]; + tensor q_35_pad_0 = const()[name = tensor("q_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489338304)))]; + tensor q_35_cast = conv(dilations = var_2087, groups = var_1186, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2085, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_91_cast)[name = tensor("q_35_cast")]; + tensor var_2091 = const()[name = tensor("op_2091"), val = tensor([1, 1])]; + tensor var_2093 = const()[name = tensor("op_2093"), val = tensor([1, 1])]; + tensor k_35_pad_type_0 = const()[name = tensor("k_35_pad_type_0"), val = tensor("custom")]; + tensor k_35_pad_0 = const()[name = tensor("k_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492615168)))]; + tensor k_35_cast = conv(dilations = var_2093, groups = var_1186, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2091, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_35_cast")]; + tensor var_2097 = const()[name = tensor("op_2097"), val = tensor([1, 1])]; + tensor var_2099 = const()[name = tensor("op_2099"), val = tensor([1, 1])]; + tensor v_35_pad_type_0 = const()[name = tensor("v_35_pad_type_0"), val = tensor("custom")]; + tensor v_35_pad_0 = const()[name = tensor("v_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(497858112)))]; + tensor v_35_cast = conv(dilations = var_2099, groups = var_1186, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2097, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_35_cast")]; + tensor var_2103 = const()[name = tensor("op_2103"), val = tensor([2, 20, 64, -1])]; + tensor var_2104_cast = reshape(shape = var_2103, x = q_35_cast)[name = tensor("op_2104_cast")]; + tensor var_2105 = const()[name = tensor("op_2105"), val = tensor([2, 20, 64, -1])]; + tensor var_2106_cast = reshape(shape = var_2105, x = k_35_cast)[name = tensor("op_2106_cast")]; + tensor var_2107 = const()[name = tensor("op_2107"), val = tensor([2, 20, 64, -1])]; + tensor var_2108_cast = reshape(shape = var_2107, x = v_35_cast)[name = tensor("op_2108_cast")]; + tensor attn_weights_69_transpose_x_0 = const()[name = tensor("attn_weights_69_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_69_transpose_y_0 = const()[name = tensor("attn_weights_69_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_69_cast = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2104_cast, y = var_2106_cast)[name = tensor("attn_weights_69_cast")]; + tensor attn_weights_71_cast = mul(x = attn_weights_69_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_71_cast")]; + tensor var_2112_cast = softmax(axis = var_1170, x = attn_weights_71_cast)[name = tensor("op_2112_cast")]; + tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; + tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; + tensor attn_35_cast = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2108_cast, y = var_2112_cast)[name = tensor("attn_35_cast")]; + tensor var_2116 = const()[name = tensor("op_2116"), val = tensor([2, 1280, 1, -1])]; + tensor input_165_cast = reshape(shape = var_2116, x = attn_35_cast)[name = tensor("input_165_cast")]; + tensor var_2121 = const()[name = tensor("op_2121"), val = tensor([1, 1])]; + tensor var_2123 = const()[name = tensor("op_2123"), val = tensor([1, 1])]; + tensor var_2125_pad_type_0 = const()[name = tensor("op_2125_pad_type_0"), val = tensor("custom")]; + tensor var_2125_pad_0 = const()[name = tensor("op_2125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503101056)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(506377920)))]; + tensor var_2125_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_2123, groups = var_1186, pad = var_2125_pad_0, pad_type = var_2125_pad_type_0, strides = var_2121, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_165_cast)[name = tensor("op_2125_cast")]; + tensor inputs_53_cast = add(x = var_2125_cast, y = inputs_51_cast)[name = tensor("inputs_53_cast")]; + tensor var_2129 = const()[name = tensor("op_2129"), val = tensor([1])]; + tensor channels_mean_53_cast = reduce_mean(axes = var_2129, keep_dims = var_1181, x = inputs_53_cast)[name = tensor("channels_mean_53_cast")]; + tensor zero_mean_53_cast = sub(x = inputs_53_cast, y = channels_mean_53_cast)[name = tensor("zero_mean_53_cast")]; + tensor zero_mean_sq_53_cast = mul(x = zero_mean_53_cast, y = zero_mean_53_cast)[name = tensor("zero_mean_sq_53_cast")]; + tensor var_2133 = const()[name = tensor("op_2133"), val = tensor([1])]; + tensor var_2134_cast = reduce_mean(axes = var_2133, keep_dims = var_1181, x = zero_mean_sq_53_cast)[name = tensor("op_2134_cast")]; + tensor var_2135_to_fp16 = const()[name = tensor("op_2135_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2136_cast = add(x = var_2134_cast, y = var_2135_to_fp16)[name = tensor("op_2136_cast")]; + tensor denom_53_epsilon_0_to_fp16 = const()[name = tensor("denom_53_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_53_cast = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2136_cast)[name = tensor("denom_53_cast")]; + tensor out_53_cast = mul(x = zero_mean_53_cast, y = denom_53_cast)[name = tensor("out_53_cast")]; + tensor var_2140_to_fp16 = const()[name = tensor("op_2140_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(506380544)))]; + tensor var_2141_cast = add(x = out_53_cast, y = var_2140_to_fp16)[name = tensor("op_2141_cast")]; + tensor var_2143_to_fp16 = const()[name = tensor("op_2143_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(506383168)))]; + tensor input_167_cast = mul(x = var_2141_cast, y = var_2143_to_fp16)[name = tensor("input_167_cast")]; + tensor var_2151 = const()[name = tensor("op_2151"), val = tensor([1, 1])]; + tensor var_2153 = const()[name = tensor("op_2153"), val = tensor([1, 1])]; + tensor var_2155_pad_type_0 = const()[name = tensor("op_2155_pad_type_0"), val = tensor("custom")]; + tensor var_2155_pad_0 = const()[name = tensor("op_2155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(506385792)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(532600256)))]; + tensor var_2155_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_2153, groups = var_1186, pad = var_2155_pad_0, pad_type = var_2155_pad_type_0, strides = var_2151, weight = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_167_cast)[name = tensor("op_2155_cast")]; + tensor var_2156_split_sizes_0 = const()[name = tensor("op_2156_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2156_axis_0 = const()[name = tensor("op_2156_axis_0"), val = tensor(1)]; + tensor var_2156_cast_0, tensor var_2156_cast_1 = split(axis = var_2156_axis_0, split_sizes = var_2156_split_sizes_0, x = var_2155_cast)[name = tensor("op_2156_cast")]; + tensor var_2158_mode_0 = const()[name = tensor("op_2158_mode_0"), val = tensor("EXACT")]; + tensor var_2158_cast = gelu(mode = var_2158_mode_0, x = var_2156_cast_1)[name = tensor("op_2158_cast")]; + tensor input_169_cast = mul(x = var_2156_cast_0, y = var_2158_cast)[name = tensor("input_169_cast")]; + tensor var_2162 = const()[name = tensor("op_2162"), val = tensor([1, 1])]; + tensor var_2164 = const()[name = tensor("op_2164"), val = tensor([1, 1])]; + tensor var_2166_pad_type_0 = const()[name = tensor("op_2166_pad_type_0"), val = tensor("custom")]; + tensor var_2166_pad_0 = const()[name = tensor("op_2166_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(532620800)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545728064)))]; + tensor var_2166_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_2164, groups = var_1186, pad = var_2166_pad_0, pad_type = var_2166_pad_type_0, strides = var_2162, weight = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_169_cast)[name = tensor("op_2166_cast")]; + tensor inputs_55_cast = add(x = var_2166_cast, y = inputs_53_cast)[name = tensor("inputs_55_cast")]; + tensor var_2176 = const()[name = tensor("op_2176"), val = tensor([1])]; + tensor channels_mean_55_cast = reduce_mean(axes = var_2176, keep_dims = var_1181, x = inputs_55_cast)[name = tensor("channels_mean_55_cast")]; + tensor zero_mean_55_cast = sub(x = inputs_55_cast, y = channels_mean_55_cast)[name = tensor("zero_mean_55_cast")]; + tensor zero_mean_sq_55_cast = mul(x = zero_mean_55_cast, y = zero_mean_55_cast)[name = tensor("zero_mean_sq_55_cast")]; + tensor var_2180 = const()[name = tensor("op_2180"), val = tensor([1])]; + tensor var_2181_cast = reduce_mean(axes = var_2180, keep_dims = var_1181, x = zero_mean_sq_55_cast)[name = tensor("op_2181_cast")]; + tensor var_2182_to_fp16 = const()[name = tensor("op_2182_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2183_cast = add(x = var_2181_cast, y = var_2182_to_fp16)[name = tensor("op_2183_cast")]; + tensor denom_55_epsilon_0_to_fp16 = const()[name = tensor("denom_55_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_55_cast = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2183_cast)[name = tensor("denom_55_cast")]; + tensor out_55_cast = mul(x = zero_mean_55_cast, y = denom_55_cast)[name = tensor("out_55_cast")]; + tensor var_2187_to_fp16 = const()[name = tensor("op_2187_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545730688)))]; + tensor var_2188_cast = add(x = out_55_cast, y = var_2187_to_fp16)[name = tensor("op_2188_cast")]; + tensor var_2190_to_fp16 = const()[name = tensor("op_2190_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545733312)))]; + tensor hidden_states_95_cast = mul(x = var_2188_cast, y = var_2190_to_fp16)[name = tensor("hidden_states_95_cast")]; + tensor var_2197 = const()[name = tensor("op_2197"), val = tensor([1, 1])]; + tensor var_2199 = const()[name = tensor("op_2199"), val = tensor([1, 1])]; + tensor q_37_pad_type_0 = const()[name = tensor("q_37_pad_type_0"), val = tensor("custom")]; + tensor q_37_pad_0 = const()[name = tensor("q_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545735936)))]; + tensor q_37_cast = conv(dilations = var_2199, groups = var_1186, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2197, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_95_cast)[name = tensor("q_37_cast")]; + tensor var_2203 = const()[name = tensor("op_2203"), val = tensor([1, 1])]; + tensor var_2205 = const()[name = tensor("op_2205"), val = tensor([1, 1])]; + tensor k_37_pad_type_0 = const()[name = tensor("k_37_pad_type_0"), val = tensor("custom")]; + tensor k_37_pad_0 = const()[name = tensor("k_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549012800)))]; + tensor k_37_cast = conv(dilations = var_2205, groups = var_1186, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2203, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_95_cast)[name = tensor("k_37_cast")]; + tensor var_2209 = const()[name = tensor("op_2209"), val = tensor([1, 1])]; + tensor var_2211 = const()[name = tensor("op_2211"), val = tensor([1, 1])]; + tensor v_37_pad_type_0 = const()[name = tensor("v_37_pad_type_0"), val = tensor("custom")]; + tensor v_37_pad_0 = const()[name = tensor("v_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(552289664)))]; + tensor v_37_cast = conv(dilations = var_2211, groups = var_1186, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2209, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_95_cast)[name = tensor("v_37_cast")]; + tensor var_2215 = const()[name = tensor("op_2215"), val = tensor([2, 20, 64, -1])]; + tensor var_2216_cast = reshape(shape = var_2215, x = q_37_cast)[name = tensor("op_2216_cast")]; + tensor var_2217 = const()[name = tensor("op_2217"), val = tensor([2, 20, 64, -1])]; + tensor var_2218_cast = reshape(shape = var_2217, x = k_37_cast)[name = tensor("op_2218_cast")]; + tensor var_2219 = const()[name = tensor("op_2219"), val = tensor([2, 20, 64, -1])]; + tensor var_2220_cast = reshape(shape = var_2219, x = v_37_cast)[name = tensor("op_2220_cast")]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_73_cast = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2216_cast, y = var_2218_cast)[name = tensor("attn_weights_73_cast")]; + tensor attn_weights_75_cast = mul(x = attn_weights_73_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_75_cast")]; + tensor var_2224_cast = softmax(axis = var_1170, x = attn_weights_75_cast)[name = tensor("op_2224_cast")]; + tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; + tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; + tensor attn_37_cast = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2220_cast, y = var_2224_cast)[name = tensor("attn_37_cast")]; + tensor var_2228 = const()[name = tensor("op_2228"), val = tensor([2, 1280, 1, -1])]; + tensor input_171_cast = reshape(shape = var_2228, x = attn_37_cast)[name = tensor("input_171_cast")]; + tensor var_2233 = const()[name = tensor("op_2233"), val = tensor([1, 1])]; + tensor var_2235 = const()[name = tensor("op_2235"), val = tensor([1, 1])]; + tensor var_2237_pad_type_0 = const()[name = tensor("op_2237_pad_type_0"), val = tensor("custom")]; + tensor var_2237_pad_0 = const()[name = tensor("op_2237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555566528)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558843392)))]; + tensor var_2237_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_2235, groups = var_1186, pad = var_2237_pad_0, pad_type = var_2237_pad_type_0, strides = var_2233, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_171_cast)[name = tensor("op_2237_cast")]; + tensor inputs_57_cast = add(x = var_2237_cast, y = inputs_55_cast)[name = tensor("inputs_57_cast")]; + tensor var_2241 = const()[name = tensor("op_2241"), val = tensor([1])]; + tensor channels_mean_57_cast = reduce_mean(axes = var_2241, keep_dims = var_1181, x = inputs_57_cast)[name = tensor("channels_mean_57_cast")]; + tensor zero_mean_57_cast = sub(x = inputs_57_cast, y = channels_mean_57_cast)[name = tensor("zero_mean_57_cast")]; + tensor zero_mean_sq_57_cast = mul(x = zero_mean_57_cast, y = zero_mean_57_cast)[name = tensor("zero_mean_sq_57_cast")]; + tensor var_2245 = const()[name = tensor("op_2245"), val = tensor([1])]; + tensor var_2246_cast = reduce_mean(axes = var_2245, keep_dims = var_1181, x = zero_mean_sq_57_cast)[name = tensor("op_2246_cast")]; + tensor var_2247_to_fp16 = const()[name = tensor("op_2247_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2248_cast = add(x = var_2246_cast, y = var_2247_to_fp16)[name = tensor("op_2248_cast")]; + tensor denom_57_epsilon_0_to_fp16 = const()[name = tensor("denom_57_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_57_cast = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2248_cast)[name = tensor("denom_57_cast")]; + tensor out_57_cast = mul(x = zero_mean_57_cast, y = denom_57_cast)[name = tensor("out_57_cast")]; + tensor var_2252_to_fp16 = const()[name = tensor("op_2252_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558846016)))]; + tensor var_2253_cast = add(x = out_57_cast, y = var_2252_to_fp16)[name = tensor("op_2253_cast")]; + tensor var_2255_to_fp16 = const()[name = tensor("op_2255_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558848640)))]; + tensor hidden_states_97_cast = mul(x = var_2253_cast, y = var_2255_to_fp16)[name = tensor("hidden_states_97_cast")]; + tensor var_2262 = const()[name = tensor("op_2262"), val = tensor([1, 1])]; + tensor var_2264 = const()[name = tensor("op_2264"), val = tensor([1, 1])]; + tensor q_39_pad_type_0 = const()[name = tensor("q_39_pad_type_0"), val = tensor("custom")]; + tensor q_39_pad_0 = const()[name = tensor("q_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558851264)))]; + tensor q_39_cast = conv(dilations = var_2264, groups = var_1186, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_2262, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_97_cast)[name = tensor("q_39_cast")]; + tensor var_2268 = const()[name = tensor("op_2268"), val = tensor([1, 1])]; + tensor var_2270 = const()[name = tensor("op_2270"), val = tensor([1, 1])]; + tensor k_39_pad_type_0 = const()[name = tensor("k_39_pad_type_0"), val = tensor("custom")]; + tensor k_39_pad_0 = const()[name = tensor("k_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(562128128)))]; + tensor k_39_cast = conv(dilations = var_2270, groups = var_1186, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_2268, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_39_cast")]; + tensor var_2274 = const()[name = tensor("op_2274"), val = tensor([1, 1])]; + tensor var_2276 = const()[name = tensor("op_2276"), val = tensor([1, 1])]; + tensor v_39_pad_type_0 = const()[name = tensor("v_39_pad_type_0"), val = tensor("custom")]; + tensor v_39_pad_0 = const()[name = tensor("v_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(567371072)))]; + tensor v_39_cast = conv(dilations = var_2276, groups = var_1186, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_2274, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_39_cast")]; + tensor var_2280 = const()[name = tensor("op_2280"), val = tensor([2, 20, 64, -1])]; + tensor var_2281_cast = reshape(shape = var_2280, x = q_39_cast)[name = tensor("op_2281_cast")]; + tensor var_2282 = const()[name = tensor("op_2282"), val = tensor([2, 20, 64, -1])]; + tensor var_2283_cast = reshape(shape = var_2282, x = k_39_cast)[name = tensor("op_2283_cast")]; + tensor var_2284 = const()[name = tensor("op_2284"), val = tensor([2, 20, 64, -1])]; + tensor var_2285_cast = reshape(shape = var_2284, x = v_39_cast)[name = tensor("op_2285_cast")]; + tensor attn_weights_77_transpose_x_0 = const()[name = tensor("attn_weights_77_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_77_transpose_y_0 = const()[name = tensor("attn_weights_77_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_77_cast = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_2281_cast, y = var_2283_cast)[name = tensor("attn_weights_77_cast")]; + tensor attn_weights_79_cast = mul(x = attn_weights_77_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_79_cast")]; + tensor var_2289_cast = softmax(axis = var_1170, x = attn_weights_79_cast)[name = tensor("op_2289_cast")]; + tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; + tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; + tensor attn_39_cast = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_2285_cast, y = var_2289_cast)[name = tensor("attn_39_cast")]; + tensor var_2293 = const()[name = tensor("op_2293"), val = tensor([2, 1280, 1, -1])]; + tensor input_173_cast = reshape(shape = var_2293, x = attn_39_cast)[name = tensor("input_173_cast")]; + tensor var_2298 = const()[name = tensor("op_2298"), val = tensor([1, 1])]; + tensor var_2300 = const()[name = tensor("op_2300"), val = tensor([1, 1])]; + tensor var_2302_pad_type_0 = const()[name = tensor("op_2302_pad_type_0"), val = tensor("custom")]; + tensor var_2302_pad_0 = const()[name = tensor("op_2302_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572614016)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575890880)))]; + tensor var_2302_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_2300, groups = var_1186, pad = var_2302_pad_0, pad_type = var_2302_pad_type_0, strides = var_2298, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_173_cast)[name = tensor("op_2302_cast")]; + tensor inputs_59_cast = add(x = var_2302_cast, y = inputs_57_cast)[name = tensor("inputs_59_cast")]; + tensor var_2306 = const()[name = tensor("op_2306"), val = tensor([1])]; + tensor channels_mean_59_cast = reduce_mean(axes = var_2306, keep_dims = var_1181, x = inputs_59_cast)[name = tensor("channels_mean_59_cast")]; + tensor zero_mean_59_cast = sub(x = inputs_59_cast, y = channels_mean_59_cast)[name = tensor("zero_mean_59_cast")]; + tensor zero_mean_sq_59_cast = mul(x = zero_mean_59_cast, y = zero_mean_59_cast)[name = tensor("zero_mean_sq_59_cast")]; + tensor var_2310 = const()[name = tensor("op_2310"), val = tensor([1])]; + tensor var_2311_cast = reduce_mean(axes = var_2310, keep_dims = var_1181, x = zero_mean_sq_59_cast)[name = tensor("op_2311_cast")]; + tensor var_2312_to_fp16 = const()[name = tensor("op_2312_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2313_cast = add(x = var_2311_cast, y = var_2312_to_fp16)[name = tensor("op_2313_cast")]; + tensor denom_59_epsilon_0_to_fp16 = const()[name = tensor("denom_59_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_59_cast = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_2313_cast)[name = tensor("denom_59_cast")]; + tensor out_59_cast = mul(x = zero_mean_59_cast, y = denom_59_cast)[name = tensor("out_59_cast")]; + tensor var_2317_to_fp16 = const()[name = tensor("op_2317_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575893504)))]; + tensor var_2318_cast = add(x = out_59_cast, y = var_2317_to_fp16)[name = tensor("op_2318_cast")]; + tensor var_2320_to_fp16 = const()[name = tensor("op_2320_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575896128)))]; + tensor input_175_cast = mul(x = var_2318_cast, y = var_2320_to_fp16)[name = tensor("input_175_cast")]; + tensor var_2328 = const()[name = tensor("op_2328"), val = tensor([1, 1])]; + tensor var_2330 = const()[name = tensor("op_2330"), val = tensor([1, 1])]; + tensor var_2332_pad_type_0 = const()[name = tensor("op_2332_pad_type_0"), val = tensor("custom")]; + tensor var_2332_pad_0 = const()[name = tensor("op_2332_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575898752)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602113216)))]; + tensor var_2332_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_2330, groups = var_1186, pad = var_2332_pad_0, pad_type = var_2332_pad_type_0, strides = var_2328, weight = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_175_cast)[name = tensor("op_2332_cast")]; + tensor var_2333_split_sizes_0 = const()[name = tensor("op_2333_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2333_axis_0 = const()[name = tensor("op_2333_axis_0"), val = tensor(1)]; + tensor var_2333_cast_0, tensor var_2333_cast_1 = split(axis = var_2333_axis_0, split_sizes = var_2333_split_sizes_0, x = var_2332_cast)[name = tensor("op_2333_cast")]; + tensor var_2335_mode_0 = const()[name = tensor("op_2335_mode_0"), val = tensor("EXACT")]; + tensor var_2335_cast = gelu(mode = var_2335_mode_0, x = var_2333_cast_1)[name = tensor("op_2335_cast")]; + tensor input_177_cast = mul(x = var_2333_cast_0, y = var_2335_cast)[name = tensor("input_177_cast")]; + tensor var_2339 = const()[name = tensor("op_2339"), val = tensor([1, 1])]; + tensor var_2341 = const()[name = tensor("op_2341"), val = tensor([1, 1])]; + tensor var_2343_pad_type_0 = const()[name = tensor("op_2343_pad_type_0"), val = tensor("custom")]; + tensor var_2343_pad_0 = const()[name = tensor("op_2343_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602133760)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(615241024)))]; + tensor var_2343_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_2341, groups = var_1186, pad = var_2343_pad_0, pad_type = var_2343_pad_type_0, strides = var_2339, weight = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_177_cast)[name = tensor("op_2343_cast")]; + tensor inputs_61_cast = add(x = var_2343_cast, y = inputs_59_cast)[name = tensor("inputs_61_cast")]; + tensor var_2353 = const()[name = tensor("op_2353"), val = tensor([1])]; + tensor channels_mean_61_cast = reduce_mean(axes = var_2353, keep_dims = var_1181, x = inputs_61_cast)[name = tensor("channels_mean_61_cast")]; + tensor zero_mean_61_cast = sub(x = inputs_61_cast, y = channels_mean_61_cast)[name = tensor("zero_mean_61_cast")]; + tensor zero_mean_sq_61_cast = mul(x = zero_mean_61_cast, y = zero_mean_61_cast)[name = tensor("zero_mean_sq_61_cast")]; + tensor var_2357 = const()[name = tensor("op_2357"), val = tensor([1])]; + tensor var_2358_cast = reduce_mean(axes = var_2357, keep_dims = var_1181, x = zero_mean_sq_61_cast)[name = tensor("op_2358_cast")]; + tensor var_2359_to_fp16 = const()[name = tensor("op_2359_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2360_cast = add(x = var_2358_cast, y = var_2359_to_fp16)[name = tensor("op_2360_cast")]; + tensor denom_61_epsilon_0_to_fp16 = const()[name = tensor("denom_61_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_61_cast = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_2360_cast)[name = tensor("denom_61_cast")]; + tensor out_61_cast = mul(x = zero_mean_61_cast, y = denom_61_cast)[name = tensor("out_61_cast")]; + tensor var_2364_to_fp16 = const()[name = tensor("op_2364_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(615243648)))]; + tensor var_2365_cast = add(x = out_61_cast, y = var_2364_to_fp16)[name = tensor("op_2365_cast")]; + tensor var_2367_to_fp16 = const()[name = tensor("op_2367_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(615246272)))]; + tensor hidden_states_101_cast = mul(x = var_2365_cast, y = var_2367_to_fp16)[name = tensor("hidden_states_101_cast")]; + tensor var_2374 = const()[name = tensor("op_2374"), val = tensor([1, 1])]; + tensor var_2376 = const()[name = tensor("op_2376"), val = tensor([1, 1])]; + tensor q_41_pad_type_0 = const()[name = tensor("q_41_pad_type_0"), val = tensor("custom")]; + tensor q_41_pad_0 = const()[name = tensor("q_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(615248896)))]; + tensor q_41_cast = conv(dilations = var_2376, groups = var_1186, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_2374, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_101_cast)[name = tensor("q_41_cast")]; + tensor var_2380 = const()[name = tensor("op_2380"), val = tensor([1, 1])]; + tensor var_2382 = const()[name = tensor("op_2382"), val = tensor([1, 1])]; + tensor k_41_pad_type_0 = const()[name = tensor("k_41_pad_type_0"), val = tensor("custom")]; + tensor k_41_pad_0 = const()[name = tensor("k_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618525760)))]; + tensor k_41_cast = conv(dilations = var_2382, groups = var_1186, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_2380, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_101_cast)[name = tensor("k_41_cast")]; + tensor var_2386 = const()[name = tensor("op_2386"), val = tensor([1, 1])]; + tensor var_2388 = const()[name = tensor("op_2388"), val = tensor([1, 1])]; + tensor v_41_pad_type_0 = const()[name = tensor("v_41_pad_type_0"), val = tensor("custom")]; + tensor v_41_pad_0 = const()[name = tensor("v_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621802624)))]; + tensor v_41_cast = conv(dilations = var_2388, groups = var_1186, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_2386, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_101_cast)[name = tensor("v_41_cast")]; + tensor var_2392 = const()[name = tensor("op_2392"), val = tensor([2, 20, 64, -1])]; + tensor var_2393_cast = reshape(shape = var_2392, x = q_41_cast)[name = tensor("op_2393_cast")]; + tensor var_2394 = const()[name = tensor("op_2394"), val = tensor([2, 20, 64, -1])]; + tensor var_2395_cast = reshape(shape = var_2394, x = k_41_cast)[name = tensor("op_2395_cast")]; + tensor var_2396 = const()[name = tensor("op_2396"), val = tensor([2, 20, 64, -1])]; + tensor var_2397_cast = reshape(shape = var_2396, x = v_41_cast)[name = tensor("op_2397_cast")]; + tensor attn_weights_81_transpose_x_0 = const()[name = tensor("attn_weights_81_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_81_transpose_y_0 = const()[name = tensor("attn_weights_81_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_81_cast = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2393_cast, y = var_2395_cast)[name = tensor("attn_weights_81_cast")]; + tensor attn_weights_83_cast = mul(x = attn_weights_81_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_83_cast")]; + tensor var_2401_cast = softmax(axis = var_1170, x = attn_weights_83_cast)[name = tensor("op_2401_cast")]; + tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; + tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; + tensor attn_41_cast = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_2397_cast, y = var_2401_cast)[name = tensor("attn_41_cast")]; + tensor var_2405 = const()[name = tensor("op_2405"), val = tensor([2, 1280, 1, -1])]; + tensor input_179_cast = reshape(shape = var_2405, x = attn_41_cast)[name = tensor("input_179_cast")]; + tensor var_2410 = const()[name = tensor("op_2410"), val = tensor([1, 1])]; + tensor var_2412 = const()[name = tensor("op_2412"), val = tensor([1, 1])]; + tensor var_2414_pad_type_0 = const()[name = tensor("op_2414_pad_type_0"), val = tensor("custom")]; + tensor var_2414_pad_0 = const()[name = tensor("op_2414_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(625079488)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628356352)))]; + tensor var_2414_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_2412, groups = var_1186, pad = var_2414_pad_0, pad_type = var_2414_pad_type_0, strides = var_2410, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_179_cast)[name = tensor("op_2414_cast")]; + tensor inputs_63_cast = add(x = var_2414_cast, y = inputs_61_cast)[name = tensor("inputs_63_cast")]; + tensor var_2418 = const()[name = tensor("op_2418"), val = tensor([1])]; + tensor channels_mean_63_cast = reduce_mean(axes = var_2418, keep_dims = var_1181, x = inputs_63_cast)[name = tensor("channels_mean_63_cast")]; + tensor zero_mean_63_cast = sub(x = inputs_63_cast, y = channels_mean_63_cast)[name = tensor("zero_mean_63_cast")]; + tensor zero_mean_sq_63_cast = mul(x = zero_mean_63_cast, y = zero_mean_63_cast)[name = tensor("zero_mean_sq_63_cast")]; + tensor var_2422 = const()[name = tensor("op_2422"), val = tensor([1])]; + tensor var_2423_cast = reduce_mean(axes = var_2422, keep_dims = var_1181, x = zero_mean_sq_63_cast)[name = tensor("op_2423_cast")]; + tensor var_2424_to_fp16 = const()[name = tensor("op_2424_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2425_cast = add(x = var_2423_cast, y = var_2424_to_fp16)[name = tensor("op_2425_cast")]; + tensor denom_63_epsilon_0_to_fp16 = const()[name = tensor("denom_63_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_63_cast = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_2425_cast)[name = tensor("denom_63_cast")]; + tensor out_63_cast = mul(x = zero_mean_63_cast, y = denom_63_cast)[name = tensor("out_63_cast")]; + tensor var_2429_to_fp16 = const()[name = tensor("op_2429_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628358976)))]; + tensor var_2430_cast = add(x = out_63_cast, y = var_2429_to_fp16)[name = tensor("op_2430_cast")]; + tensor var_2432_to_fp16 = const()[name = tensor("op_2432_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628361600)))]; + tensor hidden_states_103_cast = mul(x = var_2430_cast, y = var_2432_to_fp16)[name = tensor("hidden_states_103_cast")]; + tensor var_2439 = const()[name = tensor("op_2439"), val = tensor([1, 1])]; + tensor var_2441 = const()[name = tensor("op_2441"), val = tensor([1, 1])]; + tensor q_43_pad_type_0 = const()[name = tensor("q_43_pad_type_0"), val = tensor("custom")]; + tensor q_43_pad_0 = const()[name = tensor("q_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628364224)))]; + tensor q_43_cast = conv(dilations = var_2441, groups = var_1186, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_2439, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_103_cast)[name = tensor("q_43_cast")]; + tensor var_2445 = const()[name = tensor("op_2445"), val = tensor([1, 1])]; + tensor var_2447 = const()[name = tensor("op_2447"), val = tensor([1, 1])]; + tensor k_43_pad_type_0 = const()[name = tensor("k_43_pad_type_0"), val = tensor("custom")]; + tensor k_43_pad_0 = const()[name = tensor("k_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631641088)))]; + tensor k_43_cast = conv(dilations = var_2447, groups = var_1186, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_2445, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_43_cast")]; + tensor var_2451 = const()[name = tensor("op_2451"), val = tensor([1, 1])]; + tensor var_2453 = const()[name = tensor("op_2453"), val = tensor([1, 1])]; + tensor v_43_pad_type_0 = const()[name = tensor("v_43_pad_type_0"), val = tensor("custom")]; + tensor v_43_pad_0 = const()[name = tensor("v_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636884032)))]; + tensor v_43_cast = conv(dilations = var_2453, groups = var_1186, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_2451, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_43_cast")]; + tensor var_2457 = const()[name = tensor("op_2457"), val = tensor([2, 20, 64, -1])]; + tensor var_2458_cast = reshape(shape = var_2457, x = q_43_cast)[name = tensor("op_2458_cast")]; + tensor var_2459 = const()[name = tensor("op_2459"), val = tensor([2, 20, 64, -1])]; + tensor var_2460_cast = reshape(shape = var_2459, x = k_43_cast)[name = tensor("op_2460_cast")]; + tensor var_2461 = const()[name = tensor("op_2461"), val = tensor([2, 20, 64, -1])]; + tensor var_2462_cast = reshape(shape = var_2461, x = v_43_cast)[name = tensor("op_2462_cast")]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_85_cast = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_2458_cast, y = var_2460_cast)[name = tensor("attn_weights_85_cast")]; + tensor attn_weights_87_cast = mul(x = attn_weights_85_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_87_cast")]; + tensor var_2466_cast = softmax(axis = var_1170, x = attn_weights_87_cast)[name = tensor("op_2466_cast")]; + tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; + tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; + tensor attn_43_cast = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_2462_cast, y = var_2466_cast)[name = tensor("attn_43_cast")]; + tensor var_2470 = const()[name = tensor("op_2470"), val = tensor([2, 1280, 1, -1])]; + tensor input_181_cast = reshape(shape = var_2470, x = attn_43_cast)[name = tensor("input_181_cast")]; + tensor var_2475 = const()[name = tensor("op_2475"), val = tensor([1, 1])]; + tensor var_2477 = const()[name = tensor("op_2477"), val = tensor([1, 1])]; + tensor var_2479_pad_type_0 = const()[name = tensor("op_2479_pad_type_0"), val = tensor("custom")]; + tensor var_2479_pad_0 = const()[name = tensor("op_2479_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642126976)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645403840)))]; + tensor var_2479_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_2477, groups = var_1186, pad = var_2479_pad_0, pad_type = var_2479_pad_type_0, strides = var_2475, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_181_cast)[name = tensor("op_2479_cast")]; + tensor inputs_65_cast = add(x = var_2479_cast, y = inputs_63_cast)[name = tensor("inputs_65_cast")]; + tensor var_2483 = const()[name = tensor("op_2483"), val = tensor([1])]; + tensor channels_mean_65_cast = reduce_mean(axes = var_2483, keep_dims = var_1181, x = inputs_65_cast)[name = tensor("channels_mean_65_cast")]; + tensor zero_mean_65_cast = sub(x = inputs_65_cast, y = channels_mean_65_cast)[name = tensor("zero_mean_65_cast")]; + tensor zero_mean_sq_65_cast = mul(x = zero_mean_65_cast, y = zero_mean_65_cast)[name = tensor("zero_mean_sq_65_cast")]; + tensor var_2487 = const()[name = tensor("op_2487"), val = tensor([1])]; + tensor var_2488_cast = reduce_mean(axes = var_2487, keep_dims = var_1181, x = zero_mean_sq_65_cast)[name = tensor("op_2488_cast")]; + tensor var_2489_to_fp16 = const()[name = tensor("op_2489_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2490_cast = add(x = var_2488_cast, y = var_2489_to_fp16)[name = tensor("op_2490_cast")]; + tensor denom_65_epsilon_0_to_fp16 = const()[name = tensor("denom_65_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_65_cast = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_2490_cast)[name = tensor("denom_65_cast")]; + tensor out_65_cast = mul(x = zero_mean_65_cast, y = denom_65_cast)[name = tensor("out_65_cast")]; + tensor var_2494_to_fp16 = const()[name = tensor("op_2494_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645406464)))]; + tensor var_2495_cast = add(x = out_65_cast, y = var_2494_to_fp16)[name = tensor("op_2495_cast")]; + tensor var_2497_to_fp16 = const()[name = tensor("op_2497_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645409088)))]; + tensor input_183_cast = mul(x = var_2495_cast, y = var_2497_to_fp16)[name = tensor("input_183_cast")]; + tensor var_2505 = const()[name = tensor("op_2505"), val = tensor([1, 1])]; + tensor var_2507 = const()[name = tensor("op_2507"), val = tensor([1, 1])]; + tensor var_2509_pad_type_0 = const()[name = tensor("op_2509_pad_type_0"), val = tensor("custom")]; + tensor var_2509_pad_0 = const()[name = tensor("op_2509_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645411712)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671626176)))]; + tensor var_2509_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_2507, groups = var_1186, pad = var_2509_pad_0, pad_type = var_2509_pad_type_0, strides = var_2505, weight = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_183_cast)[name = tensor("op_2509_cast")]; + tensor var_2510_split_sizes_0 = const()[name = tensor("op_2510_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2510_axis_0 = const()[name = tensor("op_2510_axis_0"), val = tensor(1)]; + tensor var_2510_cast_0, tensor var_2510_cast_1 = split(axis = var_2510_axis_0, split_sizes = var_2510_split_sizes_0, x = var_2509_cast)[name = tensor("op_2510_cast")]; + tensor var_2512_mode_0 = const()[name = tensor("op_2512_mode_0"), val = tensor("EXACT")]; + tensor var_2512_cast = gelu(mode = var_2512_mode_0, x = var_2510_cast_1)[name = tensor("op_2512_cast")]; + tensor input_185_cast = mul(x = var_2510_cast_0, y = var_2512_cast)[name = tensor("input_185_cast")]; + tensor var_2516 = const()[name = tensor("op_2516"), val = tensor([1, 1])]; + tensor var_2518 = const()[name = tensor("op_2518"), val = tensor([1, 1])]; + tensor var_2520_pad_type_0 = const()[name = tensor("op_2520_pad_type_0"), val = tensor("custom")]; + tensor var_2520_pad_0 = const()[name = tensor("op_2520_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671646720)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(684753984)))]; + tensor var_2520_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_2518, groups = var_1186, pad = var_2520_pad_0, pad_type = var_2520_pad_type_0, strides = var_2516, weight = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_185_cast)[name = tensor("op_2520_cast")]; + tensor inputs_67_cast = add(x = var_2520_cast, y = inputs_65_cast)[name = tensor("inputs_67_cast")]; + tensor var_2530 = const()[name = tensor("op_2530"), val = tensor([1])]; + tensor channels_mean_67_cast = reduce_mean(axes = var_2530, keep_dims = var_1181, x = inputs_67_cast)[name = tensor("channels_mean_67_cast")]; + tensor zero_mean_67_cast = sub(x = inputs_67_cast, y = channels_mean_67_cast)[name = tensor("zero_mean_67_cast")]; + tensor zero_mean_sq_67_cast = mul(x = zero_mean_67_cast, y = zero_mean_67_cast)[name = tensor("zero_mean_sq_67_cast")]; + tensor var_2534 = const()[name = tensor("op_2534"), val = tensor([1])]; + tensor var_2535_cast = reduce_mean(axes = var_2534, keep_dims = var_1181, x = zero_mean_sq_67_cast)[name = tensor("op_2535_cast")]; + tensor var_2536_to_fp16 = const()[name = tensor("op_2536_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2537_cast = add(x = var_2535_cast, y = var_2536_to_fp16)[name = tensor("op_2537_cast")]; + tensor denom_67_epsilon_0_to_fp16 = const()[name = tensor("denom_67_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_67_cast = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_2537_cast)[name = tensor("denom_67_cast")]; + tensor out_67_cast = mul(x = zero_mean_67_cast, y = denom_67_cast)[name = tensor("out_67_cast")]; + tensor var_2541_to_fp16 = const()[name = tensor("op_2541_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(684756608)))]; + tensor var_2542_cast = add(x = out_67_cast, y = var_2541_to_fp16)[name = tensor("op_2542_cast")]; + tensor var_2544_to_fp16 = const()[name = tensor("op_2544_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(684759232)))]; + tensor hidden_states_107_cast = mul(x = var_2542_cast, y = var_2544_to_fp16)[name = tensor("hidden_states_107_cast")]; + tensor var_2551 = const()[name = tensor("op_2551"), val = tensor([1, 1])]; + tensor var_2553 = const()[name = tensor("op_2553"), val = tensor([1, 1])]; + tensor q_45_pad_type_0 = const()[name = tensor("q_45_pad_type_0"), val = tensor("custom")]; + tensor q_45_pad_0 = const()[name = tensor("q_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(684761856)))]; + tensor q_45_cast = conv(dilations = var_2553, groups = var_1186, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_2551, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_107_cast)[name = tensor("q_45_cast")]; + tensor var_2557 = const()[name = tensor("op_2557"), val = tensor([1, 1])]; + tensor var_2559 = const()[name = tensor("op_2559"), val = tensor([1, 1])]; + tensor k_45_pad_type_0 = const()[name = tensor("k_45_pad_type_0"), val = tensor("custom")]; + tensor k_45_pad_0 = const()[name = tensor("k_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688038720)))]; + tensor k_45_cast = conv(dilations = var_2559, groups = var_1186, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_2557, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_107_cast)[name = tensor("k_45_cast")]; + tensor var_2563 = const()[name = tensor("op_2563"), val = tensor([1, 1])]; + tensor var_2565 = const()[name = tensor("op_2565"), val = tensor([1, 1])]; + tensor v_45_pad_type_0 = const()[name = tensor("v_45_pad_type_0"), val = tensor("custom")]; + tensor v_45_pad_0 = const()[name = tensor("v_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(691315584)))]; + tensor v_45_cast = conv(dilations = var_2565, groups = var_1186, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_2563, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_107_cast)[name = tensor("v_45_cast")]; + tensor var_2569 = const()[name = tensor("op_2569"), val = tensor([2, 20, 64, -1])]; + tensor var_2570_cast = reshape(shape = var_2569, x = q_45_cast)[name = tensor("op_2570_cast")]; + tensor var_2571 = const()[name = tensor("op_2571"), val = tensor([2, 20, 64, -1])]; + tensor var_2572_cast = reshape(shape = var_2571, x = k_45_cast)[name = tensor("op_2572_cast")]; + tensor var_2573 = const()[name = tensor("op_2573"), val = tensor([2, 20, 64, -1])]; + tensor var_2574_cast = reshape(shape = var_2573, x = v_45_cast)[name = tensor("op_2574_cast")]; + tensor attn_weights_89_transpose_x_0 = const()[name = tensor("attn_weights_89_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_89_transpose_y_0 = const()[name = tensor("attn_weights_89_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_89_cast = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2570_cast, y = var_2572_cast)[name = tensor("attn_weights_89_cast")]; + tensor attn_weights_91_cast = mul(x = attn_weights_89_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_91_cast")]; + tensor var_2578_cast = softmax(axis = var_1170, x = attn_weights_91_cast)[name = tensor("op_2578_cast")]; + tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; + tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; + tensor attn_45_cast = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_2574_cast, y = var_2578_cast)[name = tensor("attn_45_cast")]; + tensor var_2582 = const()[name = tensor("op_2582"), val = tensor([2, 1280, 1, -1])]; + tensor input_187_cast = reshape(shape = var_2582, x = attn_45_cast)[name = tensor("input_187_cast")]; + tensor var_2587 = const()[name = tensor("op_2587"), val = tensor([1, 1])]; + tensor var_2589 = const()[name = tensor("op_2589"), val = tensor([1, 1])]; + tensor var_2591_pad_type_0 = const()[name = tensor("op_2591_pad_type_0"), val = tensor("custom")]; + tensor var_2591_pad_0 = const()[name = tensor("op_2591_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(694592448)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697869312)))]; + tensor var_2591_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_2589, groups = var_1186, pad = var_2591_pad_0, pad_type = var_2591_pad_type_0, strides = var_2587, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_187_cast)[name = tensor("op_2591_cast")]; + tensor inputs_69_cast = add(x = var_2591_cast, y = inputs_67_cast)[name = tensor("inputs_69_cast")]; + tensor var_2595 = const()[name = tensor("op_2595"), val = tensor([1])]; + tensor channels_mean_69_cast = reduce_mean(axes = var_2595, keep_dims = var_1181, x = inputs_69_cast)[name = tensor("channels_mean_69_cast")]; + tensor zero_mean_69_cast = sub(x = inputs_69_cast, y = channels_mean_69_cast)[name = tensor("zero_mean_69_cast")]; + tensor zero_mean_sq_69_cast = mul(x = zero_mean_69_cast, y = zero_mean_69_cast)[name = tensor("zero_mean_sq_69_cast")]; + tensor var_2599 = const()[name = tensor("op_2599"), val = tensor([1])]; + tensor var_2600_cast = reduce_mean(axes = var_2599, keep_dims = var_1181, x = zero_mean_sq_69_cast)[name = tensor("op_2600_cast")]; + tensor var_2601_to_fp16 = const()[name = tensor("op_2601_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2602_cast = add(x = var_2600_cast, y = var_2601_to_fp16)[name = tensor("op_2602_cast")]; + tensor denom_69_epsilon_0_to_fp16 = const()[name = tensor("denom_69_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_69_cast = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_2602_cast)[name = tensor("denom_69_cast")]; + tensor out_69_cast = mul(x = zero_mean_69_cast, y = denom_69_cast)[name = tensor("out_69_cast")]; + tensor var_2606_to_fp16 = const()[name = tensor("op_2606_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697871936)))]; + tensor var_2607_cast = add(x = out_69_cast, y = var_2606_to_fp16)[name = tensor("op_2607_cast")]; + tensor var_2609_to_fp16 = const()[name = tensor("op_2609_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697874560)))]; + tensor hidden_states_109_cast = mul(x = var_2607_cast, y = var_2609_to_fp16)[name = tensor("hidden_states_109_cast")]; + tensor var_2616 = const()[name = tensor("op_2616"), val = tensor([1, 1])]; + tensor var_2618 = const()[name = tensor("op_2618"), val = tensor([1, 1])]; + tensor q_47_pad_type_0 = const()[name = tensor("q_47_pad_type_0"), val = tensor("custom")]; + tensor q_47_pad_0 = const()[name = tensor("q_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697877184)))]; + tensor q_47_cast = conv(dilations = var_2618, groups = var_1186, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_2616, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_109_cast)[name = tensor("q_47_cast")]; + tensor var_2622 = const()[name = tensor("op_2622"), val = tensor([1, 1])]; + tensor var_2624 = const()[name = tensor("op_2624"), val = tensor([1, 1])]; + tensor k_47_pad_type_0 = const()[name = tensor("k_47_pad_type_0"), val = tensor("custom")]; + tensor k_47_pad_0 = const()[name = tensor("k_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(701154048)))]; + tensor k_47_cast = conv(dilations = var_2624, groups = var_1186, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_2622, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_47_cast")]; + tensor var_2628 = const()[name = tensor("op_2628"), val = tensor([1, 1])]; + tensor var_2630 = const()[name = tensor("op_2630"), val = tensor([1, 1])]; + tensor v_47_pad_type_0 = const()[name = tensor("v_47_pad_type_0"), val = tensor("custom")]; + tensor v_47_pad_0 = const()[name = tensor("v_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(706396992)))]; + tensor v_47_cast = conv(dilations = var_2630, groups = var_1186, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_2628, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_47_cast")]; + tensor var_2634 = const()[name = tensor("op_2634"), val = tensor([2, 20, 64, -1])]; + tensor var_2635_cast = reshape(shape = var_2634, x = q_47_cast)[name = tensor("op_2635_cast")]; + tensor var_2636 = const()[name = tensor("op_2636"), val = tensor([2, 20, 64, -1])]; + tensor var_2637_cast = reshape(shape = var_2636, x = k_47_cast)[name = tensor("op_2637_cast")]; + tensor var_2638 = const()[name = tensor("op_2638"), val = tensor([2, 20, 64, -1])]; + tensor var_2639_cast = reshape(shape = var_2638, x = v_47_cast)[name = tensor("op_2639_cast")]; + tensor attn_weights_93_transpose_x_0 = const()[name = tensor("attn_weights_93_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_93_transpose_y_0 = const()[name = tensor("attn_weights_93_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_93_cast = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_2635_cast, y = var_2637_cast)[name = tensor("attn_weights_93_cast")]; + tensor attn_weights_95_cast = mul(x = attn_weights_93_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_95_cast")]; + tensor var_2643_cast = softmax(axis = var_1170, x = attn_weights_95_cast)[name = tensor("op_2643_cast")]; + tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; + tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; + tensor attn_47_cast = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_2639_cast, y = var_2643_cast)[name = tensor("attn_47_cast")]; + tensor var_2647 = const()[name = tensor("op_2647"), val = tensor([2, 1280, 1, -1])]; + tensor input_189_cast = reshape(shape = var_2647, x = attn_47_cast)[name = tensor("input_189_cast")]; + tensor var_2652 = const()[name = tensor("op_2652"), val = tensor([1, 1])]; + tensor var_2654 = const()[name = tensor("op_2654"), val = tensor([1, 1])]; + tensor var_2656_pad_type_0 = const()[name = tensor("op_2656_pad_type_0"), val = tensor("custom")]; + tensor var_2656_pad_0 = const()[name = tensor("op_2656_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711639936)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(714916800)))]; + tensor var_2656_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_2654, groups = var_1186, pad = var_2656_pad_0, pad_type = var_2656_pad_type_0, strides = var_2652, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_189_cast)[name = tensor("op_2656_cast")]; + tensor inputs_71_cast = add(x = var_2656_cast, y = inputs_69_cast)[name = tensor("inputs_71_cast")]; + tensor var_2660 = const()[name = tensor("op_2660"), val = tensor([1])]; + tensor channels_mean_71_cast = reduce_mean(axes = var_2660, keep_dims = var_1181, x = inputs_71_cast)[name = tensor("channels_mean_71_cast")]; + tensor zero_mean_71_cast = sub(x = inputs_71_cast, y = channels_mean_71_cast)[name = tensor("zero_mean_71_cast")]; + tensor zero_mean_sq_71_cast = mul(x = zero_mean_71_cast, y = zero_mean_71_cast)[name = tensor("zero_mean_sq_71_cast")]; + tensor var_2664 = const()[name = tensor("op_2664"), val = tensor([1])]; + tensor var_2665_cast = reduce_mean(axes = var_2664, keep_dims = var_1181, x = zero_mean_sq_71_cast)[name = tensor("op_2665_cast")]; + tensor var_2666_to_fp16 = const()[name = tensor("op_2666_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2667_cast = add(x = var_2665_cast, y = var_2666_to_fp16)[name = tensor("op_2667_cast")]; + tensor denom_71_epsilon_0_to_fp16 = const()[name = tensor("denom_71_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_71_cast = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_2667_cast)[name = tensor("denom_71_cast")]; + tensor out_71_cast = mul(x = zero_mean_71_cast, y = denom_71_cast)[name = tensor("out_71_cast")]; + tensor var_2671_to_fp16 = const()[name = tensor("op_2671_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(714919424)))]; + tensor var_2672_cast = add(x = out_71_cast, y = var_2671_to_fp16)[name = tensor("op_2672_cast")]; + tensor var_2674_to_fp16 = const()[name = tensor("op_2674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(714922048)))]; + tensor input_191_cast = mul(x = var_2672_cast, y = var_2674_to_fp16)[name = tensor("input_191_cast")]; + tensor var_2682 = const()[name = tensor("op_2682"), val = tensor([1, 1])]; + tensor var_2684 = const()[name = tensor("op_2684"), val = tensor([1, 1])]; + tensor var_2686_pad_type_0 = const()[name = tensor("op_2686_pad_type_0"), val = tensor("custom")]; + tensor var_2686_pad_0 = const()[name = tensor("op_2686_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(714924672)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741139136)))]; + tensor var_2686_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_2684, groups = var_1186, pad = var_2686_pad_0, pad_type = var_2686_pad_type_0, strides = var_2682, weight = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_191_cast)[name = tensor("op_2686_cast")]; + tensor var_2687_split_sizes_0 = const()[name = tensor("op_2687_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2687_axis_0 = const()[name = tensor("op_2687_axis_0"), val = tensor(1)]; + tensor var_2687_cast_0, tensor var_2687_cast_1 = split(axis = var_2687_axis_0, split_sizes = var_2687_split_sizes_0, x = var_2686_cast)[name = tensor("op_2687_cast")]; + tensor var_2689_mode_0 = const()[name = tensor("op_2689_mode_0"), val = tensor("EXACT")]; + tensor var_2689_cast = gelu(mode = var_2689_mode_0, x = var_2687_cast_1)[name = tensor("op_2689_cast")]; + tensor input_193_cast = mul(x = var_2687_cast_0, y = var_2689_cast)[name = tensor("input_193_cast")]; + tensor var_2693 = const()[name = tensor("op_2693"), val = tensor([1, 1])]; + tensor var_2695 = const()[name = tensor("op_2695"), val = tensor([1, 1])]; + tensor var_2697_pad_type_0 = const()[name = tensor("op_2697_pad_type_0"), val = tensor("custom")]; + tensor var_2697_pad_0 = const()[name = tensor("op_2697_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741159680)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754266944)))]; + tensor var_2697_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_2695, groups = var_1186, pad = var_2697_pad_0, pad_type = var_2697_pad_type_0, strides = var_2693, weight = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_193_cast)[name = tensor("op_2697_cast")]; + tensor inputs_73_cast = add(x = var_2697_cast, y = inputs_71_cast)[name = tensor("inputs_73_cast")]; + tensor var_2707 = const()[name = tensor("op_2707"), val = tensor([1])]; + tensor channels_mean_73_cast = reduce_mean(axes = var_2707, keep_dims = var_1181, x = inputs_73_cast)[name = tensor("channels_mean_73_cast")]; + tensor zero_mean_73_cast = sub(x = inputs_73_cast, y = channels_mean_73_cast)[name = tensor("zero_mean_73_cast")]; + tensor zero_mean_sq_73_cast = mul(x = zero_mean_73_cast, y = zero_mean_73_cast)[name = tensor("zero_mean_sq_73_cast")]; + tensor var_2711 = const()[name = tensor("op_2711"), val = tensor([1])]; + tensor var_2712_cast = reduce_mean(axes = var_2711, keep_dims = var_1181, x = zero_mean_sq_73_cast)[name = tensor("op_2712_cast")]; + tensor var_2713_to_fp16 = const()[name = tensor("op_2713_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2714_cast = add(x = var_2712_cast, y = var_2713_to_fp16)[name = tensor("op_2714_cast")]; + tensor denom_73_epsilon_0_to_fp16 = const()[name = tensor("denom_73_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_73_cast = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_2714_cast)[name = tensor("denom_73_cast")]; + tensor out_73_cast = mul(x = zero_mean_73_cast, y = denom_73_cast)[name = tensor("out_73_cast")]; + tensor var_2718_to_fp16 = const()[name = tensor("op_2718_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754269568)))]; + tensor var_2719_cast = add(x = out_73_cast, y = var_2718_to_fp16)[name = tensor("op_2719_cast")]; + tensor var_2721_to_fp16 = const()[name = tensor("op_2721_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754272192)))]; + tensor hidden_states_113_cast = mul(x = var_2719_cast, y = var_2721_to_fp16)[name = tensor("hidden_states_113_cast")]; + tensor var_2728 = const()[name = tensor("op_2728"), val = tensor([1, 1])]; + tensor var_2730 = const()[name = tensor("op_2730"), val = tensor([1, 1])]; + tensor q_49_pad_type_0 = const()[name = tensor("q_49_pad_type_0"), val = tensor("custom")]; + tensor q_49_pad_0 = const()[name = tensor("q_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754274816)))]; + tensor q_49_cast = conv(dilations = var_2730, groups = var_1186, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_2728, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_113_cast)[name = tensor("q_49_cast")]; + tensor var_2734 = const()[name = tensor("op_2734"), val = tensor([1, 1])]; + tensor var_2736 = const()[name = tensor("op_2736"), val = tensor([1, 1])]; + tensor k_49_pad_type_0 = const()[name = tensor("k_49_pad_type_0"), val = tensor("custom")]; + tensor k_49_pad_0 = const()[name = tensor("k_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757551680)))]; + tensor k_49_cast = conv(dilations = var_2736, groups = var_1186, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_2734, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_113_cast)[name = tensor("k_49_cast")]; + tensor var_2740 = const()[name = tensor("op_2740"), val = tensor([1, 1])]; + tensor var_2742 = const()[name = tensor("op_2742"), val = tensor([1, 1])]; + tensor v_49_pad_type_0 = const()[name = tensor("v_49_pad_type_0"), val = tensor("custom")]; + tensor v_49_pad_0 = const()[name = tensor("v_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(760828544)))]; + tensor v_49_cast = conv(dilations = var_2742, groups = var_1186, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_2740, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_113_cast)[name = tensor("v_49_cast")]; + tensor var_2746 = const()[name = tensor("op_2746"), val = tensor([2, 20, 64, -1])]; + tensor var_2747_cast = reshape(shape = var_2746, x = q_49_cast)[name = tensor("op_2747_cast")]; + tensor var_2748 = const()[name = tensor("op_2748"), val = tensor([2, 20, 64, -1])]; + tensor var_2749_cast = reshape(shape = var_2748, x = k_49_cast)[name = tensor("op_2749_cast")]; + tensor var_2750 = const()[name = tensor("op_2750"), val = tensor([2, 20, 64, -1])]; + tensor var_2751_cast = reshape(shape = var_2750, x = v_49_cast)[name = tensor("op_2751_cast")]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_97_cast = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_2747_cast, y = var_2749_cast)[name = tensor("attn_weights_97_cast")]; + tensor attn_weights_99_cast = mul(x = attn_weights_97_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_99_cast")]; + tensor var_2755_cast = softmax(axis = var_1170, x = attn_weights_99_cast)[name = tensor("op_2755_cast")]; + tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; + tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; + tensor attn_49_cast = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_2751_cast, y = var_2755_cast)[name = tensor("attn_49_cast")]; + tensor var_2759 = const()[name = tensor("op_2759"), val = tensor([2, 1280, 1, -1])]; + tensor input_195_cast = reshape(shape = var_2759, x = attn_49_cast)[name = tensor("input_195_cast")]; + tensor var_2764 = const()[name = tensor("op_2764"), val = tensor([1, 1])]; + tensor var_2766 = const()[name = tensor("op_2766"), val = tensor([1, 1])]; + tensor var_2768_pad_type_0 = const()[name = tensor("op_2768_pad_type_0"), val = tensor("custom")]; + tensor var_2768_pad_0 = const()[name = tensor("op_2768_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764105408)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767382272)))]; + tensor var_2768_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_2766, groups = var_1186, pad = var_2768_pad_0, pad_type = var_2768_pad_type_0, strides = var_2764, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_195_cast)[name = tensor("op_2768_cast")]; + tensor inputs_75_cast = add(x = var_2768_cast, y = inputs_73_cast)[name = tensor("inputs_75_cast")]; + tensor var_2772 = const()[name = tensor("op_2772"), val = tensor([1])]; + tensor channels_mean_75_cast = reduce_mean(axes = var_2772, keep_dims = var_1181, x = inputs_75_cast)[name = tensor("channels_mean_75_cast")]; + tensor zero_mean_75_cast = sub(x = inputs_75_cast, y = channels_mean_75_cast)[name = tensor("zero_mean_75_cast")]; + tensor zero_mean_sq_75_cast = mul(x = zero_mean_75_cast, y = zero_mean_75_cast)[name = tensor("zero_mean_sq_75_cast")]; + tensor var_2776 = const()[name = tensor("op_2776"), val = tensor([1])]; + tensor var_2777_cast = reduce_mean(axes = var_2776, keep_dims = var_1181, x = zero_mean_sq_75_cast)[name = tensor("op_2777_cast")]; + tensor var_2778_to_fp16 = const()[name = tensor("op_2778_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2779_cast = add(x = var_2777_cast, y = var_2778_to_fp16)[name = tensor("op_2779_cast")]; + tensor denom_75_epsilon_0_to_fp16 = const()[name = tensor("denom_75_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_75_cast = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_2779_cast)[name = tensor("denom_75_cast")]; + tensor out_75_cast = mul(x = zero_mean_75_cast, y = denom_75_cast)[name = tensor("out_75_cast")]; + tensor var_2783_to_fp16 = const()[name = tensor("op_2783_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767384896)))]; + tensor var_2784_cast = add(x = out_75_cast, y = var_2783_to_fp16)[name = tensor("op_2784_cast")]; + tensor var_2786_to_fp16 = const()[name = tensor("op_2786_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767387520)))]; + tensor hidden_states_115_cast = mul(x = var_2784_cast, y = var_2786_to_fp16)[name = tensor("hidden_states_115_cast")]; + tensor var_2793 = const()[name = tensor("op_2793"), val = tensor([1, 1])]; + tensor var_2795 = const()[name = tensor("op_2795"), val = tensor([1, 1])]; + tensor q_51_pad_type_0 = const()[name = tensor("q_51_pad_type_0"), val = tensor("custom")]; + tensor q_51_pad_0 = const()[name = tensor("q_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767390144)))]; + tensor q_51_cast = conv(dilations = var_2795, groups = var_1186, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_2793, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_115_cast)[name = tensor("q_51_cast")]; + tensor var_2799 = const()[name = tensor("op_2799"), val = tensor([1, 1])]; + tensor var_2801 = const()[name = tensor("op_2801"), val = tensor([1, 1])]; + tensor k_51_pad_type_0 = const()[name = tensor("k_51_pad_type_0"), val = tensor("custom")]; + tensor k_51_pad_0 = const()[name = tensor("k_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(770667008)))]; + tensor k_51_cast = conv(dilations = var_2801, groups = var_1186, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_2799, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_51_cast")]; + tensor var_2805 = const()[name = tensor("op_2805"), val = tensor([1, 1])]; + tensor var_2807 = const()[name = tensor("op_2807"), val = tensor([1, 1])]; + tensor v_51_pad_type_0 = const()[name = tensor("v_51_pad_type_0"), val = tensor("custom")]; + tensor v_51_pad_0 = const()[name = tensor("v_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(775909952)))]; + tensor v_51_cast = conv(dilations = var_2807, groups = var_1186, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_2805, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_51_cast")]; + tensor var_2811 = const()[name = tensor("op_2811"), val = tensor([2, 20, 64, -1])]; + tensor var_2812_cast = reshape(shape = var_2811, x = q_51_cast)[name = tensor("op_2812_cast")]; + tensor var_2813 = const()[name = tensor("op_2813"), val = tensor([2, 20, 64, -1])]; + tensor var_2814_cast = reshape(shape = var_2813, x = k_51_cast)[name = tensor("op_2814_cast")]; + tensor var_2815 = const()[name = tensor("op_2815"), val = tensor([2, 20, 64, -1])]; + tensor var_2816_cast = reshape(shape = var_2815, x = v_51_cast)[name = tensor("op_2816_cast")]; + tensor attn_weights_101_transpose_x_0 = const()[name = tensor("attn_weights_101_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_101_transpose_y_0 = const()[name = tensor("attn_weights_101_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_101_cast = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_2812_cast, y = var_2814_cast)[name = tensor("attn_weights_101_cast")]; + tensor attn_weights_103_cast = mul(x = attn_weights_101_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_103_cast")]; + tensor var_2820_cast = softmax(axis = var_1170, x = attn_weights_103_cast)[name = tensor("op_2820_cast")]; + tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; + tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; + tensor attn_51_cast = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_2816_cast, y = var_2820_cast)[name = tensor("attn_51_cast")]; + tensor var_2824 = const()[name = tensor("op_2824"), val = tensor([2, 1280, 1, -1])]; + tensor input_197_cast = reshape(shape = var_2824, x = attn_51_cast)[name = tensor("input_197_cast")]; + tensor var_2829 = const()[name = tensor("op_2829"), val = tensor([1, 1])]; + tensor var_2831 = const()[name = tensor("op_2831"), val = tensor([1, 1])]; + tensor var_2833_pad_type_0 = const()[name = tensor("op_2833_pad_type_0"), val = tensor("custom")]; + tensor var_2833_pad_0 = const()[name = tensor("op_2833_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781152896)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784429760)))]; + tensor var_2833_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_2831, groups = var_1186, pad = var_2833_pad_0, pad_type = var_2833_pad_type_0, strides = var_2829, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_197_cast)[name = tensor("op_2833_cast")]; + tensor inputs_77_cast = add(x = var_2833_cast, y = inputs_75_cast)[name = tensor("inputs_77_cast")]; + tensor var_2837 = const()[name = tensor("op_2837"), val = tensor([1])]; + tensor channels_mean_77_cast = reduce_mean(axes = var_2837, keep_dims = var_1181, x = inputs_77_cast)[name = tensor("channels_mean_77_cast")]; + tensor zero_mean_77_cast = sub(x = inputs_77_cast, y = channels_mean_77_cast)[name = tensor("zero_mean_77_cast")]; + tensor zero_mean_sq_77_cast = mul(x = zero_mean_77_cast, y = zero_mean_77_cast)[name = tensor("zero_mean_sq_77_cast")]; + tensor var_2841 = const()[name = tensor("op_2841"), val = tensor([1])]; + tensor var_2842_cast = reduce_mean(axes = var_2841, keep_dims = var_1181, x = zero_mean_sq_77_cast)[name = tensor("op_2842_cast")]; + tensor var_2843_to_fp16 = const()[name = tensor("op_2843_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2844_cast = add(x = var_2842_cast, y = var_2843_to_fp16)[name = tensor("op_2844_cast")]; + tensor denom_77_epsilon_0_to_fp16 = const()[name = tensor("denom_77_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_77_cast = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_2844_cast)[name = tensor("denom_77_cast")]; + tensor out_77_cast = mul(x = zero_mean_77_cast, y = denom_77_cast)[name = tensor("out_77_cast")]; + tensor var_2848_to_fp16 = const()[name = tensor("op_2848_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784432384)))]; + tensor var_2849_cast = add(x = out_77_cast, y = var_2848_to_fp16)[name = tensor("op_2849_cast")]; + tensor var_2851_to_fp16 = const()[name = tensor("op_2851_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784435008)))]; + tensor input_199_cast = mul(x = var_2849_cast, y = var_2851_to_fp16)[name = tensor("input_199_cast")]; + tensor var_2859 = const()[name = tensor("op_2859"), val = tensor([1, 1])]; + tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, 1])]; + tensor var_2863_pad_type_0 = const()[name = tensor("op_2863_pad_type_0"), val = tensor("custom")]; + tensor var_2863_pad_0 = const()[name = tensor("op_2863_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784437632)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810652096)))]; + tensor var_2863_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_2861, groups = var_1186, pad = var_2863_pad_0, pad_type = var_2863_pad_type_0, strides = var_2859, weight = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_199_cast)[name = tensor("op_2863_cast")]; + tensor var_2864_split_sizes_0 = const()[name = tensor("op_2864_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2864_axis_0 = const()[name = tensor("op_2864_axis_0"), val = tensor(1)]; + tensor var_2864_cast_0, tensor var_2864_cast_1 = split(axis = var_2864_axis_0, split_sizes = var_2864_split_sizes_0, x = var_2863_cast)[name = tensor("op_2864_cast")]; + tensor var_2866_mode_0 = const()[name = tensor("op_2866_mode_0"), val = tensor("EXACT")]; + tensor var_2866_cast = gelu(mode = var_2866_mode_0, x = var_2864_cast_1)[name = tensor("op_2866_cast")]; + tensor input_201_cast = mul(x = var_2864_cast_0, y = var_2866_cast)[name = tensor("input_201_cast")]; + tensor var_2870 = const()[name = tensor("op_2870"), val = tensor([1, 1])]; + tensor var_2872 = const()[name = tensor("op_2872"), val = tensor([1, 1])]; + tensor var_2874_pad_type_0 = const()[name = tensor("op_2874_pad_type_0"), val = tensor("custom")]; + tensor var_2874_pad_0 = const()[name = tensor("op_2874_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810672640)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823779904)))]; + tensor var_2874_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_2872, groups = var_1186, pad = var_2874_pad_0, pad_type = var_2874_pad_type_0, strides = var_2870, weight = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_201_cast)[name = tensor("op_2874_cast")]; + tensor inputs_79_cast = add(x = var_2874_cast, y = inputs_77_cast)[name = tensor("inputs_79_cast")]; + tensor var_2884 = const()[name = tensor("op_2884"), val = tensor([1])]; + tensor channels_mean_79_cast = reduce_mean(axes = var_2884, keep_dims = var_1181, x = inputs_79_cast)[name = tensor("channels_mean_79_cast")]; + tensor zero_mean_79_cast = sub(x = inputs_79_cast, y = channels_mean_79_cast)[name = tensor("zero_mean_79_cast")]; + tensor zero_mean_sq_79_cast = mul(x = zero_mean_79_cast, y = zero_mean_79_cast)[name = tensor("zero_mean_sq_79_cast")]; + tensor var_2888 = const()[name = tensor("op_2888"), val = tensor([1])]; + tensor var_2889_cast = reduce_mean(axes = var_2888, keep_dims = var_1181, x = zero_mean_sq_79_cast)[name = tensor("op_2889_cast")]; + tensor var_2890_to_fp16 = const()[name = tensor("op_2890_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2891_cast = add(x = var_2889_cast, y = var_2890_to_fp16)[name = tensor("op_2891_cast")]; + tensor denom_79_epsilon_0_to_fp16 = const()[name = tensor("denom_79_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_79_cast = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_2891_cast)[name = tensor("denom_79_cast")]; + tensor out_79_cast = mul(x = zero_mean_79_cast, y = denom_79_cast)[name = tensor("out_79_cast")]; + tensor var_2895_to_fp16 = const()[name = tensor("op_2895_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823782528)))]; + tensor var_2896_cast = add(x = out_79_cast, y = var_2895_to_fp16)[name = tensor("op_2896_cast")]; + tensor var_2898_to_fp16 = const()[name = tensor("op_2898_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823785152)))]; + tensor hidden_states_119_cast = mul(x = var_2896_cast, y = var_2898_to_fp16)[name = tensor("hidden_states_119_cast")]; + tensor var_2905 = const()[name = tensor("op_2905"), val = tensor([1, 1])]; + tensor var_2907 = const()[name = tensor("op_2907"), val = tensor([1, 1])]; + tensor q_53_pad_type_0 = const()[name = tensor("q_53_pad_type_0"), val = tensor("custom")]; + tensor q_53_pad_0 = const()[name = tensor("q_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823787776)))]; + tensor q_53_cast = conv(dilations = var_2907, groups = var_1186, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_2905, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_119_cast)[name = tensor("q_53_cast")]; + tensor var_2911 = const()[name = tensor("op_2911"), val = tensor([1, 1])]; + tensor var_2913 = const()[name = tensor("op_2913"), val = tensor([1, 1])]; + tensor k_53_pad_type_0 = const()[name = tensor("k_53_pad_type_0"), val = tensor("custom")]; + tensor k_53_pad_0 = const()[name = tensor("k_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(827064640)))]; + tensor k_53_cast = conv(dilations = var_2913, groups = var_1186, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_2911, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_119_cast)[name = tensor("k_53_cast")]; + tensor var_2917 = const()[name = tensor("op_2917"), val = tensor([1, 1])]; + tensor var_2919 = const()[name = tensor("op_2919"), val = tensor([1, 1])]; + tensor v_53_pad_type_0 = const()[name = tensor("v_53_pad_type_0"), val = tensor("custom")]; + tensor v_53_pad_0 = const()[name = tensor("v_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830341504)))]; + tensor v_53_cast = conv(dilations = var_2919, groups = var_1186, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_2917, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_119_cast)[name = tensor("v_53_cast")]; + tensor var_2923 = const()[name = tensor("op_2923"), val = tensor([2, 20, 64, -1])]; + tensor var_2924_cast = reshape(shape = var_2923, x = q_53_cast)[name = tensor("op_2924_cast")]; + tensor var_2925 = const()[name = tensor("op_2925"), val = tensor([2, 20, 64, -1])]; + tensor var_2926_cast = reshape(shape = var_2925, x = k_53_cast)[name = tensor("op_2926_cast")]; + tensor var_2927 = const()[name = tensor("op_2927"), val = tensor([2, 20, 64, -1])]; + tensor var_2928_cast = reshape(shape = var_2927, x = v_53_cast)[name = tensor("op_2928_cast")]; + tensor attn_weights_105_transpose_x_0 = const()[name = tensor("attn_weights_105_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_105_transpose_y_0 = const()[name = tensor("attn_weights_105_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_105_cast = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_2924_cast, y = var_2926_cast)[name = tensor("attn_weights_105_cast")]; + tensor attn_weights_107_cast = mul(x = attn_weights_105_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_107_cast")]; + tensor var_2932_cast = softmax(axis = var_1170, x = attn_weights_107_cast)[name = tensor("op_2932_cast")]; + tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; + tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; + tensor attn_53_cast = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_2928_cast, y = var_2932_cast)[name = tensor("attn_53_cast")]; + tensor var_2936 = const()[name = tensor("op_2936"), val = tensor([2, 1280, 1, -1])]; + tensor input_203_cast = reshape(shape = var_2936, x = attn_53_cast)[name = tensor("input_203_cast")]; + tensor var_2941 = const()[name = tensor("op_2941"), val = tensor([1, 1])]; + tensor var_2943 = const()[name = tensor("op_2943"), val = tensor([1, 1])]; + tensor var_2945_pad_type_0 = const()[name = tensor("op_2945_pad_type_0"), val = tensor("custom")]; + tensor var_2945_pad_0 = const()[name = tensor("op_2945_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833618368)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836895232)))]; + tensor var_2945_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_2943, groups = var_1186, pad = var_2945_pad_0, pad_type = var_2945_pad_type_0, strides = var_2941, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_203_cast)[name = tensor("op_2945_cast")]; + tensor inputs_81_cast = add(x = var_2945_cast, y = inputs_79_cast)[name = tensor("inputs_81_cast")]; + tensor var_2949 = const()[name = tensor("op_2949"), val = tensor([1])]; + tensor channels_mean_81_cast = reduce_mean(axes = var_2949, keep_dims = var_1181, x = inputs_81_cast)[name = tensor("channels_mean_81_cast")]; + tensor zero_mean_81_cast = sub(x = inputs_81_cast, y = channels_mean_81_cast)[name = tensor("zero_mean_81_cast")]; + tensor zero_mean_sq_81_cast = mul(x = zero_mean_81_cast, y = zero_mean_81_cast)[name = tensor("zero_mean_sq_81_cast")]; + tensor var_2953 = const()[name = tensor("op_2953"), val = tensor([1])]; + tensor var_2954_cast = reduce_mean(axes = var_2953, keep_dims = var_1181, x = zero_mean_sq_81_cast)[name = tensor("op_2954_cast")]; + tensor var_2955_to_fp16 = const()[name = tensor("op_2955_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2956_cast = add(x = var_2954_cast, y = var_2955_to_fp16)[name = tensor("op_2956_cast")]; + tensor denom_81_epsilon_0_to_fp16 = const()[name = tensor("denom_81_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_81_cast = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_2956_cast)[name = tensor("denom_81_cast")]; + tensor out_81_cast = mul(x = zero_mean_81_cast, y = denom_81_cast)[name = tensor("out_81_cast")]; + tensor var_2960_to_fp16 = const()[name = tensor("op_2960_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836897856)))]; + tensor var_2961_cast = add(x = out_81_cast, y = var_2960_to_fp16)[name = tensor("op_2961_cast")]; + tensor var_2963_to_fp16 = const()[name = tensor("op_2963_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836900480)))]; + tensor hidden_states_121_cast = mul(x = var_2961_cast, y = var_2963_to_fp16)[name = tensor("hidden_states_121_cast")]; + tensor var_2970 = const()[name = tensor("op_2970"), val = tensor([1, 1])]; + tensor var_2972 = const()[name = tensor("op_2972"), val = tensor([1, 1])]; + tensor q_55_pad_type_0 = const()[name = tensor("q_55_pad_type_0"), val = tensor("custom")]; + tensor q_55_pad_0 = const()[name = tensor("q_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836903104)))]; + tensor q_55_cast = conv(dilations = var_2972, groups = var_1186, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_2970, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_121_cast)[name = tensor("q_55_cast")]; + tensor var_2976 = const()[name = tensor("op_2976"), val = tensor([1, 1])]; + tensor var_2978 = const()[name = tensor("op_2978"), val = tensor([1, 1])]; + tensor k_55_pad_type_0 = const()[name = tensor("k_55_pad_type_0"), val = tensor("custom")]; + tensor k_55_pad_0 = const()[name = tensor("k_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(840179968)))]; + tensor k_55_cast = conv(dilations = var_2978, groups = var_1186, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_2976, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_55_cast")]; + tensor var_2982 = const()[name = tensor("op_2982"), val = tensor([1, 1])]; + tensor var_2984 = const()[name = tensor("op_2984"), val = tensor([1, 1])]; + tensor v_55_pad_type_0 = const()[name = tensor("v_55_pad_type_0"), val = tensor("custom")]; + tensor v_55_pad_0 = const()[name = tensor("v_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(845422912)))]; + tensor v_55_cast = conv(dilations = var_2984, groups = var_1186, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_2982, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_55_cast")]; + tensor var_2988 = const()[name = tensor("op_2988"), val = tensor([2, 20, 64, -1])]; + tensor var_2989_cast = reshape(shape = var_2988, x = q_55_cast)[name = tensor("op_2989_cast")]; + tensor var_2990 = const()[name = tensor("op_2990"), val = tensor([2, 20, 64, -1])]; + tensor var_2991_cast = reshape(shape = var_2990, x = k_55_cast)[name = tensor("op_2991_cast")]; + tensor var_2992 = const()[name = tensor("op_2992"), val = tensor([2, 20, 64, -1])]; + tensor var_2993_cast = reshape(shape = var_2992, x = v_55_cast)[name = tensor("op_2993_cast")]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_109_cast = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_2989_cast, y = var_2991_cast)[name = tensor("attn_weights_109_cast")]; + tensor attn_weights_111_cast = mul(x = attn_weights_109_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_111_cast")]; + tensor var_2997_cast = softmax(axis = var_1170, x = attn_weights_111_cast)[name = tensor("op_2997_cast")]; + tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; + tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; + tensor attn_55_cast = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_2993_cast, y = var_2997_cast)[name = tensor("attn_55_cast")]; + tensor var_3001 = const()[name = tensor("op_3001"), val = tensor([2, 1280, 1, -1])]; + tensor input_205_cast = reshape(shape = var_3001, x = attn_55_cast)[name = tensor("input_205_cast")]; + tensor var_3006 = const()[name = tensor("op_3006"), val = tensor([1, 1])]; + tensor var_3008 = const()[name = tensor("op_3008"), val = tensor([1, 1])]; + tensor var_3010_pad_type_0 = const()[name = tensor("op_3010_pad_type_0"), val = tensor("custom")]; + tensor var_3010_pad_0 = const()[name = tensor("op_3010_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(850665856)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(853942720)))]; + tensor var_3010_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_3008, groups = var_1186, pad = var_3010_pad_0, pad_type = var_3010_pad_type_0, strides = var_3006, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_205_cast)[name = tensor("op_3010_cast")]; + tensor inputs_83_cast = add(x = var_3010_cast, y = inputs_81_cast)[name = tensor("inputs_83_cast")]; + tensor var_3014 = const()[name = tensor("op_3014"), val = tensor([1])]; + tensor channels_mean_83_cast = reduce_mean(axes = var_3014, keep_dims = var_1181, x = inputs_83_cast)[name = tensor("channels_mean_83_cast")]; + tensor zero_mean_83_cast = sub(x = inputs_83_cast, y = channels_mean_83_cast)[name = tensor("zero_mean_83_cast")]; + tensor zero_mean_sq_83_cast = mul(x = zero_mean_83_cast, y = zero_mean_83_cast)[name = tensor("zero_mean_sq_83_cast")]; + tensor var_3018 = const()[name = tensor("op_3018"), val = tensor([1])]; + tensor var_3019_cast = reduce_mean(axes = var_3018, keep_dims = var_1181, x = zero_mean_sq_83_cast)[name = tensor("op_3019_cast")]; + tensor var_3020_to_fp16 = const()[name = tensor("op_3020_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3021_cast = add(x = var_3019_cast, y = var_3020_to_fp16)[name = tensor("op_3021_cast")]; + tensor denom_83_epsilon_0_to_fp16 = const()[name = tensor("denom_83_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_83_cast = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_3021_cast)[name = tensor("denom_83_cast")]; + tensor out_83_cast = mul(x = zero_mean_83_cast, y = denom_83_cast)[name = tensor("out_83_cast")]; + tensor var_3025_to_fp16 = const()[name = tensor("op_3025_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(853945344)))]; + tensor var_3026_cast = add(x = out_83_cast, y = var_3025_to_fp16)[name = tensor("op_3026_cast")]; + tensor var_3028_to_fp16 = const()[name = tensor("op_3028_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(853947968)))]; + tensor input_207_cast = mul(x = var_3026_cast, y = var_3028_to_fp16)[name = tensor("input_207_cast")]; + tensor var_3036 = const()[name = tensor("op_3036"), val = tensor([1, 1])]; + tensor var_3038 = const()[name = tensor("op_3038"), val = tensor([1, 1])]; + tensor var_3040_pad_type_0 = const()[name = tensor("op_3040_pad_type_0"), val = tensor("custom")]; + tensor var_3040_pad_0 = const()[name = tensor("op_3040_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(853950592)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(880165056)))]; + tensor var_3040_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_3038, groups = var_1186, pad = var_3040_pad_0, pad_type = var_3040_pad_type_0, strides = var_3036, weight = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_207_cast)[name = tensor("op_3040_cast")]; + tensor var_3041_split_sizes_0 = const()[name = tensor("op_3041_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3041_axis_0 = const()[name = tensor("op_3041_axis_0"), val = tensor(1)]; + tensor var_3041_cast_0, tensor var_3041_cast_1 = split(axis = var_3041_axis_0, split_sizes = var_3041_split_sizes_0, x = var_3040_cast)[name = tensor("op_3041_cast")]; + tensor var_3043_mode_0 = const()[name = tensor("op_3043_mode_0"), val = tensor("EXACT")]; + tensor var_3043_cast = gelu(mode = var_3043_mode_0, x = var_3041_cast_1)[name = tensor("op_3043_cast")]; + tensor input_209_cast = mul(x = var_3041_cast_0, y = var_3043_cast)[name = tensor("input_209_cast")]; + tensor var_3047 = const()[name = tensor("op_3047"), val = tensor([1, 1])]; + tensor var_3049 = const()[name = tensor("op_3049"), val = tensor([1, 1])]; + tensor var_3051_pad_type_0 = const()[name = tensor("op_3051_pad_type_0"), val = tensor("custom")]; + tensor var_3051_pad_0 = const()[name = tensor("op_3051_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(880185600)))]; + tensor down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893292864)))]; + tensor var_3051_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_3049, groups = var_1186, pad = var_3051_pad_0, pad_type = var_3051_pad_type_0, strides = var_3047, weight = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_209_cast)[name = tensor("op_3051_cast")]; + tensor hidden_states_125_cast = add(x = var_3051_cast, y = inputs_83_cast)[name = tensor("hidden_states_125_cast")]; + tensor var_3053 = const()[name = tensor("op_3053"), val = tensor([2, 1280, 32, 32])]; + tensor input_211_cast = reshape(shape = var_3053, x = hidden_states_125_cast)[name = tensor("input_211_cast")]; + tensor var_3057 = const()[name = tensor("op_3057"), val = tensor([1, 1])]; + tensor var_3059 = const()[name = tensor("op_3059"), val = tensor([1, 1])]; + tensor hidden_states_127_pad_type_0 = const()[name = tensor("hidden_states_127_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_127_pad_0 = const()[name = tensor("hidden_states_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893295488)))]; + tensor down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896572352)))]; + tensor hidden_states_127_cast = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3059, groups = var_1186, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = var_3057, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_211_cast)[name = tensor("hidden_states_127_cast")]; + tensor input_213_cast = add(x = hidden_states_127_cast, y = hidden_states_61_cast)[name = tensor("input_213_cast")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_213_cast)[name = tensor("reshape_52_cast")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39_cast = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast)[name = tensor("reduce_mean_39_cast")]; + tensor sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast)[name = tensor("sub_26_cast")]; + tensor square_13_cast = square(x = sub_26_cast)[name = tensor("square_13_cast")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41_cast = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast)[name = tensor("reduce_mean_41_cast")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_26_cast = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast")]; + tensor sqrt_13_cast = sqrt(x = add_26_cast)[name = tensor("sqrt_13_cast")]; + tensor real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast)[name = tensor("real_div_13_cast")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast)[name = tensor("reshape_53_cast")]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896574976)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896577600)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_53_cast)[name = tensor("add_27_cast")]; + tensor input_217_cast = silu(x = add_27_cast)[name = tensor("input_217_cast")]; + tensor var_3074 = const()[name = tensor("op_3074"), val = tensor([1, 1])]; + tensor var_3076 = const()[name = tensor("op_3076"), val = tensor([1, 1])]; + tensor hidden_states_129_pad_type_0 = const()[name = tensor("hidden_states_129_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_129_pad_0 = const()[name = tensor("hidden_states_129_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896580224)))]; + tensor down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(926071488)))]; + tensor hidden_states_129_cast = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3076, groups = var_1186, pad = hidden_states_129_pad_0, pad_type = hidden_states_129_pad_type_0, strides = var_3074, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_217_cast)[name = tensor("hidden_states_129_cast")]; + tensor var_3082 = const()[name = tensor("op_3082"), val = tensor([1, 1])]; + tensor var_3084 = const()[name = tensor("op_3084"), val = tensor([1, 1])]; + tensor temb_11_pad_type_0 = const()[name = tensor("temb_11_pad_type_0"), val = tensor("custom")]; + tensor temb_11_pad_0 = const()[name = tensor("temb_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(926074112)))]; + tensor down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(929350976)))]; + tensor temb_11_cast = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3084, groups = var_1186, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_3082, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_11_cast")]; + tensor input_221_cast = add(x = hidden_states_129_cast, y = temb_11_cast)[name = tensor("input_221_cast")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_56_cast = reshape(shape = reshape_56_shape_0, x = input_221_cast)[name = tensor("reshape_56_cast")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42_cast = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast)[name = tensor("reduce_mean_42_cast")]; + tensor sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast)[name = tensor("sub_28_cast")]; + tensor square_14_cast = square(x = sub_28_cast)[name = tensor("square_14_cast")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44_cast = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast)[name = tensor("reduce_mean_44_cast")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_28_cast = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast")]; + tensor sqrt_14_cast = sqrt(x = add_28_cast)[name = tensor("sqrt_14_cast")]; + tensor real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast)[name = tensor("real_div_14_cast")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast)[name = tensor("reshape_57_cast")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(929353600)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(929356224)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_57_cast)[name = tensor("add_29_cast")]; + tensor input_225_cast = silu(x = add_29_cast)[name = tensor("input_225_cast")]; + tensor var_3094 = const()[name = tensor("op_3094"), val = tensor([1, 1])]; + tensor var_3096 = const()[name = tensor("op_3096"), val = tensor([1, 1])]; + tensor hidden_states_131_pad_type_0 = const()[name = tensor("hidden_states_131_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_131_pad_0 = const()[name = tensor("hidden_states_131_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(929358848)))]; + tensor down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(958850112)))]; + tensor hidden_states_131_cast = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3096, groups = var_1186, pad = hidden_states_131_pad_0, pad_type = hidden_states_131_pad_type_0, strides = var_3094, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_225_cast)[name = tensor("hidden_states_131_cast")]; + tensor hidden_states_133_cast = add(x = input_213_cast, y = hidden_states_131_cast)[name = tensor("hidden_states_133_cast")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_60_cast = reshape(shape = reshape_60_shape_0, x = hidden_states_133_cast)[name = tensor("reshape_60_cast")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45_cast = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast)[name = tensor("reduce_mean_45_cast")]; + tensor sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast)[name = tensor("sub_30_cast")]; + tensor square_15_cast = square(x = sub_30_cast)[name = tensor("square_15_cast")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47_cast = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast)[name = tensor("reduce_mean_47_cast")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_30_cast = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast")]; + tensor sqrt_15_cast = sqrt(x = add_30_cast)[name = tensor("sqrt_15_cast")]; + tensor real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast)[name = tensor("real_div_15_cast")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast)[name = tensor("reshape_61_cast")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(958852736)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(958855360)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_61_cast)[name = tensor("add_31_cast")]; + tensor var_3134 = const()[name = tensor("op_3134"), val = tensor([1, 1])]; + tensor var_3136 = const()[name = tensor("op_3136"), val = tensor([1, 1])]; + tensor hidden_states_135_pad_type_0 = const()[name = tensor("hidden_states_135_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_135_pad_0 = const()[name = tensor("hidden_states_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(958857984)))]; + tensor down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962134848)))]; + tensor hidden_states_135_cast = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3136, groups = var_1186, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_3134, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_31_cast)[name = tensor("hidden_states_135_cast")]; + tensor var_3141 = const()[name = tensor("op_3141"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_85_cast = reshape(shape = var_3141, x = hidden_states_135_cast)[name = tensor("inputs_85_cast")]; + tensor var_3151 = const()[name = tensor("op_3151"), val = tensor([1])]; + tensor channels_mean_85_cast = reduce_mean(axes = var_3151, keep_dims = var_1181, x = inputs_85_cast)[name = tensor("channels_mean_85_cast")]; + tensor zero_mean_85_cast = sub(x = inputs_85_cast, y = channels_mean_85_cast)[name = tensor("zero_mean_85_cast")]; + tensor zero_mean_sq_85_cast = mul(x = zero_mean_85_cast, y = zero_mean_85_cast)[name = tensor("zero_mean_sq_85_cast")]; + tensor var_3155 = const()[name = tensor("op_3155"), val = tensor([1])]; + tensor var_3156_cast = reduce_mean(axes = var_3155, keep_dims = var_1181, x = zero_mean_sq_85_cast)[name = tensor("op_3156_cast")]; + tensor var_3157_to_fp16 = const()[name = tensor("op_3157_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3158_cast = add(x = var_3156_cast, y = var_3157_to_fp16)[name = tensor("op_3158_cast")]; + tensor denom_85_epsilon_0_to_fp16 = const()[name = tensor("denom_85_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_85_cast = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_3158_cast)[name = tensor("denom_85_cast")]; + tensor out_85_cast = mul(x = zero_mean_85_cast, y = denom_85_cast)[name = tensor("out_85_cast")]; + tensor var_3162_to_fp16 = const()[name = tensor("op_3162_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962137472)))]; + tensor var_3163_cast = add(x = out_85_cast, y = var_3162_to_fp16)[name = tensor("op_3163_cast")]; + tensor var_3165_to_fp16 = const()[name = tensor("op_3165_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962140096)))]; + tensor hidden_states_137_cast = mul(x = var_3163_cast, y = var_3165_to_fp16)[name = tensor("hidden_states_137_cast")]; + tensor var_3172 = const()[name = tensor("op_3172"), val = tensor([1, 1])]; + tensor var_3174 = const()[name = tensor("op_3174"), val = tensor([1, 1])]; + tensor q_57_pad_type_0 = const()[name = tensor("q_57_pad_type_0"), val = tensor("custom")]; + tensor q_57_pad_0 = const()[name = tensor("q_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962142720)))]; + tensor q_57_cast = conv(dilations = var_3174, groups = var_1186, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_3172, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_137_cast)[name = tensor("q_57_cast")]; + tensor var_3178 = const()[name = tensor("op_3178"), val = tensor([1, 1])]; + tensor var_3180 = const()[name = tensor("op_3180"), val = tensor([1, 1])]; + tensor k_57_pad_type_0 = const()[name = tensor("k_57_pad_type_0"), val = tensor("custom")]; + tensor k_57_pad_0 = const()[name = tensor("k_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(965419584)))]; + tensor k_57_cast = conv(dilations = var_3180, groups = var_1186, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_3178, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_137_cast)[name = tensor("k_57_cast")]; + tensor var_3184 = const()[name = tensor("op_3184"), val = tensor([1, 1])]; + tensor var_3186 = const()[name = tensor("op_3186"), val = tensor([1, 1])]; + tensor v_57_pad_type_0 = const()[name = tensor("v_57_pad_type_0"), val = tensor("custom")]; + tensor v_57_pad_0 = const()[name = tensor("v_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(968696448)))]; + tensor v_57_cast = conv(dilations = var_3186, groups = var_1186, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_3184, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_137_cast)[name = tensor("v_57_cast")]; + tensor var_3190 = const()[name = tensor("op_3190"), val = tensor([2, 20, 64, -1])]; + tensor var_3191_cast = reshape(shape = var_3190, x = q_57_cast)[name = tensor("op_3191_cast")]; + tensor var_3192 = const()[name = tensor("op_3192"), val = tensor([2, 20, 64, -1])]; + tensor var_3193_cast = reshape(shape = var_3192, x = k_57_cast)[name = tensor("op_3193_cast")]; + tensor var_3194 = const()[name = tensor("op_3194"), val = tensor([2, 20, 64, -1])]; + tensor var_3195_cast = reshape(shape = var_3194, x = v_57_cast)[name = tensor("op_3195_cast")]; + tensor attn_weights_113_transpose_x_0 = const()[name = tensor("attn_weights_113_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_113_transpose_y_0 = const()[name = tensor("attn_weights_113_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_113_cast = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3191_cast, y = var_3193_cast)[name = tensor("attn_weights_113_cast")]; + tensor attn_weights_115_cast = mul(x = attn_weights_113_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_115_cast")]; + tensor var_3199_cast = softmax(axis = var_1170, x = attn_weights_115_cast)[name = tensor("op_3199_cast")]; + tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; + tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; + tensor attn_57_cast = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_3195_cast, y = var_3199_cast)[name = tensor("attn_57_cast")]; + tensor var_3203 = const()[name = tensor("op_3203"), val = tensor([2, 1280, 1, -1])]; + tensor input_229_cast = reshape(shape = var_3203, x = attn_57_cast)[name = tensor("input_229_cast")]; + tensor var_3208 = const()[name = tensor("op_3208"), val = tensor([1, 1])]; + tensor var_3210 = const()[name = tensor("op_3210"), val = tensor([1, 1])]; + tensor var_3212_pad_type_0 = const()[name = tensor("op_3212_pad_type_0"), val = tensor("custom")]; + tensor var_3212_pad_0 = const()[name = tensor("op_3212_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971973312)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975250176)))]; + tensor var_3212_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3210, groups = var_1186, pad = var_3212_pad_0, pad_type = var_3212_pad_type_0, strides = var_3208, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_229_cast)[name = tensor("op_3212_cast")]; + tensor inputs_87_cast = add(x = var_3212_cast, y = inputs_85_cast)[name = tensor("inputs_87_cast")]; + tensor var_3216 = const()[name = tensor("op_3216"), val = tensor([1])]; + tensor channels_mean_87_cast = reduce_mean(axes = var_3216, keep_dims = var_1181, x = inputs_87_cast)[name = tensor("channels_mean_87_cast")]; + tensor zero_mean_87_cast = sub(x = inputs_87_cast, y = channels_mean_87_cast)[name = tensor("zero_mean_87_cast")]; + tensor zero_mean_sq_87_cast = mul(x = zero_mean_87_cast, y = zero_mean_87_cast)[name = tensor("zero_mean_sq_87_cast")]; + tensor var_3220 = const()[name = tensor("op_3220"), val = tensor([1])]; + tensor var_3221_cast = reduce_mean(axes = var_3220, keep_dims = var_1181, x = zero_mean_sq_87_cast)[name = tensor("op_3221_cast")]; + tensor var_3222_to_fp16 = const()[name = tensor("op_3222_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3223_cast = add(x = var_3221_cast, y = var_3222_to_fp16)[name = tensor("op_3223_cast")]; + tensor denom_87_epsilon_0_to_fp16 = const()[name = tensor("denom_87_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_87_cast = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_3223_cast)[name = tensor("denom_87_cast")]; + tensor out_87_cast = mul(x = zero_mean_87_cast, y = denom_87_cast)[name = tensor("out_87_cast")]; + tensor var_3227_to_fp16 = const()[name = tensor("op_3227_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975252800)))]; + tensor var_3228_cast = add(x = out_87_cast, y = var_3227_to_fp16)[name = tensor("op_3228_cast")]; + tensor var_3230_to_fp16 = const()[name = tensor("op_3230_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975255424)))]; + tensor hidden_states_139_cast = mul(x = var_3228_cast, y = var_3230_to_fp16)[name = tensor("hidden_states_139_cast")]; + tensor var_3237 = const()[name = tensor("op_3237"), val = tensor([1, 1])]; + tensor var_3239 = const()[name = tensor("op_3239"), val = tensor([1, 1])]; + tensor q_59_pad_type_0 = const()[name = tensor("q_59_pad_type_0"), val = tensor("custom")]; + tensor q_59_pad_0 = const()[name = tensor("q_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975258048)))]; + tensor q_59_cast = conv(dilations = var_3239, groups = var_1186, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_3237, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_139_cast)[name = tensor("q_59_cast")]; + tensor var_3243 = const()[name = tensor("op_3243"), val = tensor([1, 1])]; + tensor var_3245 = const()[name = tensor("op_3245"), val = tensor([1, 1])]; + tensor k_59_pad_type_0 = const()[name = tensor("k_59_pad_type_0"), val = tensor("custom")]; + tensor k_59_pad_0 = const()[name = tensor("k_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(978534912)))]; + tensor k_59_cast = conv(dilations = var_3245, groups = var_1186, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_3243, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_59_cast")]; + tensor var_3249 = const()[name = tensor("op_3249"), val = tensor([1, 1])]; + tensor var_3251 = const()[name = tensor("op_3251"), val = tensor([1, 1])]; + tensor v_59_pad_type_0 = const()[name = tensor("v_59_pad_type_0"), val = tensor("custom")]; + tensor v_59_pad_0 = const()[name = tensor("v_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(983777856)))]; + tensor v_59_cast = conv(dilations = var_3251, groups = var_1186, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_3249, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_59_cast")]; + tensor var_3255 = const()[name = tensor("op_3255"), val = tensor([2, 20, 64, -1])]; + tensor var_3256_cast = reshape(shape = var_3255, x = q_59_cast)[name = tensor("op_3256_cast")]; + tensor var_3257 = const()[name = tensor("op_3257"), val = tensor([2, 20, 64, -1])]; + tensor var_3258_cast = reshape(shape = var_3257, x = k_59_cast)[name = tensor("op_3258_cast")]; + tensor var_3259 = const()[name = tensor("op_3259"), val = tensor([2, 20, 64, -1])]; + tensor var_3260_cast = reshape(shape = var_3259, x = v_59_cast)[name = tensor("op_3260_cast")]; + tensor attn_weights_117_transpose_x_0 = const()[name = tensor("attn_weights_117_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_117_transpose_y_0 = const()[name = tensor("attn_weights_117_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_117_cast = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_3256_cast, y = var_3258_cast)[name = tensor("attn_weights_117_cast")]; + tensor attn_weights_119_cast = mul(x = attn_weights_117_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_119_cast")]; + tensor var_3264_cast = softmax(axis = var_1170, x = attn_weights_119_cast)[name = tensor("op_3264_cast")]; + tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; + tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; + tensor attn_59_cast = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_3260_cast, y = var_3264_cast)[name = tensor("attn_59_cast")]; + tensor var_3268 = const()[name = tensor("op_3268"), val = tensor([2, 1280, 1, -1])]; + tensor input_231_cast = reshape(shape = var_3268, x = attn_59_cast)[name = tensor("input_231_cast")]; + tensor var_3273 = const()[name = tensor("op_3273"), val = tensor([1, 1])]; + tensor var_3275 = const()[name = tensor("op_3275"), val = tensor([1, 1])]; + tensor var_3277_pad_type_0 = const()[name = tensor("op_3277_pad_type_0"), val = tensor("custom")]; + tensor var_3277_pad_0 = const()[name = tensor("op_3277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(989020800)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992297664)))]; + tensor var_3277_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3275, groups = var_1186, pad = var_3277_pad_0, pad_type = var_3277_pad_type_0, strides = var_3273, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_231_cast)[name = tensor("op_3277_cast")]; + tensor inputs_89_cast = add(x = var_3277_cast, y = inputs_87_cast)[name = tensor("inputs_89_cast")]; + tensor var_3281 = const()[name = tensor("op_3281"), val = tensor([1])]; + tensor channels_mean_89_cast = reduce_mean(axes = var_3281, keep_dims = var_1181, x = inputs_89_cast)[name = tensor("channels_mean_89_cast")]; + tensor zero_mean_89_cast = sub(x = inputs_89_cast, y = channels_mean_89_cast)[name = tensor("zero_mean_89_cast")]; + tensor zero_mean_sq_89_cast = mul(x = zero_mean_89_cast, y = zero_mean_89_cast)[name = tensor("zero_mean_sq_89_cast")]; + tensor var_3285 = const()[name = tensor("op_3285"), val = tensor([1])]; + tensor var_3286_cast = reduce_mean(axes = var_3285, keep_dims = var_1181, x = zero_mean_sq_89_cast)[name = tensor("op_3286_cast")]; + tensor var_3287_to_fp16 = const()[name = tensor("op_3287_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3288_cast = add(x = var_3286_cast, y = var_3287_to_fp16)[name = tensor("op_3288_cast")]; + tensor denom_89_epsilon_0_to_fp16 = const()[name = tensor("denom_89_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_89_cast = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_3288_cast)[name = tensor("denom_89_cast")]; + tensor out_89_cast = mul(x = zero_mean_89_cast, y = denom_89_cast)[name = tensor("out_89_cast")]; + tensor var_3292_to_fp16 = const()[name = tensor("op_3292_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992300288)))]; + tensor var_3293_cast = add(x = out_89_cast, y = var_3292_to_fp16)[name = tensor("op_3293_cast")]; + tensor var_3295_to_fp16 = const()[name = tensor("op_3295_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992302912)))]; + tensor input_233_cast = mul(x = var_3293_cast, y = var_3295_to_fp16)[name = tensor("input_233_cast")]; + tensor var_3303 = const()[name = tensor("op_3303"), val = tensor([1, 1])]; + tensor var_3305 = const()[name = tensor("op_3305"), val = tensor([1, 1])]; + tensor var_3307_pad_type_0 = const()[name = tensor("op_3307_pad_type_0"), val = tensor("custom")]; + tensor var_3307_pad_0 = const()[name = tensor("op_3307_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992305536)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018520000)))]; + tensor var_3307_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3305, groups = var_1186, pad = var_3307_pad_0, pad_type = var_3307_pad_type_0, strides = var_3303, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_233_cast)[name = tensor("op_3307_cast")]; + tensor var_3308_split_sizes_0 = const()[name = tensor("op_3308_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3308_axis_0 = const()[name = tensor("op_3308_axis_0"), val = tensor(1)]; + tensor var_3308_cast_0, tensor var_3308_cast_1 = split(axis = var_3308_axis_0, split_sizes = var_3308_split_sizes_0, x = var_3307_cast)[name = tensor("op_3308_cast")]; + tensor var_3310_mode_0 = const()[name = tensor("op_3310_mode_0"), val = tensor("EXACT")]; + tensor var_3310_cast = gelu(mode = var_3310_mode_0, x = var_3308_cast_1)[name = tensor("op_3310_cast")]; + tensor input_235_cast = mul(x = var_3308_cast_0, y = var_3310_cast)[name = tensor("input_235_cast")]; + tensor var_3314 = const()[name = tensor("op_3314"), val = tensor([1, 1])]; + tensor var_3316 = const()[name = tensor("op_3316"), val = tensor([1, 1])]; + tensor var_3318_pad_type_0 = const()[name = tensor("op_3318_pad_type_0"), val = tensor("custom")]; + tensor var_3318_pad_0 = const()[name = tensor("op_3318_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018540544)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031647808)))]; + tensor var_3318_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3316, groups = var_1186, pad = var_3318_pad_0, pad_type = var_3318_pad_type_0, strides = var_3314, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_235_cast)[name = tensor("op_3318_cast")]; + tensor inputs_91_cast = add(x = var_3318_cast, y = inputs_89_cast)[name = tensor("inputs_91_cast")]; + tensor var_3328 = const()[name = tensor("op_3328"), val = tensor([1])]; + tensor channels_mean_91_cast = reduce_mean(axes = var_3328, keep_dims = var_1181, x = inputs_91_cast)[name = tensor("channels_mean_91_cast")]; + tensor zero_mean_91_cast = sub(x = inputs_91_cast, y = channels_mean_91_cast)[name = tensor("zero_mean_91_cast")]; + tensor zero_mean_sq_91_cast = mul(x = zero_mean_91_cast, y = zero_mean_91_cast)[name = tensor("zero_mean_sq_91_cast")]; + tensor var_3332 = const()[name = tensor("op_3332"), val = tensor([1])]; + tensor var_3333_cast = reduce_mean(axes = var_3332, keep_dims = var_1181, x = zero_mean_sq_91_cast)[name = tensor("op_3333_cast")]; + tensor var_3334_to_fp16 = const()[name = tensor("op_3334_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3335_cast = add(x = var_3333_cast, y = var_3334_to_fp16)[name = tensor("op_3335_cast")]; + tensor denom_91_epsilon_0_to_fp16 = const()[name = tensor("denom_91_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_91_cast = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_3335_cast)[name = tensor("denom_91_cast")]; + tensor out_91_cast = mul(x = zero_mean_91_cast, y = denom_91_cast)[name = tensor("out_91_cast")]; + tensor var_3339_to_fp16 = const()[name = tensor("op_3339_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031650432)))]; + tensor var_3340_cast = add(x = out_91_cast, y = var_3339_to_fp16)[name = tensor("op_3340_cast")]; + tensor var_3342_to_fp16 = const()[name = tensor("op_3342_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031653056)))]; + tensor hidden_states_143_cast = mul(x = var_3340_cast, y = var_3342_to_fp16)[name = tensor("hidden_states_143_cast")]; + tensor var_3349 = const()[name = tensor("op_3349"), val = tensor([1, 1])]; + tensor var_3351 = const()[name = tensor("op_3351"), val = tensor([1, 1])]; + tensor q_61_pad_type_0 = const()[name = tensor("q_61_pad_type_0"), val = tensor("custom")]; + tensor q_61_pad_0 = const()[name = tensor("q_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031655680)))]; + tensor q_61_cast = conv(dilations = var_3351, groups = var_1186, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_3349, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_143_cast)[name = tensor("q_61_cast")]; + tensor var_3355 = const()[name = tensor("op_3355"), val = tensor([1, 1])]; + tensor var_3357 = const()[name = tensor("op_3357"), val = tensor([1, 1])]; + tensor k_61_pad_type_0 = const()[name = tensor("k_61_pad_type_0"), val = tensor("custom")]; + tensor k_61_pad_0 = const()[name = tensor("k_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034932544)))]; + tensor k_61_cast = conv(dilations = var_3357, groups = var_1186, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_3355, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_143_cast)[name = tensor("k_61_cast")]; + tensor var_3361 = const()[name = tensor("op_3361"), val = tensor([1, 1])]; + tensor var_3363 = const()[name = tensor("op_3363"), val = tensor([1, 1])]; + tensor v_61_pad_type_0 = const()[name = tensor("v_61_pad_type_0"), val = tensor("custom")]; + tensor v_61_pad_0 = const()[name = tensor("v_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1038209408)))]; + tensor v_61_cast = conv(dilations = var_3363, groups = var_1186, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_3361, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_143_cast)[name = tensor("v_61_cast")]; + tensor var_3367 = const()[name = tensor("op_3367"), val = tensor([2, 20, 64, -1])]; + tensor var_3368_cast = reshape(shape = var_3367, x = q_61_cast)[name = tensor("op_3368_cast")]; + tensor var_3369 = const()[name = tensor("op_3369"), val = tensor([2, 20, 64, -1])]; + tensor var_3370_cast = reshape(shape = var_3369, x = k_61_cast)[name = tensor("op_3370_cast")]; + tensor var_3371 = const()[name = tensor("op_3371"), val = tensor([2, 20, 64, -1])]; + tensor var_3372_cast = reshape(shape = var_3371, x = v_61_cast)[name = tensor("op_3372_cast")]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_121_cast = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3368_cast, y = var_3370_cast)[name = tensor("attn_weights_121_cast")]; + tensor attn_weights_123_cast = mul(x = attn_weights_121_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_123_cast")]; + tensor var_3376_cast = softmax(axis = var_1170, x = attn_weights_123_cast)[name = tensor("op_3376_cast")]; + tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; + tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; + tensor attn_61_cast = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_3372_cast, y = var_3376_cast)[name = tensor("attn_61_cast")]; + tensor var_3380 = const()[name = tensor("op_3380"), val = tensor([2, 1280, 1, -1])]; + tensor input_237_cast = reshape(shape = var_3380, x = attn_61_cast)[name = tensor("input_237_cast")]; + tensor var_3385 = const()[name = tensor("op_3385"), val = tensor([1, 1])]; + tensor var_3387 = const()[name = tensor("op_3387"), val = tensor([1, 1])]; + tensor var_3389_pad_type_0 = const()[name = tensor("op_3389_pad_type_0"), val = tensor("custom")]; + tensor var_3389_pad_0 = const()[name = tensor("op_3389_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1041486272)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044763136)))]; + tensor var_3389_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_3387, groups = var_1186, pad = var_3389_pad_0, pad_type = var_3389_pad_type_0, strides = var_3385, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_237_cast)[name = tensor("op_3389_cast")]; + tensor inputs_93_cast = add(x = var_3389_cast, y = inputs_91_cast)[name = tensor("inputs_93_cast")]; + tensor var_3393 = const()[name = tensor("op_3393"), val = tensor([1])]; + tensor channels_mean_93_cast = reduce_mean(axes = var_3393, keep_dims = var_1181, x = inputs_93_cast)[name = tensor("channels_mean_93_cast")]; + tensor zero_mean_93_cast = sub(x = inputs_93_cast, y = channels_mean_93_cast)[name = tensor("zero_mean_93_cast")]; + tensor zero_mean_sq_93_cast = mul(x = zero_mean_93_cast, y = zero_mean_93_cast)[name = tensor("zero_mean_sq_93_cast")]; + tensor var_3397 = const()[name = tensor("op_3397"), val = tensor([1])]; + tensor var_3398_cast = reduce_mean(axes = var_3397, keep_dims = var_1181, x = zero_mean_sq_93_cast)[name = tensor("op_3398_cast")]; + tensor var_3399_to_fp16 = const()[name = tensor("op_3399_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3400_cast = add(x = var_3398_cast, y = var_3399_to_fp16)[name = tensor("op_3400_cast")]; + tensor denom_93_epsilon_0_to_fp16 = const()[name = tensor("denom_93_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_93_cast = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_3400_cast)[name = tensor("denom_93_cast")]; + tensor out_93_cast = mul(x = zero_mean_93_cast, y = denom_93_cast)[name = tensor("out_93_cast")]; + tensor var_3404_to_fp16 = const()[name = tensor("op_3404_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044765760)))]; + tensor var_3405_cast = add(x = out_93_cast, y = var_3404_to_fp16)[name = tensor("op_3405_cast")]; + tensor var_3407_to_fp16 = const()[name = tensor("op_3407_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044768384)))]; + tensor hidden_states_145_cast = mul(x = var_3405_cast, y = var_3407_to_fp16)[name = tensor("hidden_states_145_cast")]; + tensor var_3414 = const()[name = tensor("op_3414"), val = tensor([1, 1])]; + tensor var_3416 = const()[name = tensor("op_3416"), val = tensor([1, 1])]; + tensor q_63_pad_type_0 = const()[name = tensor("q_63_pad_type_0"), val = tensor("custom")]; + tensor q_63_pad_0 = const()[name = tensor("q_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044771008)))]; + tensor q_63_cast = conv(dilations = var_3416, groups = var_1186, pad = q_63_pad_0, pad_type = q_63_pad_type_0, strides = var_3414, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_145_cast)[name = tensor("q_63_cast")]; + tensor var_3420 = const()[name = tensor("op_3420"), val = tensor([1, 1])]; + tensor var_3422 = const()[name = tensor("op_3422"), val = tensor([1, 1])]; + tensor k_63_pad_type_0 = const()[name = tensor("k_63_pad_type_0"), val = tensor("custom")]; + tensor k_63_pad_0 = const()[name = tensor("k_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1048047872)))]; + tensor k_63_cast = conv(dilations = var_3422, groups = var_1186, pad = k_63_pad_0, pad_type = k_63_pad_type_0, strides = var_3420, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_63_cast")]; + tensor var_3426 = const()[name = tensor("op_3426"), val = tensor([1, 1])]; + tensor var_3428 = const()[name = tensor("op_3428"), val = tensor([1, 1])]; + tensor v_63_pad_type_0 = const()[name = tensor("v_63_pad_type_0"), val = tensor("custom")]; + tensor v_63_pad_0 = const()[name = tensor("v_63_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1053290816)))]; + tensor v_63_cast = conv(dilations = var_3428, groups = var_1186, pad = v_63_pad_0, pad_type = v_63_pad_type_0, strides = var_3426, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_63_cast")]; + tensor var_3432 = const()[name = tensor("op_3432"), val = tensor([2, 20, 64, -1])]; + tensor var_3433_cast = reshape(shape = var_3432, x = q_63_cast)[name = tensor("op_3433_cast")]; + tensor var_3434 = const()[name = tensor("op_3434"), val = tensor([2, 20, 64, -1])]; + tensor var_3435_cast = reshape(shape = var_3434, x = k_63_cast)[name = tensor("op_3435_cast")]; + tensor var_3436 = const()[name = tensor("op_3436"), val = tensor([2, 20, 64, -1])]; + tensor var_3437_cast = reshape(shape = var_3436, x = v_63_cast)[name = tensor("op_3437_cast")]; + tensor attn_weights_125_transpose_x_0 = const()[name = tensor("attn_weights_125_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_125_transpose_y_0 = const()[name = tensor("attn_weights_125_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_125_cast = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_3433_cast, y = var_3435_cast)[name = tensor("attn_weights_125_cast")]; + tensor attn_weights_127_cast = mul(x = attn_weights_125_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_127_cast")]; + tensor var_3441_cast = softmax(axis = var_1170, x = attn_weights_127_cast)[name = tensor("op_3441_cast")]; + tensor attn_63_transpose_x_0 = const()[name = tensor("attn_63_transpose_x_0"), val = tensor(false)]; + tensor attn_63_transpose_y_0 = const()[name = tensor("attn_63_transpose_y_0"), val = tensor(true)]; + tensor attn_63_cast = matmul(transpose_x = attn_63_transpose_x_0, transpose_y = attn_63_transpose_y_0, x = var_3437_cast, y = var_3441_cast)[name = tensor("attn_63_cast")]; + tensor var_3445 = const()[name = tensor("op_3445"), val = tensor([2, 1280, 1, -1])]; + tensor input_239_cast = reshape(shape = var_3445, x = attn_63_cast)[name = tensor("input_239_cast")]; + tensor var_3450 = const()[name = tensor("op_3450"), val = tensor([1, 1])]; + tensor var_3452 = const()[name = tensor("op_3452"), val = tensor([1, 1])]; + tensor var_3454_pad_type_0 = const()[name = tensor("op_3454_pad_type_0"), val = tensor("custom")]; + tensor var_3454_pad_0 = const()[name = tensor("op_3454_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1058533760)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061810624)))]; + tensor var_3454_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_3452, groups = var_1186, pad = var_3454_pad_0, pad_type = var_3454_pad_type_0, strides = var_3450, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_239_cast)[name = tensor("op_3454_cast")]; + tensor inputs_95_cast = add(x = var_3454_cast, y = inputs_93_cast)[name = tensor("inputs_95_cast")]; + tensor var_3458 = const()[name = tensor("op_3458"), val = tensor([1])]; + tensor channels_mean_95_cast = reduce_mean(axes = var_3458, keep_dims = var_1181, x = inputs_95_cast)[name = tensor("channels_mean_95_cast")]; + tensor zero_mean_95_cast = sub(x = inputs_95_cast, y = channels_mean_95_cast)[name = tensor("zero_mean_95_cast")]; + tensor zero_mean_sq_95_cast = mul(x = zero_mean_95_cast, y = zero_mean_95_cast)[name = tensor("zero_mean_sq_95_cast")]; + tensor var_3462 = const()[name = tensor("op_3462"), val = tensor([1])]; + tensor var_3463_cast = reduce_mean(axes = var_3462, keep_dims = var_1181, x = zero_mean_sq_95_cast)[name = tensor("op_3463_cast")]; + tensor var_3464_to_fp16 = const()[name = tensor("op_3464_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3465_cast = add(x = var_3463_cast, y = var_3464_to_fp16)[name = tensor("op_3465_cast")]; + tensor denom_95_epsilon_0_to_fp16 = const()[name = tensor("denom_95_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_95_cast = rsqrt(epsilon = denom_95_epsilon_0_to_fp16, x = var_3465_cast)[name = tensor("denom_95_cast")]; + tensor out_95_cast = mul(x = zero_mean_95_cast, y = denom_95_cast)[name = tensor("out_95_cast")]; + tensor var_3469_to_fp16 = const()[name = tensor("op_3469_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061813248)))]; + tensor var_3470_cast = add(x = out_95_cast, y = var_3469_to_fp16)[name = tensor("op_3470_cast")]; + tensor var_3472_to_fp16 = const()[name = tensor("op_3472_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061815872)))]; + tensor input_241_cast = mul(x = var_3470_cast, y = var_3472_to_fp16)[name = tensor("input_241_cast")]; + tensor var_3480 = const()[name = tensor("op_3480"), val = tensor([1, 1])]; + tensor var_3482 = const()[name = tensor("op_3482"), val = tensor([1, 1])]; + tensor var_3484_pad_type_0 = const()[name = tensor("op_3484_pad_type_0"), val = tensor("custom")]; + tensor var_3484_pad_0 = const()[name = tensor("op_3484_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061818496)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1088032960)))]; + tensor var_3484_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_3482, groups = var_1186, pad = var_3484_pad_0, pad_type = var_3484_pad_type_0, strides = var_3480, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_241_cast)[name = tensor("op_3484_cast")]; + tensor var_3485_split_sizes_0 = const()[name = tensor("op_3485_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3485_axis_0 = const()[name = tensor("op_3485_axis_0"), val = tensor(1)]; + tensor var_3485_cast_0, tensor var_3485_cast_1 = split(axis = var_3485_axis_0, split_sizes = var_3485_split_sizes_0, x = var_3484_cast)[name = tensor("op_3485_cast")]; + tensor var_3487_mode_0 = const()[name = tensor("op_3487_mode_0"), val = tensor("EXACT")]; + tensor var_3487_cast = gelu(mode = var_3487_mode_0, x = var_3485_cast_1)[name = tensor("op_3487_cast")]; + tensor input_243_cast = mul(x = var_3485_cast_0, y = var_3487_cast)[name = tensor("input_243_cast")]; + tensor var_3491 = const()[name = tensor("op_3491"), val = tensor([1, 1])]; + tensor var_3493 = const()[name = tensor("op_3493"), val = tensor([1, 1])]; + tensor var_3495_pad_type_0 = const()[name = tensor("op_3495_pad_type_0"), val = tensor("custom")]; + tensor var_3495_pad_0 = const()[name = tensor("op_3495_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1088053504)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101160768)))]; + tensor var_3495_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_3493, groups = var_1186, pad = var_3495_pad_0, pad_type = var_3495_pad_type_0, strides = var_3491, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_243_cast)[name = tensor("op_3495_cast")]; + tensor inputs_97_cast = add(x = var_3495_cast, y = inputs_95_cast)[name = tensor("inputs_97_cast")]; + tensor var_3505 = const()[name = tensor("op_3505"), val = tensor([1])]; + tensor channels_mean_97_cast = reduce_mean(axes = var_3505, keep_dims = var_1181, x = inputs_97_cast)[name = tensor("channels_mean_97_cast")]; + tensor zero_mean_97_cast = sub(x = inputs_97_cast, y = channels_mean_97_cast)[name = tensor("zero_mean_97_cast")]; + tensor zero_mean_sq_97_cast = mul(x = zero_mean_97_cast, y = zero_mean_97_cast)[name = tensor("zero_mean_sq_97_cast")]; + tensor var_3509 = const()[name = tensor("op_3509"), val = tensor([1])]; + tensor var_3510_cast = reduce_mean(axes = var_3509, keep_dims = var_1181, x = zero_mean_sq_97_cast)[name = tensor("op_3510_cast")]; + tensor var_3511_to_fp16 = const()[name = tensor("op_3511_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3512_cast = add(x = var_3510_cast, y = var_3511_to_fp16)[name = tensor("op_3512_cast")]; + tensor denom_97_epsilon_0_to_fp16 = const()[name = tensor("denom_97_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_97_cast = rsqrt(epsilon = denom_97_epsilon_0_to_fp16, x = var_3512_cast)[name = tensor("denom_97_cast")]; + tensor out_97_cast = mul(x = zero_mean_97_cast, y = denom_97_cast)[name = tensor("out_97_cast")]; + tensor var_3516_to_fp16 = const()[name = tensor("op_3516_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101163392)))]; + tensor var_3517_cast = add(x = out_97_cast, y = var_3516_to_fp16)[name = tensor("op_3517_cast")]; + tensor var_3519_to_fp16 = const()[name = tensor("op_3519_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101166016)))]; + tensor hidden_states_149_cast = mul(x = var_3517_cast, y = var_3519_to_fp16)[name = tensor("hidden_states_149_cast")]; + tensor var_3526 = const()[name = tensor("op_3526"), val = tensor([1, 1])]; + tensor var_3528 = const()[name = tensor("op_3528"), val = tensor([1, 1])]; + tensor q_65_pad_type_0 = const()[name = tensor("q_65_pad_type_0"), val = tensor("custom")]; + tensor q_65_pad_0 = const()[name = tensor("q_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101168640)))]; + tensor q_65_cast = conv(dilations = var_3528, groups = var_1186, pad = q_65_pad_0, pad_type = q_65_pad_type_0, strides = var_3526, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_149_cast)[name = tensor("q_65_cast")]; + tensor var_3532 = const()[name = tensor("op_3532"), val = tensor([1, 1])]; + tensor var_3534 = const()[name = tensor("op_3534"), val = tensor([1, 1])]; + tensor k_65_pad_type_0 = const()[name = tensor("k_65_pad_type_0"), val = tensor("custom")]; + tensor k_65_pad_0 = const()[name = tensor("k_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1104445504)))]; + tensor k_65_cast = conv(dilations = var_3534, groups = var_1186, pad = k_65_pad_0, pad_type = k_65_pad_type_0, strides = var_3532, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_149_cast)[name = tensor("k_65_cast")]; + tensor var_3538 = const()[name = tensor("op_3538"), val = tensor([1, 1])]; + tensor var_3540 = const()[name = tensor("op_3540"), val = tensor([1, 1])]; + tensor v_65_pad_type_0 = const()[name = tensor("v_65_pad_type_0"), val = tensor("custom")]; + tensor v_65_pad_0 = const()[name = tensor("v_65_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107722368)))]; + tensor v_65_cast = conv(dilations = var_3540, groups = var_1186, pad = v_65_pad_0, pad_type = v_65_pad_type_0, strides = var_3538, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_149_cast)[name = tensor("v_65_cast")]; + tensor var_3544 = const()[name = tensor("op_3544"), val = tensor([2, 20, 64, -1])]; + tensor var_3545_cast = reshape(shape = var_3544, x = q_65_cast)[name = tensor("op_3545_cast")]; + tensor var_3546 = const()[name = tensor("op_3546"), val = tensor([2, 20, 64, -1])]; + tensor var_3547_cast = reshape(shape = var_3546, x = k_65_cast)[name = tensor("op_3547_cast")]; + tensor var_3548 = const()[name = tensor("op_3548"), val = tensor([2, 20, 64, -1])]; + tensor var_3549_cast = reshape(shape = var_3548, x = v_65_cast)[name = tensor("op_3549_cast")]; + tensor attn_weights_129_transpose_x_0 = const()[name = tensor("attn_weights_129_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_129_transpose_y_0 = const()[name = tensor("attn_weights_129_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_129_cast = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3545_cast, y = var_3547_cast)[name = tensor("attn_weights_129_cast")]; + tensor attn_weights_131_cast = mul(x = attn_weights_129_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_131_cast")]; + tensor var_3553_cast = softmax(axis = var_1170, x = attn_weights_131_cast)[name = tensor("op_3553_cast")]; + tensor attn_65_transpose_x_0 = const()[name = tensor("attn_65_transpose_x_0"), val = tensor(false)]; + tensor attn_65_transpose_y_0 = const()[name = tensor("attn_65_transpose_y_0"), val = tensor(true)]; + tensor attn_65_cast = matmul(transpose_x = attn_65_transpose_x_0, transpose_y = attn_65_transpose_y_0, x = var_3549_cast, y = var_3553_cast)[name = tensor("attn_65_cast")]; + tensor var_3557 = const()[name = tensor("op_3557"), val = tensor([2, 1280, 1, -1])]; + tensor input_245_cast = reshape(shape = var_3557, x = attn_65_cast)[name = tensor("input_245_cast")]; + tensor var_3562 = const()[name = tensor("op_3562"), val = tensor([1, 1])]; + tensor var_3564 = const()[name = tensor("op_3564"), val = tensor([1, 1])]; + tensor var_3566_pad_type_0 = const()[name = tensor("op_3566_pad_type_0"), val = tensor("custom")]; + tensor var_3566_pad_0 = const()[name = tensor("op_3566_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1110999232)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1114276096)))]; + tensor var_3566_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_3564, groups = var_1186, pad = var_3566_pad_0, pad_type = var_3566_pad_type_0, strides = var_3562, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_245_cast)[name = tensor("op_3566_cast")]; + tensor inputs_99_cast = add(x = var_3566_cast, y = inputs_97_cast)[name = tensor("inputs_99_cast")]; + tensor var_3570 = const()[name = tensor("op_3570"), val = tensor([1])]; + tensor channels_mean_99_cast = reduce_mean(axes = var_3570, keep_dims = var_1181, x = inputs_99_cast)[name = tensor("channels_mean_99_cast")]; + tensor zero_mean_99_cast = sub(x = inputs_99_cast, y = channels_mean_99_cast)[name = tensor("zero_mean_99_cast")]; + tensor zero_mean_sq_99_cast = mul(x = zero_mean_99_cast, y = zero_mean_99_cast)[name = tensor("zero_mean_sq_99_cast")]; + tensor var_3574 = const()[name = tensor("op_3574"), val = tensor([1])]; + tensor var_3575_cast = reduce_mean(axes = var_3574, keep_dims = var_1181, x = zero_mean_sq_99_cast)[name = tensor("op_3575_cast")]; + tensor var_3576_to_fp16 = const()[name = tensor("op_3576_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3577_cast = add(x = var_3575_cast, y = var_3576_to_fp16)[name = tensor("op_3577_cast")]; + tensor denom_99_epsilon_0_to_fp16 = const()[name = tensor("denom_99_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_99_cast = rsqrt(epsilon = denom_99_epsilon_0_to_fp16, x = var_3577_cast)[name = tensor("denom_99_cast")]; + tensor out_99_cast = mul(x = zero_mean_99_cast, y = denom_99_cast)[name = tensor("out_99_cast")]; + tensor var_3581_to_fp16 = const()[name = tensor("op_3581_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1114278720)))]; + tensor var_3582_cast = add(x = out_99_cast, y = var_3581_to_fp16)[name = tensor("op_3582_cast")]; + tensor var_3584_to_fp16 = const()[name = tensor("op_3584_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1114281344)))]; + tensor hidden_states_151_cast = mul(x = var_3582_cast, y = var_3584_to_fp16)[name = tensor("hidden_states_151_cast")]; + tensor var_3591 = const()[name = tensor("op_3591"), val = tensor([1, 1])]; + tensor var_3593 = const()[name = tensor("op_3593"), val = tensor([1, 1])]; + tensor q_67_pad_type_0 = const()[name = tensor("q_67_pad_type_0"), val = tensor("custom")]; + tensor q_67_pad_0 = const()[name = tensor("q_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1114283968)))]; + tensor q_67_cast = conv(dilations = var_3593, groups = var_1186, pad = q_67_pad_0, pad_type = q_67_pad_type_0, strides = var_3591, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_151_cast)[name = tensor("q_67_cast")]; + tensor var_3597 = const()[name = tensor("op_3597"), val = tensor([1, 1])]; + tensor var_3599 = const()[name = tensor("op_3599"), val = tensor([1, 1])]; + tensor k_67_pad_type_0 = const()[name = tensor("k_67_pad_type_0"), val = tensor("custom")]; + tensor k_67_pad_0 = const()[name = tensor("k_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1117560832)))]; + tensor k_67_cast = conv(dilations = var_3599, groups = var_1186, pad = k_67_pad_0, pad_type = k_67_pad_type_0, strides = var_3597, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_67_cast")]; + tensor var_3603 = const()[name = tensor("op_3603"), val = tensor([1, 1])]; + tensor var_3605 = const()[name = tensor("op_3605"), val = tensor([1, 1])]; + tensor v_67_pad_type_0 = const()[name = tensor("v_67_pad_type_0"), val = tensor("custom")]; + tensor v_67_pad_0 = const()[name = tensor("v_67_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122803776)))]; + tensor v_67_cast = conv(dilations = var_3605, groups = var_1186, pad = v_67_pad_0, pad_type = v_67_pad_type_0, strides = var_3603, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_67_cast")]; + tensor var_3609 = const()[name = tensor("op_3609"), val = tensor([2, 20, 64, -1])]; + tensor var_3610_cast = reshape(shape = var_3609, x = q_67_cast)[name = tensor("op_3610_cast")]; + tensor var_3611 = const()[name = tensor("op_3611"), val = tensor([2, 20, 64, -1])]; + tensor var_3612_cast = reshape(shape = var_3611, x = k_67_cast)[name = tensor("op_3612_cast")]; + tensor var_3613 = const()[name = tensor("op_3613"), val = tensor([2, 20, 64, -1])]; + tensor var_3614_cast = reshape(shape = var_3613, x = v_67_cast)[name = tensor("op_3614_cast")]; + tensor attn_weights_133_transpose_x_0 = const()[name = tensor("attn_weights_133_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_133_transpose_y_0 = const()[name = tensor("attn_weights_133_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_133_cast = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = var_3610_cast, y = var_3612_cast)[name = tensor("attn_weights_133_cast")]; + tensor attn_weights_135_cast = mul(x = attn_weights_133_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_135_cast")]; + tensor var_3618_cast = softmax(axis = var_1170, x = attn_weights_135_cast)[name = tensor("op_3618_cast")]; + tensor attn_67_transpose_x_0 = const()[name = tensor("attn_67_transpose_x_0"), val = tensor(false)]; + tensor attn_67_transpose_y_0 = const()[name = tensor("attn_67_transpose_y_0"), val = tensor(true)]; + tensor attn_67_cast = matmul(transpose_x = attn_67_transpose_x_0, transpose_y = attn_67_transpose_y_0, x = var_3614_cast, y = var_3618_cast)[name = tensor("attn_67_cast")]; + tensor var_3622 = const()[name = tensor("op_3622"), val = tensor([2, 1280, 1, -1])]; + tensor input_247_cast = reshape(shape = var_3622, x = attn_67_cast)[name = tensor("input_247_cast")]; + tensor var_3627 = const()[name = tensor("op_3627"), val = tensor([1, 1])]; + tensor var_3629 = const()[name = tensor("op_3629"), val = tensor([1, 1])]; + tensor var_3631_pad_type_0 = const()[name = tensor("op_3631_pad_type_0"), val = tensor("custom")]; + tensor var_3631_pad_0 = const()[name = tensor("op_3631_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1128046720)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131323584)))]; + tensor var_3631_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_3629, groups = var_1186, pad = var_3631_pad_0, pad_type = var_3631_pad_type_0, strides = var_3627, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_247_cast)[name = tensor("op_3631_cast")]; + tensor inputs_101_cast = add(x = var_3631_cast, y = inputs_99_cast)[name = tensor("inputs_101_cast")]; + tensor var_3635 = const()[name = tensor("op_3635"), val = tensor([1])]; + tensor channels_mean_101_cast = reduce_mean(axes = var_3635, keep_dims = var_1181, x = inputs_101_cast)[name = tensor("channels_mean_101_cast")]; + tensor zero_mean_101_cast = sub(x = inputs_101_cast, y = channels_mean_101_cast)[name = tensor("zero_mean_101_cast")]; + tensor zero_mean_sq_101_cast = mul(x = zero_mean_101_cast, y = zero_mean_101_cast)[name = tensor("zero_mean_sq_101_cast")]; + tensor var_3639 = const()[name = tensor("op_3639"), val = tensor([1])]; + tensor var_3640_cast = reduce_mean(axes = var_3639, keep_dims = var_1181, x = zero_mean_sq_101_cast)[name = tensor("op_3640_cast")]; + tensor var_3641_to_fp16 = const()[name = tensor("op_3641_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3642_cast = add(x = var_3640_cast, y = var_3641_to_fp16)[name = tensor("op_3642_cast")]; + tensor denom_101_epsilon_0_to_fp16 = const()[name = tensor("denom_101_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_101_cast = rsqrt(epsilon = denom_101_epsilon_0_to_fp16, x = var_3642_cast)[name = tensor("denom_101_cast")]; + tensor out_101_cast = mul(x = zero_mean_101_cast, y = denom_101_cast)[name = tensor("out_101_cast")]; + tensor var_3646_to_fp16 = const()[name = tensor("op_3646_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131326208)))]; + tensor var_3647_cast = add(x = out_101_cast, y = var_3646_to_fp16)[name = tensor("op_3647_cast")]; + tensor var_3649_to_fp16 = const()[name = tensor("op_3649_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131328832)))]; + tensor input_249_cast = mul(x = var_3647_cast, y = var_3649_to_fp16)[name = tensor("input_249_cast")]; + tensor var_3657 = const()[name = tensor("op_3657"), val = tensor([1, 1])]; + tensor var_3659 = const()[name = tensor("op_3659"), val = tensor([1, 1])]; + tensor var_3661_pad_type_0 = const()[name = tensor("op_3661_pad_type_0"), val = tensor("custom")]; + tensor var_3661_pad_0 = const()[name = tensor("op_3661_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131331456)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1157545920)))]; + tensor var_3661_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_3659, groups = var_1186, pad = var_3661_pad_0, pad_type = var_3661_pad_type_0, strides = var_3657, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_249_cast)[name = tensor("op_3661_cast")]; + tensor var_3662_split_sizes_0 = const()[name = tensor("op_3662_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3662_axis_0 = const()[name = tensor("op_3662_axis_0"), val = tensor(1)]; + tensor var_3662_cast_0, tensor var_3662_cast_1 = split(axis = var_3662_axis_0, split_sizes = var_3662_split_sizes_0, x = var_3661_cast)[name = tensor("op_3662_cast")]; + tensor var_3664_mode_0 = const()[name = tensor("op_3664_mode_0"), val = tensor("EXACT")]; + tensor var_3664_cast = gelu(mode = var_3664_mode_0, x = var_3662_cast_1)[name = tensor("op_3664_cast")]; + tensor input_251_cast = mul(x = var_3662_cast_0, y = var_3664_cast)[name = tensor("input_251_cast")]; + tensor var_3668 = const()[name = tensor("op_3668"), val = tensor([1, 1])]; + tensor var_3670 = const()[name = tensor("op_3670"), val = tensor([1, 1])]; + tensor var_3672_pad_type_0 = const()[name = tensor("op_3672_pad_type_0"), val = tensor("custom")]; + tensor var_3672_pad_0 = const()[name = tensor("op_3672_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1157566464)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170673728)))]; + tensor var_3672_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_3670, groups = var_1186, pad = var_3672_pad_0, pad_type = var_3672_pad_type_0, strides = var_3668, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_251_cast)[name = tensor("op_3672_cast")]; + tensor inputs_103_cast = add(x = var_3672_cast, y = inputs_101_cast)[name = tensor("inputs_103_cast")]; + tensor var_3682 = const()[name = tensor("op_3682"), val = tensor([1])]; + tensor channels_mean_103_cast = reduce_mean(axes = var_3682, keep_dims = var_1181, x = inputs_103_cast)[name = tensor("channels_mean_103_cast")]; + tensor zero_mean_103_cast = sub(x = inputs_103_cast, y = channels_mean_103_cast)[name = tensor("zero_mean_103_cast")]; + tensor zero_mean_sq_103_cast = mul(x = zero_mean_103_cast, y = zero_mean_103_cast)[name = tensor("zero_mean_sq_103_cast")]; + tensor var_3686 = const()[name = tensor("op_3686"), val = tensor([1])]; + tensor var_3687_cast = reduce_mean(axes = var_3686, keep_dims = var_1181, x = zero_mean_sq_103_cast)[name = tensor("op_3687_cast")]; + tensor var_3688_to_fp16 = const()[name = tensor("op_3688_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3689_cast = add(x = var_3687_cast, y = var_3688_to_fp16)[name = tensor("op_3689_cast")]; + tensor denom_103_epsilon_0_to_fp16 = const()[name = tensor("denom_103_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_103_cast = rsqrt(epsilon = denom_103_epsilon_0_to_fp16, x = var_3689_cast)[name = tensor("denom_103_cast")]; + tensor out_103_cast = mul(x = zero_mean_103_cast, y = denom_103_cast)[name = tensor("out_103_cast")]; + tensor var_3693_to_fp16 = const()[name = tensor("op_3693_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170676352)))]; + tensor var_3694_cast = add(x = out_103_cast, y = var_3693_to_fp16)[name = tensor("op_3694_cast")]; + tensor var_3696_to_fp16 = const()[name = tensor("op_3696_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170678976)))]; + tensor hidden_states_155_cast = mul(x = var_3694_cast, y = var_3696_to_fp16)[name = tensor("hidden_states_155_cast")]; + tensor var_3703 = const()[name = tensor("op_3703"), val = tensor([1, 1])]; + tensor var_3705 = const()[name = tensor("op_3705"), val = tensor([1, 1])]; + tensor q_69_pad_type_0 = const()[name = tensor("q_69_pad_type_0"), val = tensor("custom")]; + tensor q_69_pad_0 = const()[name = tensor("q_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170681600)))]; + tensor q_69_cast = conv(dilations = var_3705, groups = var_1186, pad = q_69_pad_0, pad_type = q_69_pad_type_0, strides = var_3703, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_155_cast)[name = tensor("q_69_cast")]; + tensor var_3709 = const()[name = tensor("op_3709"), val = tensor([1, 1])]; + tensor var_3711 = const()[name = tensor("op_3711"), val = tensor([1, 1])]; + tensor k_69_pad_type_0 = const()[name = tensor("k_69_pad_type_0"), val = tensor("custom")]; + tensor k_69_pad_0 = const()[name = tensor("k_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1173958464)))]; + tensor k_69_cast = conv(dilations = var_3711, groups = var_1186, pad = k_69_pad_0, pad_type = k_69_pad_type_0, strides = var_3709, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_155_cast)[name = tensor("k_69_cast")]; + tensor var_3715 = const()[name = tensor("op_3715"), val = tensor([1, 1])]; + tensor var_3717 = const()[name = tensor("op_3717"), val = tensor([1, 1])]; + tensor v_69_pad_type_0 = const()[name = tensor("v_69_pad_type_0"), val = tensor("custom")]; + tensor v_69_pad_0 = const()[name = tensor("v_69_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1177235328)))]; + tensor v_69_cast = conv(dilations = var_3717, groups = var_1186, pad = v_69_pad_0, pad_type = v_69_pad_type_0, strides = var_3715, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_155_cast)[name = tensor("v_69_cast")]; + tensor var_3721 = const()[name = tensor("op_3721"), val = tensor([2, 20, 64, -1])]; + tensor var_3722_cast = reshape(shape = var_3721, x = q_69_cast)[name = tensor("op_3722_cast")]; + tensor var_3723 = const()[name = tensor("op_3723"), val = tensor([2, 20, 64, -1])]; + tensor var_3724_cast = reshape(shape = var_3723, x = k_69_cast)[name = tensor("op_3724_cast")]; + tensor var_3725 = const()[name = tensor("op_3725"), val = tensor([2, 20, 64, -1])]; + tensor var_3726_cast = reshape(shape = var_3725, x = v_69_cast)[name = tensor("op_3726_cast")]; + tensor attn_weights_137_transpose_x_0 = const()[name = tensor("attn_weights_137_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_137_transpose_y_0 = const()[name = tensor("attn_weights_137_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_137_cast = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3722_cast, y = var_3724_cast)[name = tensor("attn_weights_137_cast")]; + tensor attn_weights_139_cast = mul(x = attn_weights_137_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_139_cast")]; + tensor var_3730_cast = softmax(axis = var_1170, x = attn_weights_139_cast)[name = tensor("op_3730_cast")]; + tensor attn_69_transpose_x_0 = const()[name = tensor("attn_69_transpose_x_0"), val = tensor(false)]; + tensor attn_69_transpose_y_0 = const()[name = tensor("attn_69_transpose_y_0"), val = tensor(true)]; + tensor attn_69_cast = matmul(transpose_x = attn_69_transpose_x_0, transpose_y = attn_69_transpose_y_0, x = var_3726_cast, y = var_3730_cast)[name = tensor("attn_69_cast")]; + tensor var_3734 = const()[name = tensor("op_3734"), val = tensor([2, 1280, 1, -1])]; + tensor input_253_cast = reshape(shape = var_3734, x = attn_69_cast)[name = tensor("input_253_cast")]; + tensor var_3739 = const()[name = tensor("op_3739"), val = tensor([1, 1])]; + tensor var_3741 = const()[name = tensor("op_3741"), val = tensor([1, 1])]; + tensor var_3743_pad_type_0 = const()[name = tensor("op_3743_pad_type_0"), val = tensor("custom")]; + tensor var_3743_pad_0 = const()[name = tensor("op_3743_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180512192)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183789056)))]; + tensor var_3743_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_3741, groups = var_1186, pad = var_3743_pad_0, pad_type = var_3743_pad_type_0, strides = var_3739, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_253_cast)[name = tensor("op_3743_cast")]; + tensor inputs_105_cast = add(x = var_3743_cast, y = inputs_103_cast)[name = tensor("inputs_105_cast")]; + tensor var_3747 = const()[name = tensor("op_3747"), val = tensor([1])]; + tensor channels_mean_105_cast = reduce_mean(axes = var_3747, keep_dims = var_1181, x = inputs_105_cast)[name = tensor("channels_mean_105_cast")]; + tensor zero_mean_105_cast = sub(x = inputs_105_cast, y = channels_mean_105_cast)[name = tensor("zero_mean_105_cast")]; + tensor zero_mean_sq_105_cast = mul(x = zero_mean_105_cast, y = zero_mean_105_cast)[name = tensor("zero_mean_sq_105_cast")]; + tensor var_3751 = const()[name = tensor("op_3751"), val = tensor([1])]; + tensor var_3752_cast = reduce_mean(axes = var_3751, keep_dims = var_1181, x = zero_mean_sq_105_cast)[name = tensor("op_3752_cast")]; + tensor var_3753_to_fp16 = const()[name = tensor("op_3753_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3754_cast = add(x = var_3752_cast, y = var_3753_to_fp16)[name = tensor("op_3754_cast")]; + tensor denom_105_epsilon_0_to_fp16 = const()[name = tensor("denom_105_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_105_cast = rsqrt(epsilon = denom_105_epsilon_0_to_fp16, x = var_3754_cast)[name = tensor("denom_105_cast")]; + tensor out_105_cast = mul(x = zero_mean_105_cast, y = denom_105_cast)[name = tensor("out_105_cast")]; + tensor var_3758_to_fp16 = const()[name = tensor("op_3758_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183791680)))]; + tensor var_3759_cast = add(x = out_105_cast, y = var_3758_to_fp16)[name = tensor("op_3759_cast")]; + tensor var_3761_to_fp16 = const()[name = tensor("op_3761_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183794304)))]; + tensor hidden_states_157_cast = mul(x = var_3759_cast, y = var_3761_to_fp16)[name = tensor("hidden_states_157_cast")]; + tensor var_3768 = const()[name = tensor("op_3768"), val = tensor([1, 1])]; + tensor var_3770 = const()[name = tensor("op_3770"), val = tensor([1, 1])]; + tensor q_71_pad_type_0 = const()[name = tensor("q_71_pad_type_0"), val = tensor("custom")]; + tensor q_71_pad_0 = const()[name = tensor("q_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183796928)))]; + tensor q_71_cast = conv(dilations = var_3770, groups = var_1186, pad = q_71_pad_0, pad_type = q_71_pad_type_0, strides = var_3768, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_157_cast)[name = tensor("q_71_cast")]; + tensor var_3774 = const()[name = tensor("op_3774"), val = tensor([1, 1])]; + tensor var_3776 = const()[name = tensor("op_3776"), val = tensor([1, 1])]; + tensor k_71_pad_type_0 = const()[name = tensor("k_71_pad_type_0"), val = tensor("custom")]; + tensor k_71_pad_0 = const()[name = tensor("k_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1187073792)))]; + tensor k_71_cast = conv(dilations = var_3776, groups = var_1186, pad = k_71_pad_0, pad_type = k_71_pad_type_0, strides = var_3774, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_71_cast")]; + tensor var_3780 = const()[name = tensor("op_3780"), val = tensor([1, 1])]; + tensor var_3782 = const()[name = tensor("op_3782"), val = tensor([1, 1])]; + tensor v_71_pad_type_0 = const()[name = tensor("v_71_pad_type_0"), val = tensor("custom")]; + tensor v_71_pad_0 = const()[name = tensor("v_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1192316736)))]; + tensor v_71_cast = conv(dilations = var_3782, groups = var_1186, pad = v_71_pad_0, pad_type = v_71_pad_type_0, strides = var_3780, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_71_cast")]; + tensor var_3786 = const()[name = tensor("op_3786"), val = tensor([2, 20, 64, -1])]; + tensor var_3787_cast = reshape(shape = var_3786, x = q_71_cast)[name = tensor("op_3787_cast")]; + tensor var_3788 = const()[name = tensor("op_3788"), val = tensor([2, 20, 64, -1])]; + tensor var_3789_cast = reshape(shape = var_3788, x = k_71_cast)[name = tensor("op_3789_cast")]; + tensor var_3790 = const()[name = tensor("op_3790"), val = tensor([2, 20, 64, -1])]; + tensor var_3791_cast = reshape(shape = var_3790, x = v_71_cast)[name = tensor("op_3791_cast")]; + tensor attn_weights_141_transpose_x_0 = const()[name = tensor("attn_weights_141_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_141_transpose_y_0 = const()[name = tensor("attn_weights_141_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_141_cast = matmul(transpose_x = attn_weights_141_transpose_x_0, transpose_y = attn_weights_141_transpose_y_0, x = var_3787_cast, y = var_3789_cast)[name = tensor("attn_weights_141_cast")]; + tensor attn_weights_143_cast = mul(x = attn_weights_141_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_143_cast")]; + tensor var_3795_cast = softmax(axis = var_1170, x = attn_weights_143_cast)[name = tensor("op_3795_cast")]; + tensor attn_71_transpose_x_0 = const()[name = tensor("attn_71_transpose_x_0"), val = tensor(false)]; + tensor attn_71_transpose_y_0 = const()[name = tensor("attn_71_transpose_y_0"), val = tensor(true)]; + tensor attn_71_cast = matmul(transpose_x = attn_71_transpose_x_0, transpose_y = attn_71_transpose_y_0, x = var_3791_cast, y = var_3795_cast)[name = tensor("attn_71_cast")]; + tensor var_3799 = const()[name = tensor("op_3799"), val = tensor([2, 1280, 1, -1])]; + tensor input_255_cast = reshape(shape = var_3799, x = attn_71_cast)[name = tensor("input_255_cast")]; + tensor var_3804 = const()[name = tensor("op_3804"), val = tensor([1, 1])]; + tensor var_3806 = const()[name = tensor("op_3806"), val = tensor([1, 1])]; + tensor var_3808_pad_type_0 = const()[name = tensor("op_3808_pad_type_0"), val = tensor("custom")]; + tensor var_3808_pad_0 = const()[name = tensor("op_3808_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1197559680)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1200836544)))]; + tensor var_3808_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_3806, groups = var_1186, pad = var_3808_pad_0, pad_type = var_3808_pad_type_0, strides = var_3804, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_255_cast)[name = tensor("op_3808_cast")]; + tensor inputs_107_cast = add(x = var_3808_cast, y = inputs_105_cast)[name = tensor("inputs_107_cast")]; + tensor var_3812 = const()[name = tensor("op_3812"), val = tensor([1])]; + tensor channels_mean_107_cast = reduce_mean(axes = var_3812, keep_dims = var_1181, x = inputs_107_cast)[name = tensor("channels_mean_107_cast")]; + tensor zero_mean_107_cast = sub(x = inputs_107_cast, y = channels_mean_107_cast)[name = tensor("zero_mean_107_cast")]; + tensor zero_mean_sq_107_cast = mul(x = zero_mean_107_cast, y = zero_mean_107_cast)[name = tensor("zero_mean_sq_107_cast")]; + tensor var_3816 = const()[name = tensor("op_3816"), val = tensor([1])]; + tensor var_3817_cast = reduce_mean(axes = var_3816, keep_dims = var_1181, x = zero_mean_sq_107_cast)[name = tensor("op_3817_cast")]; + tensor var_3818_to_fp16 = const()[name = tensor("op_3818_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3819_cast = add(x = var_3817_cast, y = var_3818_to_fp16)[name = tensor("op_3819_cast")]; + tensor denom_107_epsilon_0_to_fp16 = const()[name = tensor("denom_107_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_107_cast = rsqrt(epsilon = denom_107_epsilon_0_to_fp16, x = var_3819_cast)[name = tensor("denom_107_cast")]; + tensor out_107_cast = mul(x = zero_mean_107_cast, y = denom_107_cast)[name = tensor("out_107_cast")]; + tensor var_3823_to_fp16 = const()[name = tensor("op_3823_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1200839168)))]; + tensor var_3824_cast = add(x = out_107_cast, y = var_3823_to_fp16)[name = tensor("op_3824_cast")]; + tensor var_3826_to_fp16 = const()[name = tensor("op_3826_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1200841792)))]; + tensor input_257_cast = mul(x = var_3824_cast, y = var_3826_to_fp16)[name = tensor("input_257_cast")]; + tensor var_3834 = const()[name = tensor("op_3834"), val = tensor([1, 1])]; + tensor var_3836 = const()[name = tensor("op_3836"), val = tensor([1, 1])]; + tensor var_3838_pad_type_0 = const()[name = tensor("op_3838_pad_type_0"), val = tensor("custom")]; + tensor var_3838_pad_0 = const()[name = tensor("op_3838_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1200844416)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227058880)))]; + tensor var_3838_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_3836, groups = var_1186, pad = var_3838_pad_0, pad_type = var_3838_pad_type_0, strides = var_3834, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_257_cast)[name = tensor("op_3838_cast")]; + tensor var_3839_split_sizes_0 = const()[name = tensor("op_3839_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3839_axis_0 = const()[name = tensor("op_3839_axis_0"), val = tensor(1)]; + tensor var_3839_cast_0, tensor var_3839_cast_1 = split(axis = var_3839_axis_0, split_sizes = var_3839_split_sizes_0, x = var_3838_cast)[name = tensor("op_3839_cast")]; + tensor var_3841_mode_0 = const()[name = tensor("op_3841_mode_0"), val = tensor("EXACT")]; + tensor var_3841_cast = gelu(mode = var_3841_mode_0, x = var_3839_cast_1)[name = tensor("op_3841_cast")]; + tensor input_259_cast = mul(x = var_3839_cast_0, y = var_3841_cast)[name = tensor("input_259_cast")]; + tensor var_3845 = const()[name = tensor("op_3845"), val = tensor([1, 1])]; + tensor var_3847 = const()[name = tensor("op_3847"), val = tensor([1, 1])]; + tensor var_3849_pad_type_0 = const()[name = tensor("op_3849_pad_type_0"), val = tensor("custom")]; + tensor var_3849_pad_0 = const()[name = tensor("op_3849_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227079424)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1240186688)))]; + tensor var_3849_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_3847, groups = var_1186, pad = var_3849_pad_0, pad_type = var_3849_pad_type_0, strides = var_3845, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_259_cast)[name = tensor("op_3849_cast")]; + tensor inputs_109_cast = add(x = var_3849_cast, y = inputs_107_cast)[name = tensor("inputs_109_cast")]; + tensor var_3859 = const()[name = tensor("op_3859"), val = tensor([1])]; + tensor channels_mean_109_cast = reduce_mean(axes = var_3859, keep_dims = var_1181, x = inputs_109_cast)[name = tensor("channels_mean_109_cast")]; + tensor zero_mean_109_cast = sub(x = inputs_109_cast, y = channels_mean_109_cast)[name = tensor("zero_mean_109_cast")]; + tensor zero_mean_sq_109_cast = mul(x = zero_mean_109_cast, y = zero_mean_109_cast)[name = tensor("zero_mean_sq_109_cast")]; + tensor var_3863 = const()[name = tensor("op_3863"), val = tensor([1])]; + tensor var_3864_cast = reduce_mean(axes = var_3863, keep_dims = var_1181, x = zero_mean_sq_109_cast)[name = tensor("op_3864_cast")]; + tensor var_3865_to_fp16 = const()[name = tensor("op_3865_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3866_cast = add(x = var_3864_cast, y = var_3865_to_fp16)[name = tensor("op_3866_cast")]; + tensor denom_109_epsilon_0_to_fp16 = const()[name = tensor("denom_109_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_109_cast = rsqrt(epsilon = denom_109_epsilon_0_to_fp16, x = var_3866_cast)[name = tensor("denom_109_cast")]; + tensor out_109_cast = mul(x = zero_mean_109_cast, y = denom_109_cast)[name = tensor("out_109_cast")]; + tensor var_3870_to_fp16 = const()[name = tensor("op_3870_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1240189312)))]; + tensor var_3871_cast = add(x = out_109_cast, y = var_3870_to_fp16)[name = tensor("op_3871_cast")]; + tensor var_3873_to_fp16 = const()[name = tensor("op_3873_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1240191936)))]; + tensor hidden_states_161_cast = mul(x = var_3871_cast, y = var_3873_to_fp16)[name = tensor("hidden_states_161_cast")]; + tensor var_3880 = const()[name = tensor("op_3880"), val = tensor([1, 1])]; + tensor var_3882 = const()[name = tensor("op_3882"), val = tensor([1, 1])]; + tensor q_73_pad_type_0 = const()[name = tensor("q_73_pad_type_0"), val = tensor("custom")]; + tensor q_73_pad_0 = const()[name = tensor("q_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1240194560)))]; + tensor q_73_cast = conv(dilations = var_3882, groups = var_1186, pad = q_73_pad_0, pad_type = q_73_pad_type_0, strides = var_3880, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_161_cast)[name = tensor("q_73_cast")]; + tensor var_3886 = const()[name = tensor("op_3886"), val = tensor([1, 1])]; + tensor var_3888 = const()[name = tensor("op_3888"), val = tensor([1, 1])]; + tensor k_73_pad_type_0 = const()[name = tensor("k_73_pad_type_0"), val = tensor("custom")]; + tensor k_73_pad_0 = const()[name = tensor("k_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1243471424)))]; + tensor k_73_cast = conv(dilations = var_3888, groups = var_1186, pad = k_73_pad_0, pad_type = k_73_pad_type_0, strides = var_3886, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_161_cast)[name = tensor("k_73_cast")]; + tensor var_3892 = const()[name = tensor("op_3892"), val = tensor([1, 1])]; + tensor var_3894 = const()[name = tensor("op_3894"), val = tensor([1, 1])]; + tensor v_73_pad_type_0 = const()[name = tensor("v_73_pad_type_0"), val = tensor("custom")]; + tensor v_73_pad_0 = const()[name = tensor("v_73_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1246748288)))]; + tensor v_73_cast = conv(dilations = var_3894, groups = var_1186, pad = v_73_pad_0, pad_type = v_73_pad_type_0, strides = var_3892, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_161_cast)[name = tensor("v_73_cast")]; + tensor var_3898 = const()[name = tensor("op_3898"), val = tensor([2, 20, 64, -1])]; + tensor var_3899_cast = reshape(shape = var_3898, x = q_73_cast)[name = tensor("op_3899_cast")]; + tensor var_3900 = const()[name = tensor("op_3900"), val = tensor([2, 20, 64, -1])]; + tensor var_3901_cast = reshape(shape = var_3900, x = k_73_cast)[name = tensor("op_3901_cast")]; + tensor var_3902 = const()[name = tensor("op_3902"), val = tensor([2, 20, 64, -1])]; + tensor var_3903_cast = reshape(shape = var_3902, x = v_73_cast)[name = tensor("op_3903_cast")]; + tensor attn_weights_145_transpose_x_0 = const()[name = tensor("attn_weights_145_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_145_transpose_y_0 = const()[name = tensor("attn_weights_145_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_145_cast = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3899_cast, y = var_3901_cast)[name = tensor("attn_weights_145_cast")]; + tensor attn_weights_147_cast = mul(x = attn_weights_145_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_147_cast")]; + tensor var_3907_cast = softmax(axis = var_1170, x = attn_weights_147_cast)[name = tensor("op_3907_cast")]; + tensor attn_73_transpose_x_0 = const()[name = tensor("attn_73_transpose_x_0"), val = tensor(false)]; + tensor attn_73_transpose_y_0 = const()[name = tensor("attn_73_transpose_y_0"), val = tensor(true)]; + tensor attn_73_cast = matmul(transpose_x = attn_73_transpose_x_0, transpose_y = attn_73_transpose_y_0, x = var_3903_cast, y = var_3907_cast)[name = tensor("attn_73_cast")]; + tensor var_3911 = const()[name = tensor("op_3911"), val = tensor([2, 1280, 1, -1])]; + tensor input_261_cast = reshape(shape = var_3911, x = attn_73_cast)[name = tensor("input_261_cast")]; + tensor var_3916 = const()[name = tensor("op_3916"), val = tensor([1, 1])]; + tensor var_3918 = const()[name = tensor("op_3918"), val = tensor([1, 1])]; + tensor var_3920_pad_type_0 = const()[name = tensor("op_3920_pad_type_0"), val = tensor("custom")]; + tensor var_3920_pad_0 = const()[name = tensor("op_3920_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1250025152)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1253302016)))]; + tensor var_3920_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_3918, groups = var_1186, pad = var_3920_pad_0, pad_type = var_3920_pad_type_0, strides = var_3916, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_261_cast)[name = tensor("op_3920_cast")]; + tensor inputs_111_cast = add(x = var_3920_cast, y = inputs_109_cast)[name = tensor("inputs_111_cast")]; + tensor var_3924 = const()[name = tensor("op_3924"), val = tensor([1])]; + tensor channels_mean_111_cast = reduce_mean(axes = var_3924, keep_dims = var_1181, x = inputs_111_cast)[name = tensor("channels_mean_111_cast")]; + tensor zero_mean_111_cast = sub(x = inputs_111_cast, y = channels_mean_111_cast)[name = tensor("zero_mean_111_cast")]; + tensor zero_mean_sq_111_cast = mul(x = zero_mean_111_cast, y = zero_mean_111_cast)[name = tensor("zero_mean_sq_111_cast")]; + tensor var_3928 = const()[name = tensor("op_3928"), val = tensor([1])]; + tensor var_3929_cast = reduce_mean(axes = var_3928, keep_dims = var_1181, x = zero_mean_sq_111_cast)[name = tensor("op_3929_cast")]; + tensor var_3930_to_fp16 = const()[name = tensor("op_3930_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3931_cast = add(x = var_3929_cast, y = var_3930_to_fp16)[name = tensor("op_3931_cast")]; + tensor denom_111_epsilon_0_to_fp16 = const()[name = tensor("denom_111_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_111_cast = rsqrt(epsilon = denom_111_epsilon_0_to_fp16, x = var_3931_cast)[name = tensor("denom_111_cast")]; + tensor out_111_cast = mul(x = zero_mean_111_cast, y = denom_111_cast)[name = tensor("out_111_cast")]; + tensor var_3935_to_fp16 = const()[name = tensor("op_3935_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1253304640)))]; + tensor var_3936_cast = add(x = out_111_cast, y = var_3935_to_fp16)[name = tensor("op_3936_cast")]; + tensor var_3938_to_fp16 = const()[name = tensor("op_3938_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1253307264)))]; + tensor hidden_states_163_cast = mul(x = var_3936_cast, y = var_3938_to_fp16)[name = tensor("hidden_states_163_cast")]; + tensor var_3945 = const()[name = tensor("op_3945"), val = tensor([1, 1])]; + tensor var_3947 = const()[name = tensor("op_3947"), val = tensor([1, 1])]; + tensor q_75_pad_type_0 = const()[name = tensor("q_75_pad_type_0"), val = tensor("custom")]; + tensor q_75_pad_0 = const()[name = tensor("q_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1253309888)))]; + tensor q_75_cast = conv(dilations = var_3947, groups = var_1186, pad = q_75_pad_0, pad_type = q_75_pad_type_0, strides = var_3945, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_163_cast)[name = tensor("q_75_cast")]; + tensor var_3951 = const()[name = tensor("op_3951"), val = tensor([1, 1])]; + tensor var_3953 = const()[name = tensor("op_3953"), val = tensor([1, 1])]; + tensor k_75_pad_type_0 = const()[name = tensor("k_75_pad_type_0"), val = tensor("custom")]; + tensor k_75_pad_0 = const()[name = tensor("k_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1256586752)))]; + tensor k_75_cast = conv(dilations = var_3953, groups = var_1186, pad = k_75_pad_0, pad_type = k_75_pad_type_0, strides = var_3951, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_75_cast")]; + tensor var_3957 = const()[name = tensor("op_3957"), val = tensor([1, 1])]; + tensor var_3959 = const()[name = tensor("op_3959"), val = tensor([1, 1])]; + tensor v_75_pad_type_0 = const()[name = tensor("v_75_pad_type_0"), val = tensor("custom")]; + tensor v_75_pad_0 = const()[name = tensor("v_75_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1261829696)))]; + tensor v_75_cast = conv(dilations = var_3959, groups = var_1186, pad = v_75_pad_0, pad_type = v_75_pad_type_0, strides = var_3957, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_75_cast")]; + tensor var_3963 = const()[name = tensor("op_3963"), val = tensor([2, 20, 64, -1])]; + tensor var_3964_cast = reshape(shape = var_3963, x = q_75_cast)[name = tensor("op_3964_cast")]; + tensor var_3965 = const()[name = tensor("op_3965"), val = tensor([2, 20, 64, -1])]; + tensor var_3966_cast = reshape(shape = var_3965, x = k_75_cast)[name = tensor("op_3966_cast")]; + tensor var_3967 = const()[name = tensor("op_3967"), val = tensor([2, 20, 64, -1])]; + tensor var_3968_cast = reshape(shape = var_3967, x = v_75_cast)[name = tensor("op_3968_cast")]; + tensor attn_weights_149_transpose_x_0 = const()[name = tensor("attn_weights_149_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_149_transpose_y_0 = const()[name = tensor("attn_weights_149_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_149_cast = matmul(transpose_x = attn_weights_149_transpose_x_0, transpose_y = attn_weights_149_transpose_y_0, x = var_3964_cast, y = var_3966_cast)[name = tensor("attn_weights_149_cast")]; + tensor attn_weights_151_cast = mul(x = attn_weights_149_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_151_cast")]; + tensor var_3972_cast = softmax(axis = var_1170, x = attn_weights_151_cast)[name = tensor("op_3972_cast")]; + tensor attn_75_transpose_x_0 = const()[name = tensor("attn_75_transpose_x_0"), val = tensor(false)]; + tensor attn_75_transpose_y_0 = const()[name = tensor("attn_75_transpose_y_0"), val = tensor(true)]; + tensor attn_75_cast = matmul(transpose_x = attn_75_transpose_x_0, transpose_y = attn_75_transpose_y_0, x = var_3968_cast, y = var_3972_cast)[name = tensor("attn_75_cast")]; + tensor var_3976 = const()[name = tensor("op_3976"), val = tensor([2, 1280, 1, -1])]; + tensor input_263_cast = reshape(shape = var_3976, x = attn_75_cast)[name = tensor("input_263_cast")]; + tensor var_3981 = const()[name = tensor("op_3981"), val = tensor([1, 1])]; + tensor var_3983 = const()[name = tensor("op_3983"), val = tensor([1, 1])]; + tensor var_3985_pad_type_0 = const()[name = tensor("op_3985_pad_type_0"), val = tensor("custom")]; + tensor var_3985_pad_0 = const()[name = tensor("op_3985_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1267072640)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270349504)))]; + tensor var_3985_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_3983, groups = var_1186, pad = var_3985_pad_0, pad_type = var_3985_pad_type_0, strides = var_3981, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_263_cast)[name = tensor("op_3985_cast")]; + tensor inputs_113_cast = add(x = var_3985_cast, y = inputs_111_cast)[name = tensor("inputs_113_cast")]; + tensor var_3989 = const()[name = tensor("op_3989"), val = tensor([1])]; + tensor channels_mean_113_cast = reduce_mean(axes = var_3989, keep_dims = var_1181, x = inputs_113_cast)[name = tensor("channels_mean_113_cast")]; + tensor zero_mean_113_cast = sub(x = inputs_113_cast, y = channels_mean_113_cast)[name = tensor("zero_mean_113_cast")]; + tensor zero_mean_sq_113_cast = mul(x = zero_mean_113_cast, y = zero_mean_113_cast)[name = tensor("zero_mean_sq_113_cast")]; + tensor var_3993 = const()[name = tensor("op_3993"), val = tensor([1])]; + tensor var_3994_cast = reduce_mean(axes = var_3993, keep_dims = var_1181, x = zero_mean_sq_113_cast)[name = tensor("op_3994_cast")]; + tensor var_3995_to_fp16 = const()[name = tensor("op_3995_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3996_cast = add(x = var_3994_cast, y = var_3995_to_fp16)[name = tensor("op_3996_cast")]; + tensor denom_113_epsilon_0_to_fp16 = const()[name = tensor("denom_113_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_113_cast = rsqrt(epsilon = denom_113_epsilon_0_to_fp16, x = var_3996_cast)[name = tensor("denom_113_cast")]; + tensor out_113_cast = mul(x = zero_mean_113_cast, y = denom_113_cast)[name = tensor("out_113_cast")]; + tensor var_4000_to_fp16 = const()[name = tensor("op_4000_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270352128)))]; + tensor var_4001_cast = add(x = out_113_cast, y = var_4000_to_fp16)[name = tensor("op_4001_cast")]; + tensor var_4003_to_fp16 = const()[name = tensor("op_4003_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270354752)))]; + tensor input_265_cast = mul(x = var_4001_cast, y = var_4003_to_fp16)[name = tensor("input_265_cast")]; + tensor var_4011 = const()[name = tensor("op_4011"), val = tensor([1, 1])]; + tensor var_4013 = const()[name = tensor("op_4013"), val = tensor([1, 1])]; + tensor var_4015_pad_type_0 = const()[name = tensor("op_4015_pad_type_0"), val = tensor("custom")]; + tensor var_4015_pad_0 = const()[name = tensor("op_4015_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270357376)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1296571840)))]; + tensor var_4015_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_4013, groups = var_1186, pad = var_4015_pad_0, pad_type = var_4015_pad_type_0, strides = var_4011, weight = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_265_cast)[name = tensor("op_4015_cast")]; + tensor var_4016_split_sizes_0 = const()[name = tensor("op_4016_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4016_axis_0 = const()[name = tensor("op_4016_axis_0"), val = tensor(1)]; + tensor var_4016_cast_0, tensor var_4016_cast_1 = split(axis = var_4016_axis_0, split_sizes = var_4016_split_sizes_0, x = var_4015_cast)[name = tensor("op_4016_cast")]; + tensor var_4018_mode_0 = const()[name = tensor("op_4018_mode_0"), val = tensor("EXACT")]; + tensor var_4018_cast = gelu(mode = var_4018_mode_0, x = var_4016_cast_1)[name = tensor("op_4018_cast")]; + tensor input_267_cast = mul(x = var_4016_cast_0, y = var_4018_cast)[name = tensor("input_267_cast")]; + tensor var_4022 = const()[name = tensor("op_4022"), val = tensor([1, 1])]; + tensor var_4024 = const()[name = tensor("op_4024"), val = tensor([1, 1])]; + tensor var_4026_pad_type_0 = const()[name = tensor("op_4026_pad_type_0"), val = tensor("custom")]; + tensor var_4026_pad_0 = const()[name = tensor("op_4026_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1296592384)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1309699648)))]; + tensor var_4026_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_4024, groups = var_1186, pad = var_4026_pad_0, pad_type = var_4026_pad_type_0, strides = var_4022, weight = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_267_cast)[name = tensor("op_4026_cast")]; + tensor inputs_115_cast = add(x = var_4026_cast, y = inputs_113_cast)[name = tensor("inputs_115_cast")]; + tensor var_4036 = const()[name = tensor("op_4036"), val = tensor([1])]; + tensor channels_mean_115_cast = reduce_mean(axes = var_4036, keep_dims = var_1181, x = inputs_115_cast)[name = tensor("channels_mean_115_cast")]; + tensor zero_mean_115_cast = sub(x = inputs_115_cast, y = channels_mean_115_cast)[name = tensor("zero_mean_115_cast")]; + tensor zero_mean_sq_115_cast = mul(x = zero_mean_115_cast, y = zero_mean_115_cast)[name = tensor("zero_mean_sq_115_cast")]; + tensor var_4040 = const()[name = tensor("op_4040"), val = tensor([1])]; + tensor var_4041_cast = reduce_mean(axes = var_4040, keep_dims = var_1181, x = zero_mean_sq_115_cast)[name = tensor("op_4041_cast")]; + tensor var_4042_to_fp16 = const()[name = tensor("op_4042_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4043_cast = add(x = var_4041_cast, y = var_4042_to_fp16)[name = tensor("op_4043_cast")]; + tensor denom_115_epsilon_0_to_fp16 = const()[name = tensor("denom_115_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_115_cast = rsqrt(epsilon = denom_115_epsilon_0_to_fp16, x = var_4043_cast)[name = tensor("denom_115_cast")]; + tensor out_115_cast = mul(x = zero_mean_115_cast, y = denom_115_cast)[name = tensor("out_115_cast")]; + tensor var_4047_to_fp16 = const()[name = tensor("op_4047_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1309702272)))]; + tensor var_4048_cast = add(x = out_115_cast, y = var_4047_to_fp16)[name = tensor("op_4048_cast")]; + tensor var_4050_to_fp16 = const()[name = tensor("op_4050_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1309704896)))]; + tensor hidden_states_167_cast = mul(x = var_4048_cast, y = var_4050_to_fp16)[name = tensor("hidden_states_167_cast")]; + tensor var_4057 = const()[name = tensor("op_4057"), val = tensor([1, 1])]; + tensor var_4059 = const()[name = tensor("op_4059"), val = tensor([1, 1])]; + tensor q_77_pad_type_0 = const()[name = tensor("q_77_pad_type_0"), val = tensor("custom")]; + tensor q_77_pad_0 = const()[name = tensor("q_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1309707520)))]; + tensor q_77_cast = conv(dilations = var_4059, groups = var_1186, pad = q_77_pad_0, pad_type = q_77_pad_type_0, strides = var_4057, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_167_cast)[name = tensor("q_77_cast")]; + tensor var_4063 = const()[name = tensor("op_4063"), val = tensor([1, 1])]; + tensor var_4065 = const()[name = tensor("op_4065"), val = tensor([1, 1])]; + tensor k_77_pad_type_0 = const()[name = tensor("k_77_pad_type_0"), val = tensor("custom")]; + tensor k_77_pad_0 = const()[name = tensor("k_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312984384)))]; + tensor k_77_cast = conv(dilations = var_4065, groups = var_1186, pad = k_77_pad_0, pad_type = k_77_pad_type_0, strides = var_4063, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_167_cast)[name = tensor("k_77_cast")]; + tensor var_4069 = const()[name = tensor("op_4069"), val = tensor([1, 1])]; + tensor var_4071 = const()[name = tensor("op_4071"), val = tensor([1, 1])]; + tensor v_77_pad_type_0 = const()[name = tensor("v_77_pad_type_0"), val = tensor("custom")]; + tensor v_77_pad_0 = const()[name = tensor("v_77_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1316261248)))]; + tensor v_77_cast = conv(dilations = var_4071, groups = var_1186, pad = v_77_pad_0, pad_type = v_77_pad_type_0, strides = var_4069, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_167_cast)[name = tensor("v_77_cast")]; + tensor var_4075 = const()[name = tensor("op_4075"), val = tensor([2, 20, 64, -1])]; + tensor var_4076_cast = reshape(shape = var_4075, x = q_77_cast)[name = tensor("op_4076_cast")]; + tensor var_4077 = const()[name = tensor("op_4077"), val = tensor([2, 20, 64, -1])]; + tensor var_4078_cast = reshape(shape = var_4077, x = k_77_cast)[name = tensor("op_4078_cast")]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([2, 20, 64, -1])]; + tensor var_4080_cast = reshape(shape = var_4079, x = v_77_cast)[name = tensor("op_4080_cast")]; + tensor attn_weights_153_transpose_x_0 = const()[name = tensor("attn_weights_153_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_153_transpose_y_0 = const()[name = tensor("attn_weights_153_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_153_cast = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_4076_cast, y = var_4078_cast)[name = tensor("attn_weights_153_cast")]; + tensor attn_weights_155_cast = mul(x = attn_weights_153_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_155_cast")]; + tensor var_4084_cast = softmax(axis = var_1170, x = attn_weights_155_cast)[name = tensor("op_4084_cast")]; + tensor attn_77_transpose_x_0 = const()[name = tensor("attn_77_transpose_x_0"), val = tensor(false)]; + tensor attn_77_transpose_y_0 = const()[name = tensor("attn_77_transpose_y_0"), val = tensor(true)]; + tensor attn_77_cast = matmul(transpose_x = attn_77_transpose_x_0, transpose_y = attn_77_transpose_y_0, x = var_4080_cast, y = var_4084_cast)[name = tensor("attn_77_cast")]; + tensor var_4088 = const()[name = tensor("op_4088"), val = tensor([2, 1280, 1, -1])]; + tensor input_269_cast = reshape(shape = var_4088, x = attn_77_cast)[name = tensor("input_269_cast")]; + tensor var_4093 = const()[name = tensor("op_4093"), val = tensor([1, 1])]; + tensor var_4095 = const()[name = tensor("op_4095"), val = tensor([1, 1])]; + tensor var_4097_pad_type_0 = const()[name = tensor("op_4097_pad_type_0"), val = tensor("custom")]; + tensor var_4097_pad_0 = const()[name = tensor("op_4097_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1319538112)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322814976)))]; + tensor var_4097_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_4095, groups = var_1186, pad = var_4097_pad_0, pad_type = var_4097_pad_type_0, strides = var_4093, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_269_cast)[name = tensor("op_4097_cast")]; + tensor inputs_117_cast = add(x = var_4097_cast, y = inputs_115_cast)[name = tensor("inputs_117_cast")]; + tensor var_4101 = const()[name = tensor("op_4101"), val = tensor([1])]; + tensor channels_mean_117_cast = reduce_mean(axes = var_4101, keep_dims = var_1181, x = inputs_117_cast)[name = tensor("channels_mean_117_cast")]; + tensor zero_mean_117_cast = sub(x = inputs_117_cast, y = channels_mean_117_cast)[name = tensor("zero_mean_117_cast")]; + tensor zero_mean_sq_117_cast = mul(x = zero_mean_117_cast, y = zero_mean_117_cast)[name = tensor("zero_mean_sq_117_cast")]; + tensor var_4105 = const()[name = tensor("op_4105"), val = tensor([1])]; + tensor var_4106_cast = reduce_mean(axes = var_4105, keep_dims = var_1181, x = zero_mean_sq_117_cast)[name = tensor("op_4106_cast")]; + tensor var_4107_to_fp16 = const()[name = tensor("op_4107_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4108_cast = add(x = var_4106_cast, y = var_4107_to_fp16)[name = tensor("op_4108_cast")]; + tensor denom_117_epsilon_0_to_fp16 = const()[name = tensor("denom_117_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_117_cast = rsqrt(epsilon = denom_117_epsilon_0_to_fp16, x = var_4108_cast)[name = tensor("denom_117_cast")]; + tensor out_117_cast = mul(x = zero_mean_117_cast, y = denom_117_cast)[name = tensor("out_117_cast")]; + tensor var_4112_to_fp16 = const()[name = tensor("op_4112_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322817600)))]; + tensor var_4113_cast = add(x = out_117_cast, y = var_4112_to_fp16)[name = tensor("op_4113_cast")]; + tensor var_4115_to_fp16 = const()[name = tensor("op_4115_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322820224)))]; + tensor hidden_states_169_cast = mul(x = var_4113_cast, y = var_4115_to_fp16)[name = tensor("hidden_states_169_cast")]; + tensor var_4122 = const()[name = tensor("op_4122"), val = tensor([1, 1])]; + tensor var_4124 = const()[name = tensor("op_4124"), val = tensor([1, 1])]; + tensor q_79_pad_type_0 = const()[name = tensor("q_79_pad_type_0"), val = tensor("custom")]; + tensor q_79_pad_0 = const()[name = tensor("q_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322822848)))]; + tensor q_79_cast = conv(dilations = var_4124, groups = var_1186, pad = q_79_pad_0, pad_type = q_79_pad_type_0, strides = var_4122, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_169_cast)[name = tensor("q_79_cast")]; + tensor var_4128 = const()[name = tensor("op_4128"), val = tensor([1, 1])]; + tensor var_4130 = const()[name = tensor("op_4130"), val = tensor([1, 1])]; + tensor k_79_pad_type_0 = const()[name = tensor("k_79_pad_type_0"), val = tensor("custom")]; + tensor k_79_pad_0 = const()[name = tensor("k_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1326099712)))]; + tensor k_79_cast = conv(dilations = var_4130, groups = var_1186, pad = k_79_pad_0, pad_type = k_79_pad_type_0, strides = var_4128, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_79_cast")]; + tensor var_4134 = const()[name = tensor("op_4134"), val = tensor([1, 1])]; + tensor var_4136 = const()[name = tensor("op_4136"), val = tensor([1, 1])]; + tensor v_79_pad_type_0 = const()[name = tensor("v_79_pad_type_0"), val = tensor("custom")]; + tensor v_79_pad_0 = const()[name = tensor("v_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1331342656)))]; + tensor v_79_cast = conv(dilations = var_4136, groups = var_1186, pad = v_79_pad_0, pad_type = v_79_pad_type_0, strides = var_4134, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_79_cast")]; + tensor var_4140 = const()[name = tensor("op_4140"), val = tensor([2, 20, 64, -1])]; + tensor var_4141_cast = reshape(shape = var_4140, x = q_79_cast)[name = tensor("op_4141_cast")]; + tensor var_4142 = const()[name = tensor("op_4142"), val = tensor([2, 20, 64, -1])]; + tensor var_4143_cast = reshape(shape = var_4142, x = k_79_cast)[name = tensor("op_4143_cast")]; + tensor var_4144 = const()[name = tensor("op_4144"), val = tensor([2, 20, 64, -1])]; + tensor var_4145_cast = reshape(shape = var_4144, x = v_79_cast)[name = tensor("op_4145_cast")]; + tensor attn_weights_157_transpose_x_0 = const()[name = tensor("attn_weights_157_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_157_transpose_y_0 = const()[name = tensor("attn_weights_157_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_157_cast = matmul(transpose_x = attn_weights_157_transpose_x_0, transpose_y = attn_weights_157_transpose_y_0, x = var_4141_cast, y = var_4143_cast)[name = tensor("attn_weights_157_cast")]; + tensor attn_weights_159_cast = mul(x = attn_weights_157_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_159_cast")]; + tensor var_4149_cast = softmax(axis = var_1170, x = attn_weights_159_cast)[name = tensor("op_4149_cast")]; + tensor attn_79_transpose_x_0 = const()[name = tensor("attn_79_transpose_x_0"), val = tensor(false)]; + tensor attn_79_transpose_y_0 = const()[name = tensor("attn_79_transpose_y_0"), val = tensor(true)]; + tensor attn_79_cast = matmul(transpose_x = attn_79_transpose_x_0, transpose_y = attn_79_transpose_y_0, x = var_4145_cast, y = var_4149_cast)[name = tensor("attn_79_cast")]; + tensor var_4153 = const()[name = tensor("op_4153"), val = tensor([2, 1280, 1, -1])]; + tensor input_271_cast = reshape(shape = var_4153, x = attn_79_cast)[name = tensor("input_271_cast")]; + tensor var_4158 = const()[name = tensor("op_4158"), val = tensor([1, 1])]; + tensor var_4160 = const()[name = tensor("op_4160"), val = tensor([1, 1])]; + tensor var_4162_pad_type_0 = const()[name = tensor("op_4162_pad_type_0"), val = tensor("custom")]; + tensor var_4162_pad_0 = const()[name = tensor("op_4162_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1336585600)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1339862464)))]; + tensor var_4162_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_4160, groups = var_1186, pad = var_4162_pad_0, pad_type = var_4162_pad_type_0, strides = var_4158, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_271_cast)[name = tensor("op_4162_cast")]; + tensor inputs_119_cast = add(x = var_4162_cast, y = inputs_117_cast)[name = tensor("inputs_119_cast")]; + tensor var_4166 = const()[name = tensor("op_4166"), val = tensor([1])]; + tensor channels_mean_119_cast = reduce_mean(axes = var_4166, keep_dims = var_1181, x = inputs_119_cast)[name = tensor("channels_mean_119_cast")]; + tensor zero_mean_119_cast = sub(x = inputs_119_cast, y = channels_mean_119_cast)[name = tensor("zero_mean_119_cast")]; + tensor zero_mean_sq_119_cast = mul(x = zero_mean_119_cast, y = zero_mean_119_cast)[name = tensor("zero_mean_sq_119_cast")]; + tensor var_4170 = const()[name = tensor("op_4170"), val = tensor([1])]; + tensor var_4171_cast = reduce_mean(axes = var_4170, keep_dims = var_1181, x = zero_mean_sq_119_cast)[name = tensor("op_4171_cast")]; + tensor var_4172_to_fp16 = const()[name = tensor("op_4172_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4173_cast = add(x = var_4171_cast, y = var_4172_to_fp16)[name = tensor("op_4173_cast")]; + tensor denom_119_epsilon_0_to_fp16 = const()[name = tensor("denom_119_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_119_cast = rsqrt(epsilon = denom_119_epsilon_0_to_fp16, x = var_4173_cast)[name = tensor("denom_119_cast")]; + tensor out_119_cast = mul(x = zero_mean_119_cast, y = denom_119_cast)[name = tensor("out_119_cast")]; + tensor var_4177_to_fp16 = const()[name = tensor("op_4177_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1339865088)))]; + tensor var_4178_cast = add(x = out_119_cast, y = var_4177_to_fp16)[name = tensor("op_4178_cast")]; + tensor var_4180_to_fp16 = const()[name = tensor("op_4180_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1339867712)))]; + tensor input_273_cast = mul(x = var_4178_cast, y = var_4180_to_fp16)[name = tensor("input_273_cast")]; + tensor var_4188 = const()[name = tensor("op_4188"), val = tensor([1, 1])]; + tensor var_4190 = const()[name = tensor("op_4190"), val = tensor([1, 1])]; + tensor var_4192_pad_type_0 = const()[name = tensor("op_4192_pad_type_0"), val = tensor("custom")]; + tensor var_4192_pad_0 = const()[name = tensor("op_4192_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1339870336)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1366084800)))]; + tensor var_4192_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_4190, groups = var_1186, pad = var_4192_pad_0, pad_type = var_4192_pad_type_0, strides = var_4188, weight = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_273_cast)[name = tensor("op_4192_cast")]; + tensor var_4193_split_sizes_0 = const()[name = tensor("op_4193_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4193_axis_0 = const()[name = tensor("op_4193_axis_0"), val = tensor(1)]; + tensor var_4193_cast_0, tensor var_4193_cast_1 = split(axis = var_4193_axis_0, split_sizes = var_4193_split_sizes_0, x = var_4192_cast)[name = tensor("op_4193_cast")]; + tensor var_4195_mode_0 = const()[name = tensor("op_4195_mode_0"), val = tensor("EXACT")]; + tensor var_4195_cast = gelu(mode = var_4195_mode_0, x = var_4193_cast_1)[name = tensor("op_4195_cast")]; + tensor input_275_cast = mul(x = var_4193_cast_0, y = var_4195_cast)[name = tensor("input_275_cast")]; + tensor var_4199 = const()[name = tensor("op_4199"), val = tensor([1, 1])]; + tensor var_4201 = const()[name = tensor("op_4201"), val = tensor([1, 1])]; + tensor var_4203_pad_type_0 = const()[name = tensor("op_4203_pad_type_0"), val = tensor("custom")]; + tensor var_4203_pad_0 = const()[name = tensor("op_4203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1366105344)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379212608)))]; + tensor var_4203_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_4201, groups = var_1186, pad = var_4203_pad_0, pad_type = var_4203_pad_type_0, strides = var_4199, weight = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_275_cast)[name = tensor("op_4203_cast")]; + tensor inputs_121_cast = add(x = var_4203_cast, y = inputs_119_cast)[name = tensor("inputs_121_cast")]; + tensor var_4213 = const()[name = tensor("op_4213"), val = tensor([1])]; + tensor channels_mean_121_cast = reduce_mean(axes = var_4213, keep_dims = var_1181, x = inputs_121_cast)[name = tensor("channels_mean_121_cast")]; + tensor zero_mean_121_cast = sub(x = inputs_121_cast, y = channels_mean_121_cast)[name = tensor("zero_mean_121_cast")]; + tensor zero_mean_sq_121_cast = mul(x = zero_mean_121_cast, y = zero_mean_121_cast)[name = tensor("zero_mean_sq_121_cast")]; + tensor var_4217 = const()[name = tensor("op_4217"), val = tensor([1])]; + tensor var_4218_cast = reduce_mean(axes = var_4217, keep_dims = var_1181, x = zero_mean_sq_121_cast)[name = tensor("op_4218_cast")]; + tensor var_4219_to_fp16 = const()[name = tensor("op_4219_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4220_cast = add(x = var_4218_cast, y = var_4219_to_fp16)[name = tensor("op_4220_cast")]; + tensor denom_121_epsilon_0_to_fp16 = const()[name = tensor("denom_121_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_121_cast = rsqrt(epsilon = denom_121_epsilon_0_to_fp16, x = var_4220_cast)[name = tensor("denom_121_cast")]; + tensor out_121_cast = mul(x = zero_mean_121_cast, y = denom_121_cast)[name = tensor("out_121_cast")]; + tensor var_4224_to_fp16 = const()[name = tensor("op_4224_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379215232)))]; + tensor var_4225_cast = add(x = out_121_cast, y = var_4224_to_fp16)[name = tensor("op_4225_cast")]; + tensor var_4227_to_fp16 = const()[name = tensor("op_4227_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379217856)))]; + tensor hidden_states_173_cast = mul(x = var_4225_cast, y = var_4227_to_fp16)[name = tensor("hidden_states_173_cast")]; + tensor var_4234 = const()[name = tensor("op_4234"), val = tensor([1, 1])]; + tensor var_4236 = const()[name = tensor("op_4236"), val = tensor([1, 1])]; + tensor q_81_pad_type_0 = const()[name = tensor("q_81_pad_type_0"), val = tensor("custom")]; + tensor q_81_pad_0 = const()[name = tensor("q_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379220480)))]; + tensor q_81_cast = conv(dilations = var_4236, groups = var_1186, pad = q_81_pad_0, pad_type = q_81_pad_type_0, strides = var_4234, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_173_cast)[name = tensor("q_81_cast")]; + tensor var_4240 = const()[name = tensor("op_4240"), val = tensor([1, 1])]; + tensor var_4242 = const()[name = tensor("op_4242"), val = tensor([1, 1])]; + tensor k_81_pad_type_0 = const()[name = tensor("k_81_pad_type_0"), val = tensor("custom")]; + tensor k_81_pad_0 = const()[name = tensor("k_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1382497344)))]; + tensor k_81_cast = conv(dilations = var_4242, groups = var_1186, pad = k_81_pad_0, pad_type = k_81_pad_type_0, strides = var_4240, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_173_cast)[name = tensor("k_81_cast")]; + tensor var_4246 = const()[name = tensor("op_4246"), val = tensor([1, 1])]; + tensor var_4248 = const()[name = tensor("op_4248"), val = tensor([1, 1])]; + tensor v_81_pad_type_0 = const()[name = tensor("v_81_pad_type_0"), val = tensor("custom")]; + tensor v_81_pad_0 = const()[name = tensor("v_81_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1385774208)))]; + tensor v_81_cast = conv(dilations = var_4248, groups = var_1186, pad = v_81_pad_0, pad_type = v_81_pad_type_0, strides = var_4246, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_173_cast)[name = tensor("v_81_cast")]; + tensor var_4252 = const()[name = tensor("op_4252"), val = tensor([2, 20, 64, -1])]; + tensor var_4253_cast = reshape(shape = var_4252, x = q_81_cast)[name = tensor("op_4253_cast")]; + tensor var_4254 = const()[name = tensor("op_4254"), val = tensor([2, 20, 64, -1])]; + tensor var_4255_cast = reshape(shape = var_4254, x = k_81_cast)[name = tensor("op_4255_cast")]; + tensor var_4256 = const()[name = tensor("op_4256"), val = tensor([2, 20, 64, -1])]; + tensor var_4257_cast = reshape(shape = var_4256, x = v_81_cast)[name = tensor("op_4257_cast")]; + tensor attn_weights_161_transpose_x_0 = const()[name = tensor("attn_weights_161_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_161_transpose_y_0 = const()[name = tensor("attn_weights_161_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_161_cast = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4253_cast, y = var_4255_cast)[name = tensor("attn_weights_161_cast")]; + tensor attn_weights_163_cast = mul(x = attn_weights_161_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_163_cast")]; + tensor var_4261_cast = softmax(axis = var_1170, x = attn_weights_163_cast)[name = tensor("op_4261_cast")]; + tensor attn_81_transpose_x_0 = const()[name = tensor("attn_81_transpose_x_0"), val = tensor(false)]; + tensor attn_81_transpose_y_0 = const()[name = tensor("attn_81_transpose_y_0"), val = tensor(true)]; + tensor attn_81_cast = matmul(transpose_x = attn_81_transpose_x_0, transpose_y = attn_81_transpose_y_0, x = var_4257_cast, y = var_4261_cast)[name = tensor("attn_81_cast")]; + tensor var_4265 = const()[name = tensor("op_4265"), val = tensor([2, 1280, 1, -1])]; + tensor input_277_cast = reshape(shape = var_4265, x = attn_81_cast)[name = tensor("input_277_cast")]; + tensor var_4270 = const()[name = tensor("op_4270"), val = tensor([1, 1])]; + tensor var_4272 = const()[name = tensor("op_4272"), val = tensor([1, 1])]; + tensor var_4274_pad_type_0 = const()[name = tensor("op_4274_pad_type_0"), val = tensor("custom")]; + tensor var_4274_pad_0 = const()[name = tensor("op_4274_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1389051072)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1392327936)))]; + tensor var_4274_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_4272, groups = var_1186, pad = var_4274_pad_0, pad_type = var_4274_pad_type_0, strides = var_4270, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_277_cast)[name = tensor("op_4274_cast")]; + tensor inputs_123_cast = add(x = var_4274_cast, y = inputs_121_cast)[name = tensor("inputs_123_cast")]; + tensor var_4278 = const()[name = tensor("op_4278"), val = tensor([1])]; + tensor channels_mean_123_cast = reduce_mean(axes = var_4278, keep_dims = var_1181, x = inputs_123_cast)[name = tensor("channels_mean_123_cast")]; + tensor zero_mean_123_cast = sub(x = inputs_123_cast, y = channels_mean_123_cast)[name = tensor("zero_mean_123_cast")]; + tensor zero_mean_sq_123_cast = mul(x = zero_mean_123_cast, y = zero_mean_123_cast)[name = tensor("zero_mean_sq_123_cast")]; + tensor var_4282 = const()[name = tensor("op_4282"), val = tensor([1])]; + tensor var_4283_cast = reduce_mean(axes = var_4282, keep_dims = var_1181, x = zero_mean_sq_123_cast)[name = tensor("op_4283_cast")]; + tensor var_4284_to_fp16 = const()[name = tensor("op_4284_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4285_cast = add(x = var_4283_cast, y = var_4284_to_fp16)[name = tensor("op_4285_cast")]; + tensor denom_123_epsilon_0_to_fp16 = const()[name = tensor("denom_123_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_123_cast = rsqrt(epsilon = denom_123_epsilon_0_to_fp16, x = var_4285_cast)[name = tensor("denom_123_cast")]; + tensor out_123_cast = mul(x = zero_mean_123_cast, y = denom_123_cast)[name = tensor("out_123_cast")]; + tensor var_4289_to_fp16 = const()[name = tensor("op_4289_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1392330560)))]; + tensor var_4290_cast = add(x = out_123_cast, y = var_4289_to_fp16)[name = tensor("op_4290_cast")]; + tensor var_4292_to_fp16 = const()[name = tensor("op_4292_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1392333184)))]; + tensor hidden_states_175_cast = mul(x = var_4290_cast, y = var_4292_to_fp16)[name = tensor("hidden_states_175_cast")]; + tensor var_4299 = const()[name = tensor("op_4299"), val = tensor([1, 1])]; + tensor var_4301 = const()[name = tensor("op_4301"), val = tensor([1, 1])]; + tensor q_83_pad_type_0 = const()[name = tensor("q_83_pad_type_0"), val = tensor("custom")]; + tensor q_83_pad_0 = const()[name = tensor("q_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1392335808)))]; + tensor q_83_cast = conv(dilations = var_4301, groups = var_1186, pad = q_83_pad_0, pad_type = q_83_pad_type_0, strides = var_4299, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_175_cast)[name = tensor("q_83_cast")]; + tensor var_4305 = const()[name = tensor("op_4305"), val = tensor([1, 1])]; + tensor var_4307 = const()[name = tensor("op_4307"), val = tensor([1, 1])]; + tensor k_83_pad_type_0 = const()[name = tensor("k_83_pad_type_0"), val = tensor("custom")]; + tensor k_83_pad_0 = const()[name = tensor("k_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1395612672)))]; + tensor k_83_cast = conv(dilations = var_4307, groups = var_1186, pad = k_83_pad_0, pad_type = k_83_pad_type_0, strides = var_4305, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_83_cast")]; + tensor var_4311 = const()[name = tensor("op_4311"), val = tensor([1, 1])]; + tensor var_4313 = const()[name = tensor("op_4313"), val = tensor([1, 1])]; + tensor v_83_pad_type_0 = const()[name = tensor("v_83_pad_type_0"), val = tensor("custom")]; + tensor v_83_pad_0 = const()[name = tensor("v_83_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1400855616)))]; + tensor v_83_cast = conv(dilations = var_4313, groups = var_1186, pad = v_83_pad_0, pad_type = v_83_pad_type_0, strides = var_4311, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_83_cast")]; + tensor var_4317 = const()[name = tensor("op_4317"), val = tensor([2, 20, 64, -1])]; + tensor var_4318_cast = reshape(shape = var_4317, x = q_83_cast)[name = tensor("op_4318_cast")]; + tensor var_4319 = const()[name = tensor("op_4319"), val = tensor([2, 20, 64, -1])]; + tensor var_4320_cast = reshape(shape = var_4319, x = k_83_cast)[name = tensor("op_4320_cast")]; + tensor var_4321 = const()[name = tensor("op_4321"), val = tensor([2, 20, 64, -1])]; + tensor var_4322_cast = reshape(shape = var_4321, x = v_83_cast)[name = tensor("op_4322_cast")]; + tensor attn_weights_165_transpose_x_0 = const()[name = tensor("attn_weights_165_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_165_transpose_y_0 = const()[name = tensor("attn_weights_165_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_165_cast = matmul(transpose_x = attn_weights_165_transpose_x_0, transpose_y = attn_weights_165_transpose_y_0, x = var_4318_cast, y = var_4320_cast)[name = tensor("attn_weights_165_cast")]; + tensor attn_weights_167_cast = mul(x = attn_weights_165_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_167_cast")]; + tensor var_4326_cast = softmax(axis = var_1170, x = attn_weights_167_cast)[name = tensor("op_4326_cast")]; + tensor attn_83_transpose_x_0 = const()[name = tensor("attn_83_transpose_x_0"), val = tensor(false)]; + tensor attn_83_transpose_y_0 = const()[name = tensor("attn_83_transpose_y_0"), val = tensor(true)]; + tensor attn_83_cast = matmul(transpose_x = attn_83_transpose_x_0, transpose_y = attn_83_transpose_y_0, x = var_4322_cast, y = var_4326_cast)[name = tensor("attn_83_cast")]; + tensor var_4330 = const()[name = tensor("op_4330"), val = tensor([2, 1280, 1, -1])]; + tensor input_279_cast = reshape(shape = var_4330, x = attn_83_cast)[name = tensor("input_279_cast")]; + tensor var_4335 = const()[name = tensor("op_4335"), val = tensor([1, 1])]; + tensor var_4337 = const()[name = tensor("op_4337"), val = tensor([1, 1])]; + tensor var_4339_pad_type_0 = const()[name = tensor("op_4339_pad_type_0"), val = tensor("custom")]; + tensor var_4339_pad_0 = const()[name = tensor("op_4339_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406098560)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1409375424)))]; + tensor var_4339_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_4337, groups = var_1186, pad = var_4339_pad_0, pad_type = var_4339_pad_type_0, strides = var_4335, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_279_cast)[name = tensor("op_4339_cast")]; + tensor inputs_125_cast = add(x = var_4339_cast, y = inputs_123_cast)[name = tensor("inputs_125_cast")]; + tensor var_4343 = const()[name = tensor("op_4343"), val = tensor([1])]; + tensor channels_mean_125_cast = reduce_mean(axes = var_4343, keep_dims = var_1181, x = inputs_125_cast)[name = tensor("channels_mean_125_cast")]; + tensor zero_mean_125_cast = sub(x = inputs_125_cast, y = channels_mean_125_cast)[name = tensor("zero_mean_125_cast")]; + tensor zero_mean_sq_125_cast = mul(x = zero_mean_125_cast, y = zero_mean_125_cast)[name = tensor("zero_mean_sq_125_cast")]; + tensor var_4347 = const()[name = tensor("op_4347"), val = tensor([1])]; + tensor var_4348_cast = reduce_mean(axes = var_4347, keep_dims = var_1181, x = zero_mean_sq_125_cast)[name = tensor("op_4348_cast")]; + tensor var_4349_to_fp16 = const()[name = tensor("op_4349_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4350_cast = add(x = var_4348_cast, y = var_4349_to_fp16)[name = tensor("op_4350_cast")]; + tensor denom_125_epsilon_0_to_fp16 = const()[name = tensor("denom_125_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_125_cast = rsqrt(epsilon = denom_125_epsilon_0_to_fp16, x = var_4350_cast)[name = tensor("denom_125_cast")]; + tensor out_125_cast = mul(x = zero_mean_125_cast, y = denom_125_cast)[name = tensor("out_125_cast")]; + tensor var_4354_to_fp16 = const()[name = tensor("op_4354_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1409378048)))]; + tensor var_4355_cast = add(x = out_125_cast, y = var_4354_to_fp16)[name = tensor("op_4355_cast")]; + tensor var_4357_to_fp16 = const()[name = tensor("op_4357_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1409380672)))]; + tensor input_281_cast = mul(x = var_4355_cast, y = var_4357_to_fp16)[name = tensor("input_281_cast")]; + tensor var_4365 = const()[name = tensor("op_4365"), val = tensor([1, 1])]; + tensor var_4367 = const()[name = tensor("op_4367"), val = tensor([1, 1])]; + tensor var_4369_pad_type_0 = const()[name = tensor("op_4369_pad_type_0"), val = tensor("custom")]; + tensor var_4369_pad_0 = const()[name = tensor("op_4369_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1409383296)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1435597760)))]; + tensor var_4369_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_4367, groups = var_1186, pad = var_4369_pad_0, pad_type = var_4369_pad_type_0, strides = var_4365, weight = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_281_cast)[name = tensor("op_4369_cast")]; + tensor var_4370_split_sizes_0 = const()[name = tensor("op_4370_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4370_axis_0 = const()[name = tensor("op_4370_axis_0"), val = tensor(1)]; + tensor var_4370_cast_0, tensor var_4370_cast_1 = split(axis = var_4370_axis_0, split_sizes = var_4370_split_sizes_0, x = var_4369_cast)[name = tensor("op_4370_cast")]; + tensor var_4372_mode_0 = const()[name = tensor("op_4372_mode_0"), val = tensor("EXACT")]; + tensor var_4372_cast = gelu(mode = var_4372_mode_0, x = var_4370_cast_1)[name = tensor("op_4372_cast")]; + tensor input_283_cast = mul(x = var_4370_cast_0, y = var_4372_cast)[name = tensor("input_283_cast")]; + tensor var_4376 = const()[name = tensor("op_4376"), val = tensor([1, 1])]; + tensor var_4378 = const()[name = tensor("op_4378"), val = tensor([1, 1])]; + tensor var_4380_pad_type_0 = const()[name = tensor("op_4380_pad_type_0"), val = tensor("custom")]; + tensor var_4380_pad_0 = const()[name = tensor("op_4380_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1435618304)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448725568)))]; + tensor var_4380_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_4378, groups = var_1186, pad = var_4380_pad_0, pad_type = var_4380_pad_type_0, strides = var_4376, weight = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_283_cast)[name = tensor("op_4380_cast")]; + tensor inputs_127_cast = add(x = var_4380_cast, y = inputs_125_cast)[name = tensor("inputs_127_cast")]; + tensor var_4390 = const()[name = tensor("op_4390"), val = tensor([1])]; + tensor channels_mean_127_cast = reduce_mean(axes = var_4390, keep_dims = var_1181, x = inputs_127_cast)[name = tensor("channels_mean_127_cast")]; + tensor zero_mean_127_cast = sub(x = inputs_127_cast, y = channels_mean_127_cast)[name = tensor("zero_mean_127_cast")]; + tensor zero_mean_sq_127_cast = mul(x = zero_mean_127_cast, y = zero_mean_127_cast)[name = tensor("zero_mean_sq_127_cast")]; + tensor var_4394 = const()[name = tensor("op_4394"), val = tensor([1])]; + tensor var_4395_cast = reduce_mean(axes = var_4394, keep_dims = var_1181, x = zero_mean_sq_127_cast)[name = tensor("op_4395_cast")]; + tensor var_4396_to_fp16 = const()[name = tensor("op_4396_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4397_cast = add(x = var_4395_cast, y = var_4396_to_fp16)[name = tensor("op_4397_cast")]; + tensor denom_127_epsilon_0_to_fp16 = const()[name = tensor("denom_127_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_127_cast = rsqrt(epsilon = denom_127_epsilon_0_to_fp16, x = var_4397_cast)[name = tensor("denom_127_cast")]; + tensor out_127_cast = mul(x = zero_mean_127_cast, y = denom_127_cast)[name = tensor("out_127_cast")]; + tensor var_4401_to_fp16 = const()[name = tensor("op_4401_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448728192)))]; + tensor var_4402_cast = add(x = out_127_cast, y = var_4401_to_fp16)[name = tensor("op_4402_cast")]; + tensor var_4404_to_fp16 = const()[name = tensor("op_4404_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448730816)))]; + tensor hidden_states_179_cast = mul(x = var_4402_cast, y = var_4404_to_fp16)[name = tensor("hidden_states_179_cast")]; + tensor var_4411 = const()[name = tensor("op_4411"), val = tensor([1, 1])]; + tensor var_4413 = const()[name = tensor("op_4413"), val = tensor([1, 1])]; + tensor q_85_pad_type_0 = const()[name = tensor("q_85_pad_type_0"), val = tensor("custom")]; + tensor q_85_pad_0 = const()[name = tensor("q_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448733440)))]; + tensor q_85_cast = conv(dilations = var_4413, groups = var_1186, pad = q_85_pad_0, pad_type = q_85_pad_type_0, strides = var_4411, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_179_cast)[name = tensor("q_85_cast")]; + tensor var_4417 = const()[name = tensor("op_4417"), val = tensor([1, 1])]; + tensor var_4419 = const()[name = tensor("op_4419"), val = tensor([1, 1])]; + tensor k_85_pad_type_0 = const()[name = tensor("k_85_pad_type_0"), val = tensor("custom")]; + tensor k_85_pad_0 = const()[name = tensor("k_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1452010304)))]; + tensor k_85_cast = conv(dilations = var_4419, groups = var_1186, pad = k_85_pad_0, pad_type = k_85_pad_type_0, strides = var_4417, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_179_cast)[name = tensor("k_85_cast")]; + tensor var_4423 = const()[name = tensor("op_4423"), val = tensor([1, 1])]; + tensor var_4425 = const()[name = tensor("op_4425"), val = tensor([1, 1])]; + tensor v_85_pad_type_0 = const()[name = tensor("v_85_pad_type_0"), val = tensor("custom")]; + tensor v_85_pad_0 = const()[name = tensor("v_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1455287168)))]; + tensor v_85_cast = conv(dilations = var_4425, groups = var_1186, pad = v_85_pad_0, pad_type = v_85_pad_type_0, strides = var_4423, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_179_cast)[name = tensor("v_85_cast")]; + tensor var_4429 = const()[name = tensor("op_4429"), val = tensor([2, 20, 64, -1])]; + tensor var_4430_cast = reshape(shape = var_4429, x = q_85_cast)[name = tensor("op_4430_cast")]; + tensor var_4431 = const()[name = tensor("op_4431"), val = tensor([2, 20, 64, -1])]; + tensor var_4432_cast = reshape(shape = var_4431, x = k_85_cast)[name = tensor("op_4432_cast")]; + tensor var_4433 = const()[name = tensor("op_4433"), val = tensor([2, 20, 64, -1])]; + tensor var_4434_cast = reshape(shape = var_4433, x = v_85_cast)[name = tensor("op_4434_cast")]; + tensor attn_weights_169_transpose_x_0 = const()[name = tensor("attn_weights_169_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_169_transpose_y_0 = const()[name = tensor("attn_weights_169_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_169_cast = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4430_cast, y = var_4432_cast)[name = tensor("attn_weights_169_cast")]; + tensor attn_weights_171_cast = mul(x = attn_weights_169_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_171_cast")]; + tensor var_4438_cast = softmax(axis = var_1170, x = attn_weights_171_cast)[name = tensor("op_4438_cast")]; + tensor attn_85_transpose_x_0 = const()[name = tensor("attn_85_transpose_x_0"), val = tensor(false)]; + tensor attn_85_transpose_y_0 = const()[name = tensor("attn_85_transpose_y_0"), val = tensor(true)]; + tensor attn_85_cast = matmul(transpose_x = attn_85_transpose_x_0, transpose_y = attn_85_transpose_y_0, x = var_4434_cast, y = var_4438_cast)[name = tensor("attn_85_cast")]; + tensor var_4442 = const()[name = tensor("op_4442"), val = tensor([2, 1280, 1, -1])]; + tensor input_285_cast = reshape(shape = var_4442, x = attn_85_cast)[name = tensor("input_285_cast")]; + tensor var_4447 = const()[name = tensor("op_4447"), val = tensor([1, 1])]; + tensor var_4449 = const()[name = tensor("op_4449"), val = tensor([1, 1])]; + tensor var_4451_pad_type_0 = const()[name = tensor("op_4451_pad_type_0"), val = tensor("custom")]; + tensor var_4451_pad_0 = const()[name = tensor("op_4451_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1458564032)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461840896)))]; + tensor var_4451_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_4449, groups = var_1186, pad = var_4451_pad_0, pad_type = var_4451_pad_type_0, strides = var_4447, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_285_cast)[name = tensor("op_4451_cast")]; + tensor inputs_129_cast = add(x = var_4451_cast, y = inputs_127_cast)[name = tensor("inputs_129_cast")]; + tensor var_4455 = const()[name = tensor("op_4455"), val = tensor([1])]; + tensor channels_mean_129_cast = reduce_mean(axes = var_4455, keep_dims = var_1181, x = inputs_129_cast)[name = tensor("channels_mean_129_cast")]; + tensor zero_mean_129_cast = sub(x = inputs_129_cast, y = channels_mean_129_cast)[name = tensor("zero_mean_129_cast")]; + tensor zero_mean_sq_129_cast = mul(x = zero_mean_129_cast, y = zero_mean_129_cast)[name = tensor("zero_mean_sq_129_cast")]; + tensor var_4459 = const()[name = tensor("op_4459"), val = tensor([1])]; + tensor var_4460_cast = reduce_mean(axes = var_4459, keep_dims = var_1181, x = zero_mean_sq_129_cast)[name = tensor("op_4460_cast")]; + tensor var_4461_to_fp16 = const()[name = tensor("op_4461_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4462_cast = add(x = var_4460_cast, y = var_4461_to_fp16)[name = tensor("op_4462_cast")]; + tensor denom_129_epsilon_0_to_fp16 = const()[name = tensor("denom_129_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_129_cast = rsqrt(epsilon = denom_129_epsilon_0_to_fp16, x = var_4462_cast)[name = tensor("denom_129_cast")]; + tensor out_129_cast = mul(x = zero_mean_129_cast, y = denom_129_cast)[name = tensor("out_129_cast")]; + tensor var_4466_to_fp16 = const()[name = tensor("op_4466_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461843520)))]; + tensor var_4467_cast = add(x = out_129_cast, y = var_4466_to_fp16)[name = tensor("op_4467_cast")]; + tensor var_4469_to_fp16 = const()[name = tensor("op_4469_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461846144)))]; + tensor hidden_states_181_cast = mul(x = var_4467_cast, y = var_4469_to_fp16)[name = tensor("hidden_states_181_cast")]; + tensor var_4476 = const()[name = tensor("op_4476"), val = tensor([1, 1])]; + tensor var_4478 = const()[name = tensor("op_4478"), val = tensor([1, 1])]; + tensor q_87_pad_type_0 = const()[name = tensor("q_87_pad_type_0"), val = tensor("custom")]; + tensor q_87_pad_0 = const()[name = tensor("q_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461848768)))]; + tensor q_87_cast = conv(dilations = var_4478, groups = var_1186, pad = q_87_pad_0, pad_type = q_87_pad_type_0, strides = var_4476, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_181_cast)[name = tensor("q_87_cast")]; + tensor var_4482 = const()[name = tensor("op_4482"), val = tensor([1, 1])]; + tensor var_4484 = const()[name = tensor("op_4484"), val = tensor([1, 1])]; + tensor k_87_pad_type_0 = const()[name = tensor("k_87_pad_type_0"), val = tensor("custom")]; + tensor k_87_pad_0 = const()[name = tensor("k_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465125632)))]; + tensor k_87_cast = conv(dilations = var_4484, groups = var_1186, pad = k_87_pad_0, pad_type = k_87_pad_type_0, strides = var_4482, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_87_cast")]; + tensor var_4488 = const()[name = tensor("op_4488"), val = tensor([1, 1])]; + tensor var_4490 = const()[name = tensor("op_4490"), val = tensor([1, 1])]; + tensor v_87_pad_type_0 = const()[name = tensor("v_87_pad_type_0"), val = tensor("custom")]; + tensor v_87_pad_0 = const()[name = tensor("v_87_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1470368576)))]; + tensor v_87_cast = conv(dilations = var_4490, groups = var_1186, pad = v_87_pad_0, pad_type = v_87_pad_type_0, strides = var_4488, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_87_cast")]; + tensor var_4494 = const()[name = tensor("op_4494"), val = tensor([2, 20, 64, -1])]; + tensor var_4495_cast = reshape(shape = var_4494, x = q_87_cast)[name = tensor("op_4495_cast")]; + tensor var_4496 = const()[name = tensor("op_4496"), val = tensor([2, 20, 64, -1])]; + tensor var_4497_cast = reshape(shape = var_4496, x = k_87_cast)[name = tensor("op_4497_cast")]; + tensor var_4498 = const()[name = tensor("op_4498"), val = tensor([2, 20, 64, -1])]; + tensor var_4499_cast = reshape(shape = var_4498, x = v_87_cast)[name = tensor("op_4499_cast")]; + tensor attn_weights_173_transpose_x_0 = const()[name = tensor("attn_weights_173_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_173_transpose_y_0 = const()[name = tensor("attn_weights_173_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_173_cast = matmul(transpose_x = attn_weights_173_transpose_x_0, transpose_y = attn_weights_173_transpose_y_0, x = var_4495_cast, y = var_4497_cast)[name = tensor("attn_weights_173_cast")]; + tensor attn_weights_175_cast = mul(x = attn_weights_173_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_175_cast")]; + tensor var_4503_cast = softmax(axis = var_1170, x = attn_weights_175_cast)[name = tensor("op_4503_cast")]; + tensor attn_87_transpose_x_0 = const()[name = tensor("attn_87_transpose_x_0"), val = tensor(false)]; + tensor attn_87_transpose_y_0 = const()[name = tensor("attn_87_transpose_y_0"), val = tensor(true)]; + tensor attn_87_cast = matmul(transpose_x = attn_87_transpose_x_0, transpose_y = attn_87_transpose_y_0, x = var_4499_cast, y = var_4503_cast)[name = tensor("attn_87_cast")]; + tensor var_4507 = const()[name = tensor("op_4507"), val = tensor([2, 1280, 1, -1])]; + tensor input_287_cast = reshape(shape = var_4507, x = attn_87_cast)[name = tensor("input_287_cast")]; + tensor var_4512 = const()[name = tensor("op_4512"), val = tensor([1, 1])]; + tensor var_4514 = const()[name = tensor("op_4514"), val = tensor([1, 1])]; + tensor var_4516_pad_type_0 = const()[name = tensor("op_4516_pad_type_0"), val = tensor("custom")]; + tensor var_4516_pad_0 = const()[name = tensor("op_4516_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1475611520)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1478888384)))]; + tensor var_4516_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_4514, groups = var_1186, pad = var_4516_pad_0, pad_type = var_4516_pad_type_0, strides = var_4512, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_287_cast)[name = tensor("op_4516_cast")]; + tensor inputs_131_cast = add(x = var_4516_cast, y = inputs_129_cast)[name = tensor("inputs_131_cast")]; + tensor var_4520 = const()[name = tensor("op_4520"), val = tensor([1])]; + tensor channels_mean_131_cast = reduce_mean(axes = var_4520, keep_dims = var_1181, x = inputs_131_cast)[name = tensor("channels_mean_131_cast")]; + tensor zero_mean_131_cast = sub(x = inputs_131_cast, y = channels_mean_131_cast)[name = tensor("zero_mean_131_cast")]; + tensor zero_mean_sq_131_cast = mul(x = zero_mean_131_cast, y = zero_mean_131_cast)[name = tensor("zero_mean_sq_131_cast")]; + tensor var_4524 = const()[name = tensor("op_4524"), val = tensor([1])]; + tensor var_4525_cast = reduce_mean(axes = var_4524, keep_dims = var_1181, x = zero_mean_sq_131_cast)[name = tensor("op_4525_cast")]; + tensor var_4526_to_fp16 = const()[name = tensor("op_4526_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4527_cast = add(x = var_4525_cast, y = var_4526_to_fp16)[name = tensor("op_4527_cast")]; + tensor denom_131_epsilon_0_to_fp16 = const()[name = tensor("denom_131_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_131_cast = rsqrt(epsilon = denom_131_epsilon_0_to_fp16, x = var_4527_cast)[name = tensor("denom_131_cast")]; + tensor out_131_cast = mul(x = zero_mean_131_cast, y = denom_131_cast)[name = tensor("out_131_cast")]; + tensor var_4531_to_fp16 = const()[name = tensor("op_4531_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1478891008)))]; + tensor var_4532_cast = add(x = out_131_cast, y = var_4531_to_fp16)[name = tensor("op_4532_cast")]; + tensor var_4534_to_fp16 = const()[name = tensor("op_4534_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1478893632)))]; + tensor input_289_cast = mul(x = var_4532_cast, y = var_4534_to_fp16)[name = tensor("input_289_cast")]; + tensor var_4542 = const()[name = tensor("op_4542"), val = tensor([1, 1])]; + tensor var_4544 = const()[name = tensor("op_4544"), val = tensor([1, 1])]; + tensor var_4546_pad_type_0 = const()[name = tensor("op_4546_pad_type_0"), val = tensor("custom")]; + tensor var_4546_pad_0 = const()[name = tensor("op_4546_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1478896256)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1505110720)))]; + tensor var_4546_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_4544, groups = var_1186, pad = var_4546_pad_0, pad_type = var_4546_pad_type_0, strides = var_4542, weight = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_289_cast)[name = tensor("op_4546_cast")]; + tensor var_4547_split_sizes_0 = const()[name = tensor("op_4547_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4547_axis_0 = const()[name = tensor("op_4547_axis_0"), val = tensor(1)]; + tensor var_4547_cast_0, tensor var_4547_cast_1 = split(axis = var_4547_axis_0, split_sizes = var_4547_split_sizes_0, x = var_4546_cast)[name = tensor("op_4547_cast")]; + tensor var_4549_mode_0 = const()[name = tensor("op_4549_mode_0"), val = tensor("EXACT")]; + tensor var_4549_cast = gelu(mode = var_4549_mode_0, x = var_4547_cast_1)[name = tensor("op_4549_cast")]; + tensor input_291_cast = mul(x = var_4547_cast_0, y = var_4549_cast)[name = tensor("input_291_cast")]; + tensor var_4553 = const()[name = tensor("op_4553"), val = tensor([1, 1])]; + tensor var_4555 = const()[name = tensor("op_4555"), val = tensor([1, 1])]; + tensor var_4557_pad_type_0 = const()[name = tensor("op_4557_pad_type_0"), val = tensor("custom")]; + tensor var_4557_pad_0 = const()[name = tensor("op_4557_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1505131264)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1518238528)))]; + tensor var_4557_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_4555, groups = var_1186, pad = var_4557_pad_0, pad_type = var_4557_pad_type_0, strides = var_4553, weight = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_291_cast)[name = tensor("op_4557_cast")]; + tensor inputs_133_cast = add(x = var_4557_cast, y = inputs_131_cast)[name = tensor("inputs_133_cast")]; + tensor var_4567 = const()[name = tensor("op_4567"), val = tensor([1])]; + tensor channels_mean_133_cast = reduce_mean(axes = var_4567, keep_dims = var_1181, x = inputs_133_cast)[name = tensor("channels_mean_133_cast")]; + tensor zero_mean_133_cast = sub(x = inputs_133_cast, y = channels_mean_133_cast)[name = tensor("zero_mean_133_cast")]; + tensor zero_mean_sq_133_cast = mul(x = zero_mean_133_cast, y = zero_mean_133_cast)[name = tensor("zero_mean_sq_133_cast")]; + tensor var_4571 = const()[name = tensor("op_4571"), val = tensor([1])]; + tensor var_4572_cast = reduce_mean(axes = var_4571, keep_dims = var_1181, x = zero_mean_sq_133_cast)[name = tensor("op_4572_cast")]; + tensor var_4573_to_fp16 = const()[name = tensor("op_4573_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4574_cast = add(x = var_4572_cast, y = var_4573_to_fp16)[name = tensor("op_4574_cast")]; + tensor denom_133_epsilon_0_to_fp16 = const()[name = tensor("denom_133_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_133_cast = rsqrt(epsilon = denom_133_epsilon_0_to_fp16, x = var_4574_cast)[name = tensor("denom_133_cast")]; + tensor out_133_cast = mul(x = zero_mean_133_cast, y = denom_133_cast)[name = tensor("out_133_cast")]; + tensor var_4578_to_fp16 = const()[name = tensor("op_4578_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1518241152)))]; + tensor var_4579_cast = add(x = out_133_cast, y = var_4578_to_fp16)[name = tensor("op_4579_cast")]; + tensor var_4581_to_fp16 = const()[name = tensor("op_4581_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1518243776)))]; + tensor hidden_states_185_cast = mul(x = var_4579_cast, y = var_4581_to_fp16)[name = tensor("hidden_states_185_cast")]; + tensor var_4588 = const()[name = tensor("op_4588"), val = tensor([1, 1])]; + tensor var_4590 = const()[name = tensor("op_4590"), val = tensor([1, 1])]; + tensor q_89_pad_type_0 = const()[name = tensor("q_89_pad_type_0"), val = tensor("custom")]; + tensor q_89_pad_0 = const()[name = tensor("q_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1518246400)))]; + tensor q_89_cast = conv(dilations = var_4590, groups = var_1186, pad = q_89_pad_0, pad_type = q_89_pad_type_0, strides = var_4588, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_185_cast)[name = tensor("q_89_cast")]; + tensor var_4594 = const()[name = tensor("op_4594"), val = tensor([1, 1])]; + tensor var_4596 = const()[name = tensor("op_4596"), val = tensor([1, 1])]; + tensor k_89_pad_type_0 = const()[name = tensor("k_89_pad_type_0"), val = tensor("custom")]; + tensor k_89_pad_0 = const()[name = tensor("k_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1521523264)))]; + tensor k_89_cast = conv(dilations = var_4596, groups = var_1186, pad = k_89_pad_0, pad_type = k_89_pad_type_0, strides = var_4594, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_185_cast)[name = tensor("k_89_cast")]; + tensor var_4600 = const()[name = tensor("op_4600"), val = tensor([1, 1])]; + tensor var_4602 = const()[name = tensor("op_4602"), val = tensor([1, 1])]; + tensor v_89_pad_type_0 = const()[name = tensor("v_89_pad_type_0"), val = tensor("custom")]; + tensor v_89_pad_0 = const()[name = tensor("v_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1524800128)))]; + tensor v_89_cast = conv(dilations = var_4602, groups = var_1186, pad = v_89_pad_0, pad_type = v_89_pad_type_0, strides = var_4600, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_185_cast)[name = tensor("v_89_cast")]; + tensor var_4606 = const()[name = tensor("op_4606"), val = tensor([2, 20, 64, -1])]; + tensor var_4607_cast = reshape(shape = var_4606, x = q_89_cast)[name = tensor("op_4607_cast")]; + tensor var_4608 = const()[name = tensor("op_4608"), val = tensor([2, 20, 64, -1])]; + tensor var_4609_cast = reshape(shape = var_4608, x = k_89_cast)[name = tensor("op_4609_cast")]; + tensor var_4610 = const()[name = tensor("op_4610"), val = tensor([2, 20, 64, -1])]; + tensor var_4611_cast = reshape(shape = var_4610, x = v_89_cast)[name = tensor("op_4611_cast")]; + tensor attn_weights_177_transpose_x_0 = const()[name = tensor("attn_weights_177_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_177_transpose_y_0 = const()[name = tensor("attn_weights_177_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_177_cast = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4607_cast, y = var_4609_cast)[name = tensor("attn_weights_177_cast")]; + tensor attn_weights_179_cast = mul(x = attn_weights_177_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_179_cast")]; + tensor var_4615_cast = softmax(axis = var_1170, x = attn_weights_179_cast)[name = tensor("op_4615_cast")]; + tensor attn_89_transpose_x_0 = const()[name = tensor("attn_89_transpose_x_0"), val = tensor(false)]; + tensor attn_89_transpose_y_0 = const()[name = tensor("attn_89_transpose_y_0"), val = tensor(true)]; + tensor attn_89_cast = matmul(transpose_x = attn_89_transpose_x_0, transpose_y = attn_89_transpose_y_0, x = var_4611_cast, y = var_4615_cast)[name = tensor("attn_89_cast")]; + tensor var_4619 = const()[name = tensor("op_4619"), val = tensor([2, 1280, 1, -1])]; + tensor input_293_cast = reshape(shape = var_4619, x = attn_89_cast)[name = tensor("input_293_cast")]; + tensor var_4624 = const()[name = tensor("op_4624"), val = tensor([1, 1])]; + tensor var_4626 = const()[name = tensor("op_4626"), val = tensor([1, 1])]; + tensor var_4628_pad_type_0 = const()[name = tensor("op_4628_pad_type_0"), val = tensor("custom")]; + tensor var_4628_pad_0 = const()[name = tensor("op_4628_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1528076992)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531353856)))]; + tensor var_4628_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_4626, groups = var_1186, pad = var_4628_pad_0, pad_type = var_4628_pad_type_0, strides = var_4624, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_293_cast)[name = tensor("op_4628_cast")]; + tensor inputs_135_cast = add(x = var_4628_cast, y = inputs_133_cast)[name = tensor("inputs_135_cast")]; + tensor var_4632 = const()[name = tensor("op_4632"), val = tensor([1])]; + tensor channels_mean_135_cast = reduce_mean(axes = var_4632, keep_dims = var_1181, x = inputs_135_cast)[name = tensor("channels_mean_135_cast")]; + tensor zero_mean_135_cast = sub(x = inputs_135_cast, y = channels_mean_135_cast)[name = tensor("zero_mean_135_cast")]; + tensor zero_mean_sq_135_cast = mul(x = zero_mean_135_cast, y = zero_mean_135_cast)[name = tensor("zero_mean_sq_135_cast")]; + tensor var_4636 = const()[name = tensor("op_4636"), val = tensor([1])]; + tensor var_4637_cast = reduce_mean(axes = var_4636, keep_dims = var_1181, x = zero_mean_sq_135_cast)[name = tensor("op_4637_cast")]; + tensor var_4638_to_fp16 = const()[name = tensor("op_4638_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4639_cast = add(x = var_4637_cast, y = var_4638_to_fp16)[name = tensor("op_4639_cast")]; + tensor denom_135_epsilon_0_to_fp16 = const()[name = tensor("denom_135_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_135_cast = rsqrt(epsilon = denom_135_epsilon_0_to_fp16, x = var_4639_cast)[name = tensor("denom_135_cast")]; + tensor out_135_cast = mul(x = zero_mean_135_cast, y = denom_135_cast)[name = tensor("out_135_cast")]; + tensor var_4643_to_fp16 = const()[name = tensor("op_4643_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531356480)))]; + tensor var_4644_cast = add(x = out_135_cast, y = var_4643_to_fp16)[name = tensor("op_4644_cast")]; + tensor var_4646_to_fp16 = const()[name = tensor("op_4646_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531359104)))]; + tensor hidden_states_187_cast = mul(x = var_4644_cast, y = var_4646_to_fp16)[name = tensor("hidden_states_187_cast")]; + tensor var_4653 = const()[name = tensor("op_4653"), val = tensor([1, 1])]; + tensor var_4655 = const()[name = tensor("op_4655"), val = tensor([1, 1])]; + tensor q_91_pad_type_0 = const()[name = tensor("q_91_pad_type_0"), val = tensor("custom")]; + tensor q_91_pad_0 = const()[name = tensor("q_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531361728)))]; + tensor q_91_cast = conv(dilations = var_4655, groups = var_1186, pad = q_91_pad_0, pad_type = q_91_pad_type_0, strides = var_4653, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_187_cast)[name = tensor("q_91_cast")]; + tensor var_4659 = const()[name = tensor("op_4659"), val = tensor([1, 1])]; + tensor var_4661 = const()[name = tensor("op_4661"), val = tensor([1, 1])]; + tensor k_91_pad_type_0 = const()[name = tensor("k_91_pad_type_0"), val = tensor("custom")]; + tensor k_91_pad_0 = const()[name = tensor("k_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1534638592)))]; + tensor k_91_cast = conv(dilations = var_4661, groups = var_1186, pad = k_91_pad_0, pad_type = k_91_pad_type_0, strides = var_4659, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_91_cast")]; + tensor var_4665 = const()[name = tensor("op_4665"), val = tensor([1, 1])]; + tensor var_4667 = const()[name = tensor("op_4667"), val = tensor([1, 1])]; + tensor v_91_pad_type_0 = const()[name = tensor("v_91_pad_type_0"), val = tensor("custom")]; + tensor v_91_pad_0 = const()[name = tensor("v_91_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1539881536)))]; + tensor v_91_cast = conv(dilations = var_4667, groups = var_1186, pad = v_91_pad_0, pad_type = v_91_pad_type_0, strides = var_4665, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_91_cast")]; + tensor var_4671 = const()[name = tensor("op_4671"), val = tensor([2, 20, 64, -1])]; + tensor var_4672_cast = reshape(shape = var_4671, x = q_91_cast)[name = tensor("op_4672_cast")]; + tensor var_4673 = const()[name = tensor("op_4673"), val = tensor([2, 20, 64, -1])]; + tensor var_4674_cast = reshape(shape = var_4673, x = k_91_cast)[name = tensor("op_4674_cast")]; + tensor var_4675 = const()[name = tensor("op_4675"), val = tensor([2, 20, 64, -1])]; + tensor var_4676_cast = reshape(shape = var_4675, x = v_91_cast)[name = tensor("op_4676_cast")]; + tensor attn_weights_181_transpose_x_0 = const()[name = tensor("attn_weights_181_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_181_transpose_y_0 = const()[name = tensor("attn_weights_181_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_181_cast = matmul(transpose_x = attn_weights_181_transpose_x_0, transpose_y = attn_weights_181_transpose_y_0, x = var_4672_cast, y = var_4674_cast)[name = tensor("attn_weights_181_cast")]; + tensor attn_weights_183_cast = mul(x = attn_weights_181_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_183_cast")]; + tensor var_4680_cast = softmax(axis = var_1170, x = attn_weights_183_cast)[name = tensor("op_4680_cast")]; + tensor attn_91_transpose_x_0 = const()[name = tensor("attn_91_transpose_x_0"), val = tensor(false)]; + tensor attn_91_transpose_y_0 = const()[name = tensor("attn_91_transpose_y_0"), val = tensor(true)]; + tensor attn_91_cast = matmul(transpose_x = attn_91_transpose_x_0, transpose_y = attn_91_transpose_y_0, x = var_4676_cast, y = var_4680_cast)[name = tensor("attn_91_cast")]; + tensor var_4684 = const()[name = tensor("op_4684"), val = tensor([2, 1280, 1, -1])]; + tensor input_295_cast = reshape(shape = var_4684, x = attn_91_cast)[name = tensor("input_295_cast")]; + tensor var_4689 = const()[name = tensor("op_4689"), val = tensor([1, 1])]; + tensor var_4691 = const()[name = tensor("op_4691"), val = tensor([1, 1])]; + tensor var_4693_pad_type_0 = const()[name = tensor("op_4693_pad_type_0"), val = tensor("custom")]; + tensor var_4693_pad_0 = const()[name = tensor("op_4693_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1545124480)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548401344)))]; + tensor var_4693_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_4691, groups = var_1186, pad = var_4693_pad_0, pad_type = var_4693_pad_type_0, strides = var_4689, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_295_cast)[name = tensor("op_4693_cast")]; + tensor inputs_137_cast = add(x = var_4693_cast, y = inputs_135_cast)[name = tensor("inputs_137_cast")]; + tensor var_4697 = const()[name = tensor("op_4697"), val = tensor([1])]; + tensor channels_mean_137_cast = reduce_mean(axes = var_4697, keep_dims = var_1181, x = inputs_137_cast)[name = tensor("channels_mean_137_cast")]; + tensor zero_mean_137_cast = sub(x = inputs_137_cast, y = channels_mean_137_cast)[name = tensor("zero_mean_137_cast")]; + tensor zero_mean_sq_137_cast = mul(x = zero_mean_137_cast, y = zero_mean_137_cast)[name = tensor("zero_mean_sq_137_cast")]; + tensor var_4701 = const()[name = tensor("op_4701"), val = tensor([1])]; + tensor var_4702_cast = reduce_mean(axes = var_4701, keep_dims = var_1181, x = zero_mean_sq_137_cast)[name = tensor("op_4702_cast")]; + tensor var_4703_to_fp16 = const()[name = tensor("op_4703_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4704_cast = add(x = var_4702_cast, y = var_4703_to_fp16)[name = tensor("op_4704_cast")]; + tensor denom_137_epsilon_0_to_fp16 = const()[name = tensor("denom_137_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_137_cast = rsqrt(epsilon = denom_137_epsilon_0_to_fp16, x = var_4704_cast)[name = tensor("denom_137_cast")]; + tensor out_137_cast = mul(x = zero_mean_137_cast, y = denom_137_cast)[name = tensor("out_137_cast")]; + tensor var_4708_to_fp16 = const()[name = tensor("op_4708_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548403968)))]; + tensor var_4709_cast = add(x = out_137_cast, y = var_4708_to_fp16)[name = tensor("op_4709_cast")]; + tensor var_4711_to_fp16 = const()[name = tensor("op_4711_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548406592)))]; + tensor input_297_cast = mul(x = var_4709_cast, y = var_4711_to_fp16)[name = tensor("input_297_cast")]; + tensor var_4719 = const()[name = tensor("op_4719"), val = tensor([1, 1])]; + tensor var_4721 = const()[name = tensor("op_4721"), val = tensor([1, 1])]; + tensor var_4723_pad_type_0 = const()[name = tensor("op_4723_pad_type_0"), val = tensor("custom")]; + tensor var_4723_pad_0 = const()[name = tensor("op_4723_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548409216)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1574623680)))]; + tensor var_4723_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_4721, groups = var_1186, pad = var_4723_pad_0, pad_type = var_4723_pad_type_0, strides = var_4719, weight = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_297_cast)[name = tensor("op_4723_cast")]; + tensor var_4724_split_sizes_0 = const()[name = tensor("op_4724_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4724_axis_0 = const()[name = tensor("op_4724_axis_0"), val = tensor(1)]; + tensor var_4724_cast_0, tensor var_4724_cast_1 = split(axis = var_4724_axis_0, split_sizes = var_4724_split_sizes_0, x = var_4723_cast)[name = tensor("op_4724_cast")]; + tensor var_4726_mode_0 = const()[name = tensor("op_4726_mode_0"), val = tensor("EXACT")]; + tensor var_4726_cast = gelu(mode = var_4726_mode_0, x = var_4724_cast_1)[name = tensor("op_4726_cast")]; + tensor input_299_cast = mul(x = var_4724_cast_0, y = var_4726_cast)[name = tensor("input_299_cast")]; + tensor var_4730 = const()[name = tensor("op_4730"), val = tensor([1, 1])]; + tensor var_4732 = const()[name = tensor("op_4732"), val = tensor([1, 1])]; + tensor var_4734_pad_type_0 = const()[name = tensor("op_4734_pad_type_0"), val = tensor("custom")]; + tensor var_4734_pad_0 = const()[name = tensor("op_4734_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1574644224)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1587751488)))]; + tensor var_4734_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_4732, groups = var_1186, pad = var_4734_pad_0, pad_type = var_4734_pad_type_0, strides = var_4730, weight = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_299_cast)[name = tensor("op_4734_cast")]; + tensor inputs_139_cast = add(x = var_4734_cast, y = inputs_137_cast)[name = tensor("inputs_139_cast")]; + tensor var_4744 = const()[name = tensor("op_4744"), val = tensor([1])]; + tensor channels_mean_139_cast = reduce_mean(axes = var_4744, keep_dims = var_1181, x = inputs_139_cast)[name = tensor("channels_mean_139_cast")]; + tensor zero_mean_139_cast = sub(x = inputs_139_cast, y = channels_mean_139_cast)[name = tensor("zero_mean_139_cast")]; + tensor zero_mean_sq_139_cast = mul(x = zero_mean_139_cast, y = zero_mean_139_cast)[name = tensor("zero_mean_sq_139_cast")]; + tensor var_4748 = const()[name = tensor("op_4748"), val = tensor([1])]; + tensor var_4749_cast = reduce_mean(axes = var_4748, keep_dims = var_1181, x = zero_mean_sq_139_cast)[name = tensor("op_4749_cast")]; + tensor var_4750_to_fp16 = const()[name = tensor("op_4750_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4751_cast = add(x = var_4749_cast, y = var_4750_to_fp16)[name = tensor("op_4751_cast")]; + tensor denom_139_epsilon_0_to_fp16 = const()[name = tensor("denom_139_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_139_cast = rsqrt(epsilon = denom_139_epsilon_0_to_fp16, x = var_4751_cast)[name = tensor("denom_139_cast")]; + tensor out_139_cast = mul(x = zero_mean_139_cast, y = denom_139_cast)[name = tensor("out_139_cast")]; + tensor var_4755_to_fp16 = const()[name = tensor("op_4755_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1587754112)))]; + tensor var_4756_cast = add(x = out_139_cast, y = var_4755_to_fp16)[name = tensor("op_4756_cast")]; + tensor var_4758_to_fp16 = const()[name = tensor("op_4758_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1587756736)))]; + tensor hidden_states_191_cast = mul(x = var_4756_cast, y = var_4758_to_fp16)[name = tensor("hidden_states_191_cast")]; + tensor var_4765 = const()[name = tensor("op_4765"), val = tensor([1, 1])]; + tensor var_4767 = const()[name = tensor("op_4767"), val = tensor([1, 1])]; + tensor q_93_pad_type_0 = const()[name = tensor("q_93_pad_type_0"), val = tensor("custom")]; + tensor q_93_pad_0 = const()[name = tensor("q_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1587759360)))]; + tensor q_93_cast = conv(dilations = var_4767, groups = var_1186, pad = q_93_pad_0, pad_type = q_93_pad_type_0, strides = var_4765, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_191_cast)[name = tensor("q_93_cast")]; + tensor var_4771 = const()[name = tensor("op_4771"), val = tensor([1, 1])]; + tensor var_4773 = const()[name = tensor("op_4773"), val = tensor([1, 1])]; + tensor k_93_pad_type_0 = const()[name = tensor("k_93_pad_type_0"), val = tensor("custom")]; + tensor k_93_pad_0 = const()[name = tensor("k_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591036224)))]; + tensor k_93_cast = conv(dilations = var_4773, groups = var_1186, pad = k_93_pad_0, pad_type = k_93_pad_type_0, strides = var_4771, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_191_cast)[name = tensor("k_93_cast")]; + tensor var_4777 = const()[name = tensor("op_4777"), val = tensor([1, 1])]; + tensor var_4779 = const()[name = tensor("op_4779"), val = tensor([1, 1])]; + tensor v_93_pad_type_0 = const()[name = tensor("v_93_pad_type_0"), val = tensor("custom")]; + tensor v_93_pad_0 = const()[name = tensor("v_93_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1594313088)))]; + tensor v_93_cast = conv(dilations = var_4779, groups = var_1186, pad = v_93_pad_0, pad_type = v_93_pad_type_0, strides = var_4777, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_191_cast)[name = tensor("v_93_cast")]; + tensor var_4783 = const()[name = tensor("op_4783"), val = tensor([2, 20, 64, -1])]; + tensor var_4784_cast = reshape(shape = var_4783, x = q_93_cast)[name = tensor("op_4784_cast")]; + tensor var_4785 = const()[name = tensor("op_4785"), val = tensor([2, 20, 64, -1])]; + tensor var_4786_cast = reshape(shape = var_4785, x = k_93_cast)[name = tensor("op_4786_cast")]; + tensor var_4787 = const()[name = tensor("op_4787"), val = tensor([2, 20, 64, -1])]; + tensor var_4788_cast = reshape(shape = var_4787, x = v_93_cast)[name = tensor("op_4788_cast")]; + tensor attn_weights_185_transpose_x_0 = const()[name = tensor("attn_weights_185_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_185_transpose_y_0 = const()[name = tensor("attn_weights_185_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_185_cast = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4784_cast, y = var_4786_cast)[name = tensor("attn_weights_185_cast")]; + tensor attn_weights_187_cast = mul(x = attn_weights_185_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_187_cast")]; + tensor var_4792_cast = softmax(axis = var_1170, x = attn_weights_187_cast)[name = tensor("op_4792_cast")]; + tensor attn_93_transpose_x_0 = const()[name = tensor("attn_93_transpose_x_0"), val = tensor(false)]; + tensor attn_93_transpose_y_0 = const()[name = tensor("attn_93_transpose_y_0"), val = tensor(true)]; + tensor attn_93_cast = matmul(transpose_x = attn_93_transpose_x_0, transpose_y = attn_93_transpose_y_0, x = var_4788_cast, y = var_4792_cast)[name = tensor("attn_93_cast")]; + tensor var_4796 = const()[name = tensor("op_4796"), val = tensor([2, 1280, 1, -1])]; + tensor input_301_cast = reshape(shape = var_4796, x = attn_93_cast)[name = tensor("input_301_cast")]; + tensor var_4801 = const()[name = tensor("op_4801"), val = tensor([1, 1])]; + tensor var_4803 = const()[name = tensor("op_4803"), val = tensor([1, 1])]; + tensor var_4805_pad_type_0 = const()[name = tensor("op_4805_pad_type_0"), val = tensor("custom")]; + tensor var_4805_pad_0 = const()[name = tensor("op_4805_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1597589952)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1600866816)))]; + tensor var_4805_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_4803, groups = var_1186, pad = var_4805_pad_0, pad_type = var_4805_pad_type_0, strides = var_4801, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_301_cast)[name = tensor("op_4805_cast")]; + tensor inputs_141_cast = add(x = var_4805_cast, y = inputs_139_cast)[name = tensor("inputs_141_cast")]; + tensor var_4809 = const()[name = tensor("op_4809"), val = tensor([1])]; + tensor channels_mean_141_cast = reduce_mean(axes = var_4809, keep_dims = var_1181, x = inputs_141_cast)[name = tensor("channels_mean_141_cast")]; + tensor zero_mean_141_cast = sub(x = inputs_141_cast, y = channels_mean_141_cast)[name = tensor("zero_mean_141_cast")]; + tensor zero_mean_sq_141_cast = mul(x = zero_mean_141_cast, y = zero_mean_141_cast)[name = tensor("zero_mean_sq_141_cast")]; + tensor var_4813 = const()[name = tensor("op_4813"), val = tensor([1])]; + tensor var_4814_cast = reduce_mean(axes = var_4813, keep_dims = var_1181, x = zero_mean_sq_141_cast)[name = tensor("op_4814_cast")]; + tensor var_4815_to_fp16 = const()[name = tensor("op_4815_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4816_cast = add(x = var_4814_cast, y = var_4815_to_fp16)[name = tensor("op_4816_cast")]; + tensor denom_141_epsilon_0_to_fp16 = const()[name = tensor("denom_141_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_141_cast = rsqrt(epsilon = denom_141_epsilon_0_to_fp16, x = var_4816_cast)[name = tensor("denom_141_cast")]; + tensor out_141_cast = mul(x = zero_mean_141_cast, y = denom_141_cast)[name = tensor("out_141_cast")]; + tensor var_4820_to_fp16 = const()[name = tensor("op_4820_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1600869440)))]; + tensor var_4821_cast = add(x = out_141_cast, y = var_4820_to_fp16)[name = tensor("op_4821_cast")]; + tensor var_4823_to_fp16 = const()[name = tensor("op_4823_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1600872064)))]; + tensor hidden_states_193_cast = mul(x = var_4821_cast, y = var_4823_to_fp16)[name = tensor("hidden_states_193_cast")]; + tensor var_4830 = const()[name = tensor("op_4830"), val = tensor([1, 1])]; + tensor var_4832 = const()[name = tensor("op_4832"), val = tensor([1, 1])]; + tensor q_95_pad_type_0 = const()[name = tensor("q_95_pad_type_0"), val = tensor("custom")]; + tensor q_95_pad_0 = const()[name = tensor("q_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1600874688)))]; + tensor q_95_cast = conv(dilations = var_4832, groups = var_1186, pad = q_95_pad_0, pad_type = q_95_pad_type_0, strides = var_4830, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_193_cast)[name = tensor("q_95_cast")]; + tensor var_4836 = const()[name = tensor("op_4836"), val = tensor([1, 1])]; + tensor var_4838 = const()[name = tensor("op_4838"), val = tensor([1, 1])]; + tensor k_95_pad_type_0 = const()[name = tensor("k_95_pad_type_0"), val = tensor("custom")]; + tensor k_95_pad_0 = const()[name = tensor("k_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1604151552)))]; + tensor k_95_cast = conv(dilations = var_4838, groups = var_1186, pad = k_95_pad_0, pad_type = k_95_pad_type_0, strides = var_4836, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_95_cast")]; + tensor var_4842 = const()[name = tensor("op_4842"), val = tensor([1, 1])]; + tensor var_4844 = const()[name = tensor("op_4844"), val = tensor([1, 1])]; + tensor v_95_pad_type_0 = const()[name = tensor("v_95_pad_type_0"), val = tensor("custom")]; + tensor v_95_pad_0 = const()[name = tensor("v_95_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1609394496)))]; + tensor v_95_cast = conv(dilations = var_4844, groups = var_1186, pad = v_95_pad_0, pad_type = v_95_pad_type_0, strides = var_4842, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_95_cast")]; + tensor var_4848 = const()[name = tensor("op_4848"), val = tensor([2, 20, 64, -1])]; + tensor var_4849_cast = reshape(shape = var_4848, x = q_95_cast)[name = tensor("op_4849_cast")]; + tensor var_4850 = const()[name = tensor("op_4850"), val = tensor([2, 20, 64, -1])]; + tensor var_4851_cast = reshape(shape = var_4850, x = k_95_cast)[name = tensor("op_4851_cast")]; + tensor var_4852 = const()[name = tensor("op_4852"), val = tensor([2, 20, 64, -1])]; + tensor var_4853_cast = reshape(shape = var_4852, x = v_95_cast)[name = tensor("op_4853_cast")]; + tensor attn_weights_189_transpose_x_0 = const()[name = tensor("attn_weights_189_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_189_transpose_y_0 = const()[name = tensor("attn_weights_189_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_189_cast = matmul(transpose_x = attn_weights_189_transpose_x_0, transpose_y = attn_weights_189_transpose_y_0, x = var_4849_cast, y = var_4851_cast)[name = tensor("attn_weights_189_cast")]; + tensor attn_weights_191_cast = mul(x = attn_weights_189_cast, y = var_1177_to_fp16)[name = tensor("attn_weights_191_cast")]; + tensor var_4857_cast = softmax(axis = var_1170, x = attn_weights_191_cast)[name = tensor("op_4857_cast")]; + tensor attn_95_transpose_x_0 = const()[name = tensor("attn_95_transpose_x_0"), val = tensor(false)]; + tensor attn_95_transpose_y_0 = const()[name = tensor("attn_95_transpose_y_0"), val = tensor(true)]; + tensor attn_95_cast = matmul(transpose_x = attn_95_transpose_x_0, transpose_y = attn_95_transpose_y_0, x = var_4853_cast, y = var_4857_cast)[name = tensor("attn_95_cast")]; + tensor var_4861 = const()[name = tensor("op_4861"), val = tensor([2, 1280, 1, -1])]; + tensor input_303_cast = reshape(shape = var_4861, x = attn_95_cast)[name = tensor("input_303_cast")]; + tensor var_4866 = const()[name = tensor("op_4866"), val = tensor([1, 1])]; + tensor var_4868 = const()[name = tensor("op_4868"), val = tensor([1, 1])]; + tensor var_4870_pad_type_0 = const()[name = tensor("op_4870_pad_type_0"), val = tensor("custom")]; + tensor var_4870_pad_0 = const()[name = tensor("op_4870_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1614637440)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617914304)))]; + tensor var_4870_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_4868, groups = var_1186, pad = var_4870_pad_0, pad_type = var_4870_pad_type_0, strides = var_4866, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_303_cast)[name = tensor("op_4870_cast")]; + tensor inputs_143_cast = add(x = var_4870_cast, y = inputs_141_cast)[name = tensor("inputs_143_cast")]; + tensor var_4874 = const()[name = tensor("op_4874"), val = tensor([1])]; + tensor channels_mean_143_cast = reduce_mean(axes = var_4874, keep_dims = var_1181, x = inputs_143_cast)[name = tensor("channels_mean_143_cast")]; + tensor zero_mean_143_cast = sub(x = inputs_143_cast, y = channels_mean_143_cast)[name = tensor("zero_mean_143_cast")]; + tensor zero_mean_sq_143_cast = mul(x = zero_mean_143_cast, y = zero_mean_143_cast)[name = tensor("zero_mean_sq_143_cast")]; + tensor var_4878 = const()[name = tensor("op_4878"), val = tensor([1])]; + tensor var_4879_cast = reduce_mean(axes = var_4878, keep_dims = var_1181, x = zero_mean_sq_143_cast)[name = tensor("op_4879_cast")]; + tensor var_4880_to_fp16 = const()[name = tensor("op_4880_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4881_cast = add(x = var_4879_cast, y = var_4880_to_fp16)[name = tensor("op_4881_cast")]; + tensor denom_143_epsilon_0_to_fp16 = const()[name = tensor("denom_143_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_143_cast = rsqrt(epsilon = denom_143_epsilon_0_to_fp16, x = var_4881_cast)[name = tensor("denom_143_cast")]; + tensor out_143_cast = mul(x = zero_mean_143_cast, y = denom_143_cast)[name = tensor("out_143_cast")]; + tensor var_4885_to_fp16 = const()[name = tensor("op_4885_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617916928)))]; + tensor var_4886_cast = add(x = out_143_cast, y = var_4885_to_fp16)[name = tensor("op_4886_cast")]; + tensor var_4888_to_fp16 = const()[name = tensor("op_4888_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617919552)))]; + tensor input_305_cast = mul(x = var_4886_cast, y = var_4888_to_fp16)[name = tensor("input_305_cast")]; + tensor var_4896 = const()[name = tensor("op_4896"), val = tensor([1, 1])]; + tensor var_4898 = const()[name = tensor("op_4898"), val = tensor([1, 1])]; + tensor var_4900_pad_type_0 = const()[name = tensor("op_4900_pad_type_0"), val = tensor("custom")]; + tensor var_4900_pad_0 = const()[name = tensor("op_4900_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617922176)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1644136640)))]; + tensor var_4900_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_4898, groups = var_1186, pad = var_4900_pad_0, pad_type = var_4900_pad_type_0, strides = var_4896, weight = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_305_cast)[name = tensor("op_4900_cast")]; + tensor var_4901_split_sizes_0 = const()[name = tensor("op_4901_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4901_axis_0 = const()[name = tensor("op_4901_axis_0"), val = tensor(1)]; + tensor var_4901_cast_0, tensor var_4901_cast_1 = split(axis = var_4901_axis_0, split_sizes = var_4901_split_sizes_0, x = var_4900_cast)[name = tensor("op_4901_cast")]; + tensor var_4903_mode_0 = const()[name = tensor("op_4903_mode_0"), val = tensor("EXACT")]; + tensor var_4903_cast = gelu(mode = var_4903_mode_0, x = var_4901_cast_1)[name = tensor("op_4903_cast")]; + tensor input_307_cast = mul(x = var_4901_cast_0, y = var_4903_cast)[name = tensor("input_307_cast")]; + tensor var_4907 = const()[name = tensor("op_4907"), val = tensor([1, 1])]; + tensor var_4909 = const()[name = tensor("op_4909"), val = tensor([1, 1])]; + tensor var_4911_pad_type_0 = const()[name = tensor("op_4911_pad_type_0"), val = tensor("custom")]; + tensor var_4911_pad_0 = const()[name = tensor("op_4911_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1644157184)))]; + tensor down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1657264448)))]; + tensor var_4911_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_4909, groups = var_1186, pad = var_4911_pad_0, pad_type = var_4911_pad_type_0, strides = var_4907, weight = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_307_cast)[name = tensor("op_4911_cast")]; + tensor hidden_states_197_cast = add(x = var_4911_cast, y = inputs_143_cast)[name = tensor("hidden_states_197_cast")]; + tensor var_4913 = const()[name = tensor("op_4913"), val = tensor([2, 1280, 32, 32])]; + tensor input_309_cast = reshape(shape = var_4913, x = hidden_states_197_cast)[name = tensor("input_309_cast")]; + tensor var_4917 = const()[name = tensor("op_4917"), val = tensor([1, 1])]; + tensor var_4919 = const()[name = tensor("op_4919"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_type_0 = const()[name = tensor("hidden_states_199_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_199_pad_0 = const()[name = tensor("hidden_states_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1657267072)))]; + tensor down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1660543936)))]; + tensor hidden_states_199_cast = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_4919, groups = var_1186, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_4917, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_309_cast)[name = tensor("hidden_states_199_cast")]; + tensor input_311_cast = add(x = hidden_states_199_cast, y = hidden_states_133_cast)[name = tensor("input_311_cast")]; + tensor var_4927 = const()[name = tensor("op_4927"), val = tensor(3)]; + tensor var_4938 = const()[name = tensor("op_4938"), val = tensor(true)]; + tensor var_4943 = const()[name = tensor("op_4943"), val = tensor(1)]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_64_cast = reshape(shape = reshape_64_shape_0, x = input_311_cast)[name = tensor("reshape_64_cast")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48_cast = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast)[name = tensor("reduce_mean_48_cast")]; + tensor sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast)[name = tensor("sub_32_cast")]; + tensor square_16_cast = square(x = sub_32_cast)[name = tensor("square_16_cast")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50_cast = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast)[name = tensor("reduce_mean_50_cast")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_32_cast = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast")]; + tensor sqrt_16_cast = sqrt(x = add_32_cast)[name = tensor("sqrt_16_cast")]; + tensor real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast)[name = tensor("real_div_16_cast")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast)[name = tensor("reshape_65_cast")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1660546560)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1660549184)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_65_cast)[name = tensor("add_33_cast")]; + tensor input_315_cast = silu(x = add_33_cast)[name = tensor("input_315_cast")]; + tensor var_4961 = const()[name = tensor("op_4961"), val = tensor([1, 1])]; + tensor var_4963 = const()[name = tensor("op_4963"), val = tensor([1, 1])]; + tensor hidden_states_201_pad_type_0 = const()[name = tensor("hidden_states_201_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_201_pad_0 = const()[name = tensor("hidden_states_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1660551808)))]; + tensor mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1690043072)))]; + tensor hidden_states_201_cast = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_4963, groups = var_4943, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_4961, weight = mid_block_resnets_0_conv1_weight_to_fp16, x = input_315_cast)[name = tensor("hidden_states_201_cast")]; + tensor var_4969 = const()[name = tensor("op_4969"), val = tensor([1, 1])]; + tensor var_4971 = const()[name = tensor("op_4971"), val = tensor([1, 1])]; + tensor temb_13_pad_type_0 = const()[name = tensor("temb_13_pad_type_0"), val = tensor("custom")]; + tensor temb_13_pad_0 = const()[name = tensor("temb_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1690045696)))]; + tensor mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693322560)))]; + tensor temb_13_cast = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_4971, groups = var_4943, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_4969, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_13_cast")]; + tensor input_319_cast = add(x = hidden_states_201_cast, y = temb_13_cast)[name = tensor("input_319_cast")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_68_cast = reshape(shape = reshape_68_shape_0, x = input_319_cast)[name = tensor("reshape_68_cast")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51_cast = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast)[name = tensor("reduce_mean_51_cast")]; + tensor sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast)[name = tensor("sub_34_cast")]; + tensor square_17_cast = square(x = sub_34_cast)[name = tensor("square_17_cast")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53_cast = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast)[name = tensor("reduce_mean_53_cast")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_34_cast = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast")]; + tensor sqrt_17_cast = sqrt(x = add_34_cast)[name = tensor("sqrt_17_cast")]; + tensor real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast)[name = tensor("real_div_17_cast")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast)[name = tensor("reshape_69_cast")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693325184)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693327808)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_69_cast)[name = tensor("add_35_cast")]; + tensor input_323_cast = silu(x = add_35_cast)[name = tensor("input_323_cast")]; + tensor var_4981 = const()[name = tensor("op_4981"), val = tensor([1, 1])]; + tensor var_4983 = const()[name = tensor("op_4983"), val = tensor([1, 1])]; + tensor hidden_states_203_pad_type_0 = const()[name = tensor("hidden_states_203_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_203_pad_0 = const()[name = tensor("hidden_states_203_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693330432)))]; + tensor mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722821696)))]; + tensor hidden_states_203_cast = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_4983, groups = var_4943, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = var_4981, weight = mid_block_resnets_0_conv2_weight_to_fp16, x = input_323_cast)[name = tensor("hidden_states_203_cast")]; + tensor hidden_states_205_cast = add(x = input_311_cast, y = hidden_states_203_cast)[name = tensor("hidden_states_205_cast")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_72_cast = reshape(shape = reshape_72_shape_0, x = hidden_states_205_cast)[name = tensor("reshape_72_cast")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast)[name = tensor("reduce_mean_54_cast")]; + tensor sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast)[name = tensor("sub_36_cast")]; + tensor square_18_cast = square(x = sub_36_cast)[name = tensor("square_18_cast")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast)[name = tensor("reduce_mean_56_cast")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_36_cast = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast")]; + tensor sqrt_18_cast = sqrt(x = add_36_cast)[name = tensor("sqrt_18_cast")]; + tensor real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast)[name = tensor("real_div_18_cast")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast)[name = tensor("reshape_73_cast")]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722824320)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722826944)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_37_cast = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_73_cast)[name = tensor("add_37_cast")]; + tensor var_5021 = const()[name = tensor("op_5021"), val = tensor([1, 1])]; + tensor var_5023 = const()[name = tensor("op_5023"), val = tensor([1, 1])]; + tensor hidden_states_207_pad_type_0 = const()[name = tensor("hidden_states_207_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_207_pad_0 = const()[name = tensor("hidden_states_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722829568)))]; + tensor mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726106432)))]; + tensor hidden_states_207_cast = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_5023, groups = var_4943, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = var_5021, weight = mid_block_attentions_0_proj_in_weight_to_fp16, x = add_37_cast)[name = tensor("hidden_states_207_cast")]; + tensor var_5028 = const()[name = tensor("op_5028"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_145_cast = reshape(shape = var_5028, x = hidden_states_207_cast)[name = tensor("inputs_145_cast")]; + tensor var_5038 = const()[name = tensor("op_5038"), val = tensor([1])]; + tensor channels_mean_145_cast = reduce_mean(axes = var_5038, keep_dims = var_4938, x = inputs_145_cast)[name = tensor("channels_mean_145_cast")]; + tensor zero_mean_145_cast = sub(x = inputs_145_cast, y = channels_mean_145_cast)[name = tensor("zero_mean_145_cast")]; + tensor zero_mean_sq_145_cast = mul(x = zero_mean_145_cast, y = zero_mean_145_cast)[name = tensor("zero_mean_sq_145_cast")]; + tensor var_5042 = const()[name = tensor("op_5042"), val = tensor([1])]; + tensor var_5043_cast = reduce_mean(axes = var_5042, keep_dims = var_4938, x = zero_mean_sq_145_cast)[name = tensor("op_5043_cast")]; + tensor var_5044_to_fp16 = const()[name = tensor("op_5044_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5045_cast = add(x = var_5043_cast, y = var_5044_to_fp16)[name = tensor("op_5045_cast")]; + tensor denom_145_epsilon_0_to_fp16 = const()[name = tensor("denom_145_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_145_cast = rsqrt(epsilon = denom_145_epsilon_0_to_fp16, x = var_5045_cast)[name = tensor("denom_145_cast")]; + tensor out_145_cast = mul(x = zero_mean_145_cast, y = denom_145_cast)[name = tensor("out_145_cast")]; + tensor var_5049_to_fp16 = const()[name = tensor("op_5049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726109056)))]; + tensor var_5050_cast = add(x = out_145_cast, y = var_5049_to_fp16)[name = tensor("op_5050_cast")]; + tensor var_5052_to_fp16 = const()[name = tensor("op_5052_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726111680)))]; + tensor hidden_states_209_cast = mul(x = var_5050_cast, y = var_5052_to_fp16)[name = tensor("hidden_states_209_cast")]; + tensor var_5059 = const()[name = tensor("op_5059"), val = tensor([1, 1])]; + tensor var_5061 = const()[name = tensor("op_5061"), val = tensor([1, 1])]; + tensor q_97_pad_type_0 = const()[name = tensor("q_97_pad_type_0"), val = tensor("custom")]; + tensor q_97_pad_0 = const()[name = tensor("q_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726114304)))]; + tensor q_97_cast = conv(dilations = var_5061, groups = var_4943, pad = q_97_pad_0, pad_type = q_97_pad_type_0, strides = var_5059, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_209_cast)[name = tensor("q_97_cast")]; + tensor var_5065 = const()[name = tensor("op_5065"), val = tensor([1, 1])]; + tensor var_5067 = const()[name = tensor("op_5067"), val = tensor([1, 1])]; + tensor k_97_pad_type_0 = const()[name = tensor("k_97_pad_type_0"), val = tensor("custom")]; + tensor k_97_pad_0 = const()[name = tensor("k_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1729391168)))]; + tensor k_97_cast = conv(dilations = var_5067, groups = var_4943, pad = k_97_pad_0, pad_type = k_97_pad_type_0, strides = var_5065, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_209_cast)[name = tensor("k_97_cast")]; + tensor var_5071 = const()[name = tensor("op_5071"), val = tensor([1, 1])]; + tensor var_5073 = const()[name = tensor("op_5073"), val = tensor([1, 1])]; + tensor v_97_pad_type_0 = const()[name = tensor("v_97_pad_type_0"), val = tensor("custom")]; + tensor v_97_pad_0 = const()[name = tensor("v_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1732668032)))]; + tensor v_97_cast = conv(dilations = var_5073, groups = var_4943, pad = v_97_pad_0, pad_type = v_97_pad_type_0, strides = var_5071, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_209_cast)[name = tensor("v_97_cast")]; + tensor var_5077 = const()[name = tensor("op_5077"), val = tensor([2, 20, 64, -1])]; + tensor var_5078_cast = reshape(shape = var_5077, x = q_97_cast)[name = tensor("op_5078_cast")]; + tensor var_5079 = const()[name = tensor("op_5079"), val = tensor([2, 20, 64, -1])]; + tensor var_5080_cast = reshape(shape = var_5079, x = k_97_cast)[name = tensor("op_5080_cast")]; + tensor var_5081 = const()[name = tensor("op_5081"), val = tensor([2, 20, 64, -1])]; + tensor var_5082_cast = reshape(shape = var_5081, x = v_97_cast)[name = tensor("op_5082_cast")]; + tensor attn_weights_193_transpose_x_0 = const()[name = tensor("attn_weights_193_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_193_transpose_y_0 = const()[name = tensor("attn_weights_193_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_193_cast = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_5078_cast, y = var_5080_cast)[name = tensor("attn_weights_193_cast")]; + tensor var_4934_to_fp16 = const()[name = tensor("op_4934_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_195_cast = mul(x = attn_weights_193_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_195_cast")]; + tensor var_5086_cast = softmax(axis = var_4927, x = attn_weights_195_cast)[name = tensor("op_5086_cast")]; + tensor attn_97_transpose_x_0 = const()[name = tensor("attn_97_transpose_x_0"), val = tensor(false)]; + tensor attn_97_transpose_y_0 = const()[name = tensor("attn_97_transpose_y_0"), val = tensor(true)]; + tensor attn_97_cast = matmul(transpose_x = attn_97_transpose_x_0, transpose_y = attn_97_transpose_y_0, x = var_5082_cast, y = var_5086_cast)[name = tensor("attn_97_cast")]; + tensor var_5090 = const()[name = tensor("op_5090"), val = tensor([2, 1280, 1, -1])]; + tensor input_327_cast = reshape(shape = var_5090, x = attn_97_cast)[name = tensor("input_327_cast")]; + tensor var_5095 = const()[name = tensor("op_5095"), val = tensor([1, 1])]; + tensor var_5097 = const()[name = tensor("op_5097"), val = tensor([1, 1])]; + tensor var_5099_pad_type_0 = const()[name = tensor("op_5099_pad_type_0"), val = tensor("custom")]; + tensor var_5099_pad_0 = const()[name = tensor("op_5099_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1735944896)))]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1739221760)))]; + tensor var_5099_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_5097, groups = var_4943, pad = var_5099_pad_0, pad_type = var_5099_pad_type_0, strides = var_5095, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_327_cast)[name = tensor("op_5099_cast")]; + tensor inputs_147_cast = add(x = var_5099_cast, y = inputs_145_cast)[name = tensor("inputs_147_cast")]; + tensor var_5103 = const()[name = tensor("op_5103"), val = tensor([1])]; + tensor channels_mean_147_cast = reduce_mean(axes = var_5103, keep_dims = var_4938, x = inputs_147_cast)[name = tensor("channels_mean_147_cast")]; + tensor zero_mean_147_cast = sub(x = inputs_147_cast, y = channels_mean_147_cast)[name = tensor("zero_mean_147_cast")]; + tensor zero_mean_sq_147_cast = mul(x = zero_mean_147_cast, y = zero_mean_147_cast)[name = tensor("zero_mean_sq_147_cast")]; + tensor var_5107 = const()[name = tensor("op_5107"), val = tensor([1])]; + tensor var_5108_cast = reduce_mean(axes = var_5107, keep_dims = var_4938, x = zero_mean_sq_147_cast)[name = tensor("op_5108_cast")]; + tensor var_5109_to_fp16 = const()[name = tensor("op_5109_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5110_cast = add(x = var_5108_cast, y = var_5109_to_fp16)[name = tensor("op_5110_cast")]; + tensor denom_147_epsilon_0_to_fp16 = const()[name = tensor("denom_147_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_147_cast = rsqrt(epsilon = denom_147_epsilon_0_to_fp16, x = var_5110_cast)[name = tensor("denom_147_cast")]; + tensor out_147_cast = mul(x = zero_mean_147_cast, y = denom_147_cast)[name = tensor("out_147_cast")]; + tensor var_5114_to_fp16 = const()[name = tensor("op_5114_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1739224384)))]; + tensor var_5115_cast = add(x = out_147_cast, y = var_5114_to_fp16)[name = tensor("op_5115_cast")]; + tensor var_5117_to_fp16 = const()[name = tensor("op_5117_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1739227008)))]; + tensor hidden_states_211_cast = mul(x = var_5115_cast, y = var_5117_to_fp16)[name = tensor("hidden_states_211_cast")]; + tensor var_5124 = const()[name = tensor("op_5124"), val = tensor([1, 1])]; + tensor var_5126 = const()[name = tensor("op_5126"), val = tensor([1, 1])]; + tensor q_99_pad_type_0 = const()[name = tensor("q_99_pad_type_0"), val = tensor("custom")]; + tensor q_99_pad_0 = const()[name = tensor("q_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1739229632)))]; + tensor q_99_cast = conv(dilations = var_5126, groups = var_4943, pad = q_99_pad_0, pad_type = q_99_pad_type_0, strides = var_5124, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_211_cast)[name = tensor("q_99_cast")]; + tensor var_5130 = const()[name = tensor("op_5130"), val = tensor([1, 1])]; + tensor var_5132 = const()[name = tensor("op_5132"), val = tensor([1, 1])]; + tensor k_99_pad_type_0 = const()[name = tensor("k_99_pad_type_0"), val = tensor("custom")]; + tensor k_99_pad_0 = const()[name = tensor("k_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1742506496)))]; + tensor k_99_cast = conv(dilations = var_5132, groups = var_4943, pad = k_99_pad_0, pad_type = k_99_pad_type_0, strides = var_5130, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_99_cast")]; + tensor var_5136 = const()[name = tensor("op_5136"), val = tensor([1, 1])]; + tensor var_5138 = const()[name = tensor("op_5138"), val = tensor([1, 1])]; + tensor v_99_pad_type_0 = const()[name = tensor("v_99_pad_type_0"), val = tensor("custom")]; + tensor v_99_pad_0 = const()[name = tensor("v_99_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1747749440)))]; + tensor v_99_cast = conv(dilations = var_5138, groups = var_4943, pad = v_99_pad_0, pad_type = v_99_pad_type_0, strides = var_5136, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_99_cast")]; + tensor var_5142 = const()[name = tensor("op_5142"), val = tensor([2, 20, 64, -1])]; + tensor var_5143_cast = reshape(shape = var_5142, x = q_99_cast)[name = tensor("op_5143_cast")]; + tensor var_5144 = const()[name = tensor("op_5144"), val = tensor([2, 20, 64, -1])]; + tensor var_5145_cast = reshape(shape = var_5144, x = k_99_cast)[name = tensor("op_5145_cast")]; + tensor var_5146 = const()[name = tensor("op_5146"), val = tensor([2, 20, 64, -1])]; + tensor var_5147_cast = reshape(shape = var_5146, x = v_99_cast)[name = tensor("op_5147_cast")]; + tensor attn_weights_197_transpose_x_0 = const()[name = tensor("attn_weights_197_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_197_transpose_y_0 = const()[name = tensor("attn_weights_197_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_197_cast = matmul(transpose_x = attn_weights_197_transpose_x_0, transpose_y = attn_weights_197_transpose_y_0, x = var_5143_cast, y = var_5145_cast)[name = tensor("attn_weights_197_cast")]; + tensor attn_weights_199_cast = mul(x = attn_weights_197_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_199_cast")]; + tensor var_5151_cast = softmax(axis = var_4927, x = attn_weights_199_cast)[name = tensor("op_5151_cast")]; + tensor attn_99_transpose_x_0 = const()[name = tensor("attn_99_transpose_x_0"), val = tensor(false)]; + tensor attn_99_transpose_y_0 = const()[name = tensor("attn_99_transpose_y_0"), val = tensor(true)]; + tensor attn_99_cast = matmul(transpose_x = attn_99_transpose_x_0, transpose_y = attn_99_transpose_y_0, x = var_5147_cast, y = var_5151_cast)[name = tensor("attn_99_cast")]; + tensor var_5155 = const()[name = tensor("op_5155"), val = tensor([2, 1280, 1, -1])]; + tensor input_329_cast = reshape(shape = var_5155, x = attn_99_cast)[name = tensor("input_329_cast")]; + tensor var_5160 = const()[name = tensor("op_5160"), val = tensor([1, 1])]; + tensor var_5162 = const()[name = tensor("op_5162"), val = tensor([1, 1])]; + tensor var_5164_pad_type_0 = const()[name = tensor("op_5164_pad_type_0"), val = tensor("custom")]; + tensor var_5164_pad_0 = const()[name = tensor("op_5164_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1752992384)))]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756269248)))]; + tensor var_5164_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_5162, groups = var_4943, pad = var_5164_pad_0, pad_type = var_5164_pad_type_0, strides = var_5160, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_329_cast)[name = tensor("op_5164_cast")]; + tensor inputs_149_cast = add(x = var_5164_cast, y = inputs_147_cast)[name = tensor("inputs_149_cast")]; + tensor var_5168 = const()[name = tensor("op_5168"), val = tensor([1])]; + tensor channels_mean_149_cast = reduce_mean(axes = var_5168, keep_dims = var_4938, x = inputs_149_cast)[name = tensor("channels_mean_149_cast")]; + tensor zero_mean_149_cast = sub(x = inputs_149_cast, y = channels_mean_149_cast)[name = tensor("zero_mean_149_cast")]; + tensor zero_mean_sq_149_cast = mul(x = zero_mean_149_cast, y = zero_mean_149_cast)[name = tensor("zero_mean_sq_149_cast")]; + tensor var_5172 = const()[name = tensor("op_5172"), val = tensor([1])]; + tensor var_5173_cast = reduce_mean(axes = var_5172, keep_dims = var_4938, x = zero_mean_sq_149_cast)[name = tensor("op_5173_cast")]; + tensor var_5174_to_fp16 = const()[name = tensor("op_5174_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5175_cast = add(x = var_5173_cast, y = var_5174_to_fp16)[name = tensor("op_5175_cast")]; + tensor denom_149_epsilon_0_to_fp16 = const()[name = tensor("denom_149_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_149_cast = rsqrt(epsilon = denom_149_epsilon_0_to_fp16, x = var_5175_cast)[name = tensor("denom_149_cast")]; + tensor out_149_cast = mul(x = zero_mean_149_cast, y = denom_149_cast)[name = tensor("out_149_cast")]; + tensor var_5179_to_fp16 = const()[name = tensor("op_5179_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756271872)))]; + tensor var_5180_cast = add(x = out_149_cast, y = var_5179_to_fp16)[name = tensor("op_5180_cast")]; + tensor var_5182_to_fp16 = const()[name = tensor("op_5182_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756274496)))]; + tensor input_331_cast = mul(x = var_5180_cast, y = var_5182_to_fp16)[name = tensor("input_331_cast")]; + tensor var_5190 = const()[name = tensor("op_5190"), val = tensor([1, 1])]; + tensor var_5192 = const()[name = tensor("op_5192"), val = tensor([1, 1])]; + tensor var_5194_pad_type_0 = const()[name = tensor("op_5194_pad_type_0"), val = tensor("custom")]; + tensor var_5194_pad_0 = const()[name = tensor("op_5194_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756277120)))]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1782491584)))]; + tensor var_5194_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_5192, groups = var_4943, pad = var_5194_pad_0, pad_type = var_5194_pad_type_0, strides = var_5190, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_331_cast)[name = tensor("op_5194_cast")]; + tensor var_5195_split_sizes_0 = const()[name = tensor("op_5195_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5195_axis_0 = const()[name = tensor("op_5195_axis_0"), val = tensor(1)]; + tensor var_5195_cast_0, tensor var_5195_cast_1 = split(axis = var_5195_axis_0, split_sizes = var_5195_split_sizes_0, x = var_5194_cast)[name = tensor("op_5195_cast")]; + tensor var_5197_mode_0 = const()[name = tensor("op_5197_mode_0"), val = tensor("EXACT")]; + tensor var_5197_cast = gelu(mode = var_5197_mode_0, x = var_5195_cast_1)[name = tensor("op_5197_cast")]; + tensor input_333_cast = mul(x = var_5195_cast_0, y = var_5197_cast)[name = tensor("input_333_cast")]; + tensor var_5201 = const()[name = tensor("op_5201"), val = tensor([1, 1])]; + tensor var_5203 = const()[name = tensor("op_5203"), val = tensor([1, 1])]; + tensor var_5205_pad_type_0 = const()[name = tensor("op_5205_pad_type_0"), val = tensor("custom")]; + tensor var_5205_pad_0 = const()[name = tensor("op_5205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1782512128)))]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795619392)))]; + tensor var_5205_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_5203, groups = var_4943, pad = var_5205_pad_0, pad_type = var_5205_pad_type_0, strides = var_5201, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_333_cast)[name = tensor("op_5205_cast")]; + tensor inputs_151_cast = add(x = var_5205_cast, y = inputs_149_cast)[name = tensor("inputs_151_cast")]; + tensor var_5215 = const()[name = tensor("op_5215"), val = tensor([1])]; + tensor channels_mean_151_cast = reduce_mean(axes = var_5215, keep_dims = var_4938, x = inputs_151_cast)[name = tensor("channels_mean_151_cast")]; + tensor zero_mean_151_cast = sub(x = inputs_151_cast, y = channels_mean_151_cast)[name = tensor("zero_mean_151_cast")]; + tensor zero_mean_sq_151_cast = mul(x = zero_mean_151_cast, y = zero_mean_151_cast)[name = tensor("zero_mean_sq_151_cast")]; + tensor var_5219 = const()[name = tensor("op_5219"), val = tensor([1])]; + tensor var_5220_cast = reduce_mean(axes = var_5219, keep_dims = var_4938, x = zero_mean_sq_151_cast)[name = tensor("op_5220_cast")]; + tensor var_5221_to_fp16 = const()[name = tensor("op_5221_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5222_cast = add(x = var_5220_cast, y = var_5221_to_fp16)[name = tensor("op_5222_cast")]; + tensor denom_151_epsilon_0_to_fp16 = const()[name = tensor("denom_151_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_151_cast = rsqrt(epsilon = denom_151_epsilon_0_to_fp16, x = var_5222_cast)[name = tensor("denom_151_cast")]; + tensor out_151_cast = mul(x = zero_mean_151_cast, y = denom_151_cast)[name = tensor("out_151_cast")]; + tensor var_5226_to_fp16 = const()[name = tensor("op_5226_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795622016)))]; + tensor var_5227_cast = add(x = out_151_cast, y = var_5226_to_fp16)[name = tensor("op_5227_cast")]; + tensor var_5229_to_fp16 = const()[name = tensor("op_5229_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795624640)))]; + tensor hidden_states_215_cast = mul(x = var_5227_cast, y = var_5229_to_fp16)[name = tensor("hidden_states_215_cast")]; + tensor var_5236 = const()[name = tensor("op_5236"), val = tensor([1, 1])]; + tensor var_5238 = const()[name = tensor("op_5238"), val = tensor([1, 1])]; + tensor q_101_pad_type_0 = const()[name = tensor("q_101_pad_type_0"), val = tensor("custom")]; + tensor q_101_pad_0 = const()[name = tensor("q_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795627264)))]; + tensor q_101_cast = conv(dilations = var_5238, groups = var_4943, pad = q_101_pad_0, pad_type = q_101_pad_type_0, strides = var_5236, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_215_cast)[name = tensor("q_101_cast")]; + tensor var_5242 = const()[name = tensor("op_5242"), val = tensor([1, 1])]; + tensor var_5244 = const()[name = tensor("op_5244"), val = tensor([1, 1])]; + tensor k_101_pad_type_0 = const()[name = tensor("k_101_pad_type_0"), val = tensor("custom")]; + tensor k_101_pad_0 = const()[name = tensor("k_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1798904128)))]; + tensor k_101_cast = conv(dilations = var_5244, groups = var_4943, pad = k_101_pad_0, pad_type = k_101_pad_type_0, strides = var_5242, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_215_cast)[name = tensor("k_101_cast")]; + tensor var_5248 = const()[name = tensor("op_5248"), val = tensor([1, 1])]; + tensor var_5250 = const()[name = tensor("op_5250"), val = tensor([1, 1])]; + tensor v_101_pad_type_0 = const()[name = tensor("v_101_pad_type_0"), val = tensor("custom")]; + tensor v_101_pad_0 = const()[name = tensor("v_101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1802180992)))]; + tensor v_101_cast = conv(dilations = var_5250, groups = var_4943, pad = v_101_pad_0, pad_type = v_101_pad_type_0, strides = var_5248, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_215_cast)[name = tensor("v_101_cast")]; + tensor var_5254 = const()[name = tensor("op_5254"), val = tensor([2, 20, 64, -1])]; + tensor var_5255_cast = reshape(shape = var_5254, x = q_101_cast)[name = tensor("op_5255_cast")]; + tensor var_5256 = const()[name = tensor("op_5256"), val = tensor([2, 20, 64, -1])]; + tensor var_5257_cast = reshape(shape = var_5256, x = k_101_cast)[name = tensor("op_5257_cast")]; + tensor var_5258 = const()[name = tensor("op_5258"), val = tensor([2, 20, 64, -1])]; + tensor var_5259_cast = reshape(shape = var_5258, x = v_101_cast)[name = tensor("op_5259_cast")]; + tensor attn_weights_201_transpose_x_0 = const()[name = tensor("attn_weights_201_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_201_transpose_y_0 = const()[name = tensor("attn_weights_201_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_201_cast = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_5255_cast, y = var_5257_cast)[name = tensor("attn_weights_201_cast")]; + tensor attn_weights_203_cast = mul(x = attn_weights_201_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_203_cast")]; + tensor var_5263_cast = softmax(axis = var_4927, x = attn_weights_203_cast)[name = tensor("op_5263_cast")]; + tensor attn_101_transpose_x_0 = const()[name = tensor("attn_101_transpose_x_0"), val = tensor(false)]; + tensor attn_101_transpose_y_0 = const()[name = tensor("attn_101_transpose_y_0"), val = tensor(true)]; + tensor attn_101_cast = matmul(transpose_x = attn_101_transpose_x_0, transpose_y = attn_101_transpose_y_0, x = var_5259_cast, y = var_5263_cast)[name = tensor("attn_101_cast")]; + tensor var_5267 = const()[name = tensor("op_5267"), val = tensor([2, 1280, 1, -1])]; + tensor input_335_cast = reshape(shape = var_5267, x = attn_101_cast)[name = tensor("input_335_cast")]; + tensor var_5272 = const()[name = tensor("op_5272"), val = tensor([1, 1])]; + tensor var_5274 = const()[name = tensor("op_5274"), val = tensor([1, 1])]; + tensor var_5276_pad_type_0 = const()[name = tensor("op_5276_pad_type_0"), val = tensor("custom")]; + tensor var_5276_pad_0 = const()[name = tensor("op_5276_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805457856)))]; + tensor mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808734720)))]; + tensor var_5276_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_5274, groups = var_4943, pad = var_5276_pad_0, pad_type = var_5276_pad_type_0, strides = var_5272, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_335_cast)[name = tensor("op_5276_cast")]; + tensor inputs_153_cast = add(x = var_5276_cast, y = inputs_151_cast)[name = tensor("inputs_153_cast")]; + tensor var_5280 = const()[name = tensor("op_5280"), val = tensor([1])]; + tensor channels_mean_153_cast = reduce_mean(axes = var_5280, keep_dims = var_4938, x = inputs_153_cast)[name = tensor("channels_mean_153_cast")]; + tensor zero_mean_153_cast = sub(x = inputs_153_cast, y = channels_mean_153_cast)[name = tensor("zero_mean_153_cast")]; + tensor zero_mean_sq_153_cast = mul(x = zero_mean_153_cast, y = zero_mean_153_cast)[name = tensor("zero_mean_sq_153_cast")]; + tensor var_5284 = const()[name = tensor("op_5284"), val = tensor([1])]; + tensor var_5285_cast = reduce_mean(axes = var_5284, keep_dims = var_4938, x = zero_mean_sq_153_cast)[name = tensor("op_5285_cast")]; + tensor var_5286_to_fp16 = const()[name = tensor("op_5286_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5287_cast = add(x = var_5285_cast, y = var_5286_to_fp16)[name = tensor("op_5287_cast")]; + tensor denom_153_epsilon_0_to_fp16 = const()[name = tensor("denom_153_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_153_cast = rsqrt(epsilon = denom_153_epsilon_0_to_fp16, x = var_5287_cast)[name = tensor("denom_153_cast")]; + tensor out_153_cast = mul(x = zero_mean_153_cast, y = denom_153_cast)[name = tensor("out_153_cast")]; + tensor var_5291_to_fp16 = const()[name = tensor("op_5291_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808737344)))]; + tensor var_5292_cast = add(x = out_153_cast, y = var_5291_to_fp16)[name = tensor("op_5292_cast")]; + tensor var_5294_to_fp16 = const()[name = tensor("op_5294_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808739968)))]; + tensor hidden_states_217_cast = mul(x = var_5292_cast, y = var_5294_to_fp16)[name = tensor("hidden_states_217_cast")]; + tensor var_5301 = const()[name = tensor("op_5301"), val = tensor([1, 1])]; + tensor var_5303 = const()[name = tensor("op_5303"), val = tensor([1, 1])]; + tensor q_103_pad_type_0 = const()[name = tensor("q_103_pad_type_0"), val = tensor("custom")]; + tensor q_103_pad_0 = const()[name = tensor("q_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808742592)))]; + tensor q_103_cast = conv(dilations = var_5303, groups = var_4943, pad = q_103_pad_0, pad_type = q_103_pad_type_0, strides = var_5301, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_217_cast)[name = tensor("q_103_cast")]; + tensor var_5307 = const()[name = tensor("op_5307"), val = tensor([1, 1])]; + tensor var_5309 = const()[name = tensor("op_5309"), val = tensor([1, 1])]; + tensor k_103_pad_type_0 = const()[name = tensor("k_103_pad_type_0"), val = tensor("custom")]; + tensor k_103_pad_0 = const()[name = tensor("k_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1812019456)))]; + tensor k_103_cast = conv(dilations = var_5309, groups = var_4943, pad = k_103_pad_0, pad_type = k_103_pad_type_0, strides = var_5307, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_103_cast")]; + tensor var_5313 = const()[name = tensor("op_5313"), val = tensor([1, 1])]; + tensor var_5315 = const()[name = tensor("op_5315"), val = tensor([1, 1])]; + tensor v_103_pad_type_0 = const()[name = tensor("v_103_pad_type_0"), val = tensor("custom")]; + tensor v_103_pad_0 = const()[name = tensor("v_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1817262400)))]; + tensor v_103_cast = conv(dilations = var_5315, groups = var_4943, pad = v_103_pad_0, pad_type = v_103_pad_type_0, strides = var_5313, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_103_cast")]; + tensor var_5319 = const()[name = tensor("op_5319"), val = tensor([2, 20, 64, -1])]; + tensor var_5320_cast = reshape(shape = var_5319, x = q_103_cast)[name = tensor("op_5320_cast")]; + tensor var_5321 = const()[name = tensor("op_5321"), val = tensor([2, 20, 64, -1])]; + tensor var_5322_cast = reshape(shape = var_5321, x = k_103_cast)[name = tensor("op_5322_cast")]; + tensor var_5323 = const()[name = tensor("op_5323"), val = tensor([2, 20, 64, -1])]; + tensor var_5324_cast = reshape(shape = var_5323, x = v_103_cast)[name = tensor("op_5324_cast")]; + tensor attn_weights_205_transpose_x_0 = const()[name = tensor("attn_weights_205_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_205_transpose_y_0 = const()[name = tensor("attn_weights_205_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_205_cast = matmul(transpose_x = attn_weights_205_transpose_x_0, transpose_y = attn_weights_205_transpose_y_0, x = var_5320_cast, y = var_5322_cast)[name = tensor("attn_weights_205_cast")]; + tensor attn_weights_207_cast = mul(x = attn_weights_205_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_207_cast")]; + tensor var_5328_cast = softmax(axis = var_4927, x = attn_weights_207_cast)[name = tensor("op_5328_cast")]; + tensor attn_103_transpose_x_0 = const()[name = tensor("attn_103_transpose_x_0"), val = tensor(false)]; + tensor attn_103_transpose_y_0 = const()[name = tensor("attn_103_transpose_y_0"), val = tensor(true)]; + tensor attn_103_cast = matmul(transpose_x = attn_103_transpose_x_0, transpose_y = attn_103_transpose_y_0, x = var_5324_cast, y = var_5328_cast)[name = tensor("attn_103_cast")]; + tensor var_5332 = const()[name = tensor("op_5332"), val = tensor([2, 1280, 1, -1])]; + tensor input_337_cast = reshape(shape = var_5332, x = attn_103_cast)[name = tensor("input_337_cast")]; + tensor var_5337 = const()[name = tensor("op_5337"), val = tensor([1, 1])]; + tensor var_5339 = const()[name = tensor("op_5339"), val = tensor([1, 1])]; + tensor var_5341_pad_type_0 = const()[name = tensor("op_5341_pad_type_0"), val = tensor("custom")]; + tensor var_5341_pad_0 = const()[name = tensor("op_5341_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1822505344)))]; + tensor mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1825782208)))]; + tensor var_5341_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_5339, groups = var_4943, pad = var_5341_pad_0, pad_type = var_5341_pad_type_0, strides = var_5337, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_337_cast)[name = tensor("op_5341_cast")]; + tensor inputs_155_cast = add(x = var_5341_cast, y = inputs_153_cast)[name = tensor("inputs_155_cast")]; + tensor var_5345 = const()[name = tensor("op_5345"), val = tensor([1])]; + tensor channels_mean_155_cast = reduce_mean(axes = var_5345, keep_dims = var_4938, x = inputs_155_cast)[name = tensor("channels_mean_155_cast")]; + tensor zero_mean_155_cast = sub(x = inputs_155_cast, y = channels_mean_155_cast)[name = tensor("zero_mean_155_cast")]; + tensor zero_mean_sq_155_cast = mul(x = zero_mean_155_cast, y = zero_mean_155_cast)[name = tensor("zero_mean_sq_155_cast")]; + tensor var_5349 = const()[name = tensor("op_5349"), val = tensor([1])]; + tensor var_5350_cast = reduce_mean(axes = var_5349, keep_dims = var_4938, x = zero_mean_sq_155_cast)[name = tensor("op_5350_cast")]; + tensor var_5351_to_fp16 = const()[name = tensor("op_5351_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5352_cast = add(x = var_5350_cast, y = var_5351_to_fp16)[name = tensor("op_5352_cast")]; + tensor denom_155_epsilon_0_to_fp16 = const()[name = tensor("denom_155_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_155_cast = rsqrt(epsilon = denom_155_epsilon_0_to_fp16, x = var_5352_cast)[name = tensor("denom_155_cast")]; + tensor out_155_cast = mul(x = zero_mean_155_cast, y = denom_155_cast)[name = tensor("out_155_cast")]; + tensor var_5356_to_fp16 = const()[name = tensor("op_5356_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1825784832)))]; + tensor var_5357_cast = add(x = out_155_cast, y = var_5356_to_fp16)[name = tensor("op_5357_cast")]; + tensor var_5359_to_fp16 = const()[name = tensor("op_5359_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1825787456)))]; + tensor input_339_cast = mul(x = var_5357_cast, y = var_5359_to_fp16)[name = tensor("input_339_cast")]; + tensor var_5367 = const()[name = tensor("op_5367"), val = tensor([1, 1])]; + tensor var_5369 = const()[name = tensor("op_5369"), val = tensor([1, 1])]; + tensor var_5371_pad_type_0 = const()[name = tensor("op_5371_pad_type_0"), val = tensor("custom")]; + tensor var_5371_pad_0 = const()[name = tensor("op_5371_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1825790080)))]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852004544)))]; + tensor var_5371_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_5369, groups = var_4943, pad = var_5371_pad_0, pad_type = var_5371_pad_type_0, strides = var_5367, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_339_cast)[name = tensor("op_5371_cast")]; + tensor var_5372_split_sizes_0 = const()[name = tensor("op_5372_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5372_axis_0 = const()[name = tensor("op_5372_axis_0"), val = tensor(1)]; + tensor var_5372_cast_0, tensor var_5372_cast_1 = split(axis = var_5372_axis_0, split_sizes = var_5372_split_sizes_0, x = var_5371_cast)[name = tensor("op_5372_cast")]; + tensor var_5374_mode_0 = const()[name = tensor("op_5374_mode_0"), val = tensor("EXACT")]; + tensor var_5374_cast = gelu(mode = var_5374_mode_0, x = var_5372_cast_1)[name = tensor("op_5374_cast")]; + tensor input_341_cast = mul(x = var_5372_cast_0, y = var_5374_cast)[name = tensor("input_341_cast")]; + tensor var_5378 = const()[name = tensor("op_5378"), val = tensor([1, 1])]; + tensor var_5380 = const()[name = tensor("op_5380"), val = tensor([1, 1])]; + tensor var_5382_pad_type_0 = const()[name = tensor("op_5382_pad_type_0"), val = tensor("custom")]; + tensor var_5382_pad_0 = const()[name = tensor("op_5382_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852025088)))]; + tensor mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865132352)))]; + tensor var_5382_cast = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_5380, groups = var_4943, pad = var_5382_pad_0, pad_type = var_5382_pad_type_0, strides = var_5378, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_341_cast)[name = tensor("op_5382_cast")]; + tensor inputs_157_cast = add(x = var_5382_cast, y = inputs_155_cast)[name = tensor("inputs_157_cast")]; + tensor var_5392 = const()[name = tensor("op_5392"), val = tensor([1])]; + tensor channels_mean_157_cast = reduce_mean(axes = var_5392, keep_dims = var_4938, x = inputs_157_cast)[name = tensor("channels_mean_157_cast")]; + tensor zero_mean_157_cast = sub(x = inputs_157_cast, y = channels_mean_157_cast)[name = tensor("zero_mean_157_cast")]; + tensor zero_mean_sq_157_cast = mul(x = zero_mean_157_cast, y = zero_mean_157_cast)[name = tensor("zero_mean_sq_157_cast")]; + tensor var_5396 = const()[name = tensor("op_5396"), val = tensor([1])]; + tensor var_5397_cast = reduce_mean(axes = var_5396, keep_dims = var_4938, x = zero_mean_sq_157_cast)[name = tensor("op_5397_cast")]; + tensor var_5398_to_fp16 = const()[name = tensor("op_5398_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5399_cast = add(x = var_5397_cast, y = var_5398_to_fp16)[name = tensor("op_5399_cast")]; + tensor denom_157_epsilon_0_to_fp16 = const()[name = tensor("denom_157_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_157_cast = rsqrt(epsilon = denom_157_epsilon_0_to_fp16, x = var_5399_cast)[name = tensor("denom_157_cast")]; + tensor out_157_cast = mul(x = zero_mean_157_cast, y = denom_157_cast)[name = tensor("out_157_cast")]; + tensor var_5403_to_fp16 = const()[name = tensor("op_5403_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865134976)))]; + tensor var_5404_cast = add(x = out_157_cast, y = var_5403_to_fp16)[name = tensor("op_5404_cast")]; + tensor var_5406_to_fp16 = const()[name = tensor("op_5406_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865137600)))]; + tensor hidden_states_221_cast = mul(x = var_5404_cast, y = var_5406_to_fp16)[name = tensor("hidden_states_221_cast")]; + tensor var_5413 = const()[name = tensor("op_5413"), val = tensor([1, 1])]; + tensor var_5415 = const()[name = tensor("op_5415"), val = tensor([1, 1])]; + tensor q_105_pad_type_0 = const()[name = tensor("q_105_pad_type_0"), val = tensor("custom")]; + tensor q_105_pad_0 = const()[name = tensor("q_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865140224)))]; + tensor q_105_cast = conv(dilations = var_5415, groups = var_4943, pad = q_105_pad_0, pad_type = q_105_pad_type_0, strides = var_5413, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_221_cast)[name = tensor("q_105_cast")]; + tensor var_5419 = const()[name = tensor("op_5419"), val = tensor([1, 1])]; + tensor var_5421 = const()[name = tensor("op_5421"), val = tensor([1, 1])]; + tensor k_105_pad_type_0 = const()[name = tensor("k_105_pad_type_0"), val = tensor("custom")]; + tensor k_105_pad_0 = const()[name = tensor("k_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1868417088)))]; + tensor k_105_cast = conv(dilations = var_5421, groups = var_4943, pad = k_105_pad_0, pad_type = k_105_pad_type_0, strides = var_5419, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_221_cast)[name = tensor("k_105_cast")]; + tensor var_5425 = const()[name = tensor("op_5425"), val = tensor([1, 1])]; + tensor var_5427 = const()[name = tensor("op_5427"), val = tensor([1, 1])]; + tensor v_105_pad_type_0 = const()[name = tensor("v_105_pad_type_0"), val = tensor("custom")]; + tensor v_105_pad_0 = const()[name = tensor("v_105_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1871693952)))]; + tensor v_105_cast = conv(dilations = var_5427, groups = var_4943, pad = v_105_pad_0, pad_type = v_105_pad_type_0, strides = var_5425, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_221_cast)[name = tensor("v_105_cast")]; + tensor var_5431 = const()[name = tensor("op_5431"), val = tensor([2, 20, 64, -1])]; + tensor var_5432_cast = reshape(shape = var_5431, x = q_105_cast)[name = tensor("op_5432_cast")]; + tensor var_5433 = const()[name = tensor("op_5433"), val = tensor([2, 20, 64, -1])]; + tensor var_5434_cast = reshape(shape = var_5433, x = k_105_cast)[name = tensor("op_5434_cast")]; + tensor var_5435 = const()[name = tensor("op_5435"), val = tensor([2, 20, 64, -1])]; + tensor var_5436_cast = reshape(shape = var_5435, x = v_105_cast)[name = tensor("op_5436_cast")]; + tensor attn_weights_209_transpose_x_0 = const()[name = tensor("attn_weights_209_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_209_transpose_y_0 = const()[name = tensor("attn_weights_209_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_209_cast = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5432_cast, y = var_5434_cast)[name = tensor("attn_weights_209_cast")]; + tensor attn_weights_211_cast = mul(x = attn_weights_209_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_211_cast")]; + tensor var_5440_cast = softmax(axis = var_4927, x = attn_weights_211_cast)[name = tensor("op_5440_cast")]; + tensor attn_105_transpose_x_0 = const()[name = tensor("attn_105_transpose_x_0"), val = tensor(false)]; + tensor attn_105_transpose_y_0 = const()[name = tensor("attn_105_transpose_y_0"), val = tensor(true)]; + tensor attn_105_cast = matmul(transpose_x = attn_105_transpose_x_0, transpose_y = attn_105_transpose_y_0, x = var_5436_cast, y = var_5440_cast)[name = tensor("attn_105_cast")]; + tensor var_5444 = const()[name = tensor("op_5444"), val = tensor([2, 1280, 1, -1])]; + tensor input_343_cast = reshape(shape = var_5444, x = attn_105_cast)[name = tensor("input_343_cast")]; + tensor var_5449 = const()[name = tensor("op_5449"), val = tensor([1, 1])]; + tensor var_5451 = const()[name = tensor("op_5451"), val = tensor([1, 1])]; + tensor var_5453_pad_type_0 = const()[name = tensor("op_5453_pad_type_0"), val = tensor("custom")]; + tensor var_5453_pad_0 = const()[name = tensor("op_5453_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1874970816)))]; + tensor mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878247680)))]; + tensor var_5453_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_5451, groups = var_4943, pad = var_5453_pad_0, pad_type = var_5453_pad_type_0, strides = var_5449, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_343_cast)[name = tensor("op_5453_cast")]; + tensor inputs_159_cast = add(x = var_5453_cast, y = inputs_157_cast)[name = tensor("inputs_159_cast")]; + tensor var_5457 = const()[name = tensor("op_5457"), val = tensor([1])]; + tensor channels_mean_159_cast = reduce_mean(axes = var_5457, keep_dims = var_4938, x = inputs_159_cast)[name = tensor("channels_mean_159_cast")]; + tensor zero_mean_159_cast = sub(x = inputs_159_cast, y = channels_mean_159_cast)[name = tensor("zero_mean_159_cast")]; + tensor zero_mean_sq_159_cast = mul(x = zero_mean_159_cast, y = zero_mean_159_cast)[name = tensor("zero_mean_sq_159_cast")]; + tensor var_5461 = const()[name = tensor("op_5461"), val = tensor([1])]; + tensor var_5462_cast = reduce_mean(axes = var_5461, keep_dims = var_4938, x = zero_mean_sq_159_cast)[name = tensor("op_5462_cast")]; + tensor var_5463_to_fp16 = const()[name = tensor("op_5463_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5464_cast = add(x = var_5462_cast, y = var_5463_to_fp16)[name = tensor("op_5464_cast")]; + tensor denom_159_epsilon_0_to_fp16 = const()[name = tensor("denom_159_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_159_cast = rsqrt(epsilon = denom_159_epsilon_0_to_fp16, x = var_5464_cast)[name = tensor("denom_159_cast")]; + tensor out_159_cast = mul(x = zero_mean_159_cast, y = denom_159_cast)[name = tensor("out_159_cast")]; + tensor var_5468_to_fp16 = const()[name = tensor("op_5468_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878250304)))]; + tensor var_5469_cast = add(x = out_159_cast, y = var_5468_to_fp16)[name = tensor("op_5469_cast")]; + tensor var_5471_to_fp16 = const()[name = tensor("op_5471_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878252928)))]; + tensor hidden_states_223_cast = mul(x = var_5469_cast, y = var_5471_to_fp16)[name = tensor("hidden_states_223_cast")]; + tensor var_5478 = const()[name = tensor("op_5478"), val = tensor([1, 1])]; + tensor var_5480 = const()[name = tensor("op_5480"), val = tensor([1, 1])]; + tensor q_107_pad_type_0 = const()[name = tensor("q_107_pad_type_0"), val = tensor("custom")]; + tensor q_107_pad_0 = const()[name = tensor("q_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878255552)))]; + tensor q_107_cast = conv(dilations = var_5480, groups = var_4943, pad = q_107_pad_0, pad_type = q_107_pad_type_0, strides = var_5478, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_223_cast)[name = tensor("q_107_cast")]; + tensor var_5484 = const()[name = tensor("op_5484"), val = tensor([1, 1])]; + tensor var_5486 = const()[name = tensor("op_5486"), val = tensor([1, 1])]; + tensor k_107_pad_type_0 = const()[name = tensor("k_107_pad_type_0"), val = tensor("custom")]; + tensor k_107_pad_0 = const()[name = tensor("k_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1881532416)))]; + tensor k_107_cast = conv(dilations = var_5486, groups = var_4943, pad = k_107_pad_0, pad_type = k_107_pad_type_0, strides = var_5484, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_107_cast")]; + tensor var_5490 = const()[name = tensor("op_5490"), val = tensor([1, 1])]; + tensor var_5492 = const()[name = tensor("op_5492"), val = tensor([1, 1])]; + tensor v_107_pad_type_0 = const()[name = tensor("v_107_pad_type_0"), val = tensor("custom")]; + tensor v_107_pad_0 = const()[name = tensor("v_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1886775360)))]; + tensor v_107_cast = conv(dilations = var_5492, groups = var_4943, pad = v_107_pad_0, pad_type = v_107_pad_type_0, strides = var_5490, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_107_cast")]; + tensor var_5496 = const()[name = tensor("op_5496"), val = tensor([2, 20, 64, -1])]; + tensor var_5497_cast = reshape(shape = var_5496, x = q_107_cast)[name = tensor("op_5497_cast")]; + tensor var_5498 = const()[name = tensor("op_5498"), val = tensor([2, 20, 64, -1])]; + tensor var_5499_cast = reshape(shape = var_5498, x = k_107_cast)[name = tensor("op_5499_cast")]; + tensor var_5500 = const()[name = tensor("op_5500"), val = tensor([2, 20, 64, -1])]; + tensor var_5501_cast = reshape(shape = var_5500, x = v_107_cast)[name = tensor("op_5501_cast")]; + tensor attn_weights_213_transpose_x_0 = const()[name = tensor("attn_weights_213_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_213_transpose_y_0 = const()[name = tensor("attn_weights_213_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_213_cast = matmul(transpose_x = attn_weights_213_transpose_x_0, transpose_y = attn_weights_213_transpose_y_0, x = var_5497_cast, y = var_5499_cast)[name = tensor("attn_weights_213_cast")]; + tensor attn_weights_215_cast = mul(x = attn_weights_213_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_215_cast")]; + tensor var_5505_cast = softmax(axis = var_4927, x = attn_weights_215_cast)[name = tensor("op_5505_cast")]; + tensor attn_107_transpose_x_0 = const()[name = tensor("attn_107_transpose_x_0"), val = tensor(false)]; + tensor attn_107_transpose_y_0 = const()[name = tensor("attn_107_transpose_y_0"), val = tensor(true)]; + tensor attn_107_cast = matmul(transpose_x = attn_107_transpose_x_0, transpose_y = attn_107_transpose_y_0, x = var_5501_cast, y = var_5505_cast)[name = tensor("attn_107_cast")]; + tensor var_5509 = const()[name = tensor("op_5509"), val = tensor([2, 1280, 1, -1])]; + tensor input_345_cast = reshape(shape = var_5509, x = attn_107_cast)[name = tensor("input_345_cast")]; + tensor var_5514 = const()[name = tensor("op_5514"), val = tensor([1, 1])]; + tensor var_5516 = const()[name = tensor("op_5516"), val = tensor([1, 1])]; + tensor var_5518_pad_type_0 = const()[name = tensor("op_5518_pad_type_0"), val = tensor("custom")]; + tensor var_5518_pad_0 = const()[name = tensor("op_5518_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892018304)))]; + tensor mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1895295168)))]; + tensor var_5518_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_5516, groups = var_4943, pad = var_5518_pad_0, pad_type = var_5518_pad_type_0, strides = var_5514, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_345_cast)[name = tensor("op_5518_cast")]; + tensor inputs_161_cast = add(x = var_5518_cast, y = inputs_159_cast)[name = tensor("inputs_161_cast")]; + tensor var_5522 = const()[name = tensor("op_5522"), val = tensor([1])]; + tensor channels_mean_161_cast = reduce_mean(axes = var_5522, keep_dims = var_4938, x = inputs_161_cast)[name = tensor("channels_mean_161_cast")]; + tensor zero_mean_161_cast = sub(x = inputs_161_cast, y = channels_mean_161_cast)[name = tensor("zero_mean_161_cast")]; + tensor zero_mean_sq_161_cast = mul(x = zero_mean_161_cast, y = zero_mean_161_cast)[name = tensor("zero_mean_sq_161_cast")]; + tensor var_5526 = const()[name = tensor("op_5526"), val = tensor([1])]; + tensor var_5527_cast = reduce_mean(axes = var_5526, keep_dims = var_4938, x = zero_mean_sq_161_cast)[name = tensor("op_5527_cast")]; + tensor var_5528_to_fp16 = const()[name = tensor("op_5528_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5529_cast = add(x = var_5527_cast, y = var_5528_to_fp16)[name = tensor("op_5529_cast")]; + tensor denom_161_epsilon_0_to_fp16 = const()[name = tensor("denom_161_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_161_cast = rsqrt(epsilon = denom_161_epsilon_0_to_fp16, x = var_5529_cast)[name = tensor("denom_161_cast")]; + tensor out_161_cast = mul(x = zero_mean_161_cast, y = denom_161_cast)[name = tensor("out_161_cast")]; + tensor var_5533_to_fp16 = const()[name = tensor("op_5533_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1895297792)))]; + tensor var_5534_cast = add(x = out_161_cast, y = var_5533_to_fp16)[name = tensor("op_5534_cast")]; + tensor var_5536_to_fp16 = const()[name = tensor("op_5536_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1895300416)))]; + tensor input_347_cast = mul(x = var_5534_cast, y = var_5536_to_fp16)[name = tensor("input_347_cast")]; + tensor var_5544 = const()[name = tensor("op_5544"), val = tensor([1, 1])]; + tensor var_5546 = const()[name = tensor("op_5546"), val = tensor([1, 1])]; + tensor var_5548_pad_type_0 = const()[name = tensor("op_5548_pad_type_0"), val = tensor("custom")]; + tensor var_5548_pad_0 = const()[name = tensor("op_5548_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1895303040)))]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921517504)))]; + tensor var_5548_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_5546, groups = var_4943, pad = var_5548_pad_0, pad_type = var_5548_pad_type_0, strides = var_5544, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_347_cast)[name = tensor("op_5548_cast")]; + tensor var_5549_split_sizes_0 = const()[name = tensor("op_5549_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5549_axis_0 = const()[name = tensor("op_5549_axis_0"), val = tensor(1)]; + tensor var_5549_cast_0, tensor var_5549_cast_1 = split(axis = var_5549_axis_0, split_sizes = var_5549_split_sizes_0, x = var_5548_cast)[name = tensor("op_5549_cast")]; + tensor var_5551_mode_0 = const()[name = tensor("op_5551_mode_0"), val = tensor("EXACT")]; + tensor var_5551_cast = gelu(mode = var_5551_mode_0, x = var_5549_cast_1)[name = tensor("op_5551_cast")]; + tensor input_349_cast = mul(x = var_5549_cast_0, y = var_5551_cast)[name = tensor("input_349_cast")]; + tensor var_5555 = const()[name = tensor("op_5555"), val = tensor([1, 1])]; + tensor var_5557 = const()[name = tensor("op_5557"), val = tensor([1, 1])]; + tensor var_5559_pad_type_0 = const()[name = tensor("op_5559_pad_type_0"), val = tensor("custom")]; + tensor var_5559_pad_0 = const()[name = tensor("op_5559_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921538048)))]; + tensor mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1934645312)))]; + tensor var_5559_cast = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_5557, groups = var_4943, pad = var_5559_pad_0, pad_type = var_5559_pad_type_0, strides = var_5555, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_349_cast)[name = tensor("op_5559_cast")]; + tensor inputs_163_cast = add(x = var_5559_cast, y = inputs_161_cast)[name = tensor("inputs_163_cast")]; + tensor var_5569 = const()[name = tensor("op_5569"), val = tensor([1])]; + tensor channels_mean_163_cast = reduce_mean(axes = var_5569, keep_dims = var_4938, x = inputs_163_cast)[name = tensor("channels_mean_163_cast")]; + tensor zero_mean_163_cast = sub(x = inputs_163_cast, y = channels_mean_163_cast)[name = tensor("zero_mean_163_cast")]; + tensor zero_mean_sq_163_cast = mul(x = zero_mean_163_cast, y = zero_mean_163_cast)[name = tensor("zero_mean_sq_163_cast")]; + tensor var_5573 = const()[name = tensor("op_5573"), val = tensor([1])]; + tensor var_5574_cast = reduce_mean(axes = var_5573, keep_dims = var_4938, x = zero_mean_sq_163_cast)[name = tensor("op_5574_cast")]; + tensor var_5575_to_fp16 = const()[name = tensor("op_5575_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5576_cast = add(x = var_5574_cast, y = var_5575_to_fp16)[name = tensor("op_5576_cast")]; + tensor denom_163_epsilon_0_to_fp16 = const()[name = tensor("denom_163_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_163_cast = rsqrt(epsilon = denom_163_epsilon_0_to_fp16, x = var_5576_cast)[name = tensor("denom_163_cast")]; + tensor out_163_cast = mul(x = zero_mean_163_cast, y = denom_163_cast)[name = tensor("out_163_cast")]; + tensor var_5580_to_fp16 = const()[name = tensor("op_5580_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1934647936)))]; + tensor var_5581_cast = add(x = out_163_cast, y = var_5580_to_fp16)[name = tensor("op_5581_cast")]; + tensor var_5583_to_fp16 = const()[name = tensor("op_5583_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1934650560)))]; + tensor hidden_states_227_cast = mul(x = var_5581_cast, y = var_5583_to_fp16)[name = tensor("hidden_states_227_cast")]; + tensor var_5590 = const()[name = tensor("op_5590"), val = tensor([1, 1])]; + tensor var_5592 = const()[name = tensor("op_5592"), val = tensor([1, 1])]; + tensor q_109_pad_type_0 = const()[name = tensor("q_109_pad_type_0"), val = tensor("custom")]; + tensor q_109_pad_0 = const()[name = tensor("q_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1934653184)))]; + tensor q_109_cast = conv(dilations = var_5592, groups = var_4943, pad = q_109_pad_0, pad_type = q_109_pad_type_0, strides = var_5590, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_227_cast)[name = tensor("q_109_cast")]; + tensor var_5596 = const()[name = tensor("op_5596"), val = tensor([1, 1])]; + tensor var_5598 = const()[name = tensor("op_5598"), val = tensor([1, 1])]; + tensor k_109_pad_type_0 = const()[name = tensor("k_109_pad_type_0"), val = tensor("custom")]; + tensor k_109_pad_0 = const()[name = tensor("k_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1937930048)))]; + tensor k_109_cast = conv(dilations = var_5598, groups = var_4943, pad = k_109_pad_0, pad_type = k_109_pad_type_0, strides = var_5596, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_227_cast)[name = tensor("k_109_cast")]; + tensor var_5602 = const()[name = tensor("op_5602"), val = tensor([1, 1])]; + tensor var_5604 = const()[name = tensor("op_5604"), val = tensor([1, 1])]; + tensor v_109_pad_type_0 = const()[name = tensor("v_109_pad_type_0"), val = tensor("custom")]; + tensor v_109_pad_0 = const()[name = tensor("v_109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1941206912)))]; + tensor v_109_cast = conv(dilations = var_5604, groups = var_4943, pad = v_109_pad_0, pad_type = v_109_pad_type_0, strides = var_5602, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_227_cast)[name = tensor("v_109_cast")]; + tensor var_5608 = const()[name = tensor("op_5608"), val = tensor([2, 20, 64, -1])]; + tensor var_5609_cast = reshape(shape = var_5608, x = q_109_cast)[name = tensor("op_5609_cast")]; + tensor var_5610 = const()[name = tensor("op_5610"), val = tensor([2, 20, 64, -1])]; + tensor var_5611_cast = reshape(shape = var_5610, x = k_109_cast)[name = tensor("op_5611_cast")]; + tensor var_5612 = const()[name = tensor("op_5612"), val = tensor([2, 20, 64, -1])]; + tensor var_5613_cast = reshape(shape = var_5612, x = v_109_cast)[name = tensor("op_5613_cast")]; + tensor attn_weights_217_transpose_x_0 = const()[name = tensor("attn_weights_217_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_217_transpose_y_0 = const()[name = tensor("attn_weights_217_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_217_cast = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5609_cast, y = var_5611_cast)[name = tensor("attn_weights_217_cast")]; + tensor attn_weights_219_cast = mul(x = attn_weights_217_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_219_cast")]; + tensor var_5617_cast = softmax(axis = var_4927, x = attn_weights_219_cast)[name = tensor("op_5617_cast")]; + tensor attn_109_transpose_x_0 = const()[name = tensor("attn_109_transpose_x_0"), val = tensor(false)]; + tensor attn_109_transpose_y_0 = const()[name = tensor("attn_109_transpose_y_0"), val = tensor(true)]; + tensor attn_109_cast = matmul(transpose_x = attn_109_transpose_x_0, transpose_y = attn_109_transpose_y_0, x = var_5613_cast, y = var_5617_cast)[name = tensor("attn_109_cast")]; + tensor var_5621 = const()[name = tensor("op_5621"), val = tensor([2, 1280, 1, -1])]; + tensor input_351_cast = reshape(shape = var_5621, x = attn_109_cast)[name = tensor("input_351_cast")]; + tensor var_5626 = const()[name = tensor("op_5626"), val = tensor([1, 1])]; + tensor var_5628 = const()[name = tensor("op_5628"), val = tensor([1, 1])]; + tensor var_5630_pad_type_0 = const()[name = tensor("op_5630_pad_type_0"), val = tensor("custom")]; + tensor var_5630_pad_0 = const()[name = tensor("op_5630_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1944483776)))]; + tensor mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1947760640)))]; + tensor var_5630_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_5628, groups = var_4943, pad = var_5630_pad_0, pad_type = var_5630_pad_type_0, strides = var_5626, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_351_cast)[name = tensor("op_5630_cast")]; + tensor inputs_165_cast = add(x = var_5630_cast, y = inputs_163_cast)[name = tensor("inputs_165_cast")]; + tensor var_5634 = const()[name = tensor("op_5634"), val = tensor([1])]; + tensor channels_mean_165_cast = reduce_mean(axes = var_5634, keep_dims = var_4938, x = inputs_165_cast)[name = tensor("channels_mean_165_cast")]; + tensor zero_mean_165_cast = sub(x = inputs_165_cast, y = channels_mean_165_cast)[name = tensor("zero_mean_165_cast")]; + tensor zero_mean_sq_165_cast = mul(x = zero_mean_165_cast, y = zero_mean_165_cast)[name = tensor("zero_mean_sq_165_cast")]; + tensor var_5638 = const()[name = tensor("op_5638"), val = tensor([1])]; + tensor var_5639_cast = reduce_mean(axes = var_5638, keep_dims = var_4938, x = zero_mean_sq_165_cast)[name = tensor("op_5639_cast")]; + tensor var_5640_to_fp16 = const()[name = tensor("op_5640_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5641_cast = add(x = var_5639_cast, y = var_5640_to_fp16)[name = tensor("op_5641_cast")]; + tensor denom_165_epsilon_0_to_fp16 = const()[name = tensor("denom_165_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_165_cast = rsqrt(epsilon = denom_165_epsilon_0_to_fp16, x = var_5641_cast)[name = tensor("denom_165_cast")]; + tensor out_165_cast = mul(x = zero_mean_165_cast, y = denom_165_cast)[name = tensor("out_165_cast")]; + tensor var_5645_to_fp16 = const()[name = tensor("op_5645_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1947763264)))]; + tensor var_5646_cast = add(x = out_165_cast, y = var_5645_to_fp16)[name = tensor("op_5646_cast")]; + tensor var_5648_to_fp16 = const()[name = tensor("op_5648_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1947765888)))]; + tensor hidden_states_229_cast = mul(x = var_5646_cast, y = var_5648_to_fp16)[name = tensor("hidden_states_229_cast")]; + tensor var_5655 = const()[name = tensor("op_5655"), val = tensor([1, 1])]; + tensor var_5657 = const()[name = tensor("op_5657"), val = tensor([1, 1])]; + tensor q_111_pad_type_0 = const()[name = tensor("q_111_pad_type_0"), val = tensor("custom")]; + tensor q_111_pad_0 = const()[name = tensor("q_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1947768512)))]; + tensor q_111_cast = conv(dilations = var_5657, groups = var_4943, pad = q_111_pad_0, pad_type = q_111_pad_type_0, strides = var_5655, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_229_cast)[name = tensor("q_111_cast")]; + tensor var_5661 = const()[name = tensor("op_5661"), val = tensor([1, 1])]; + tensor var_5663 = const()[name = tensor("op_5663"), val = tensor([1, 1])]; + tensor k_111_pad_type_0 = const()[name = tensor("k_111_pad_type_0"), val = tensor("custom")]; + tensor k_111_pad_0 = const()[name = tensor("k_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1951045376)))]; + tensor k_111_cast = conv(dilations = var_5663, groups = var_4943, pad = k_111_pad_0, pad_type = k_111_pad_type_0, strides = var_5661, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_111_cast")]; + tensor var_5667 = const()[name = tensor("op_5667"), val = tensor([1, 1])]; + tensor var_5669 = const()[name = tensor("op_5669"), val = tensor([1, 1])]; + tensor v_111_pad_type_0 = const()[name = tensor("v_111_pad_type_0"), val = tensor("custom")]; + tensor v_111_pad_0 = const()[name = tensor("v_111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1956288320)))]; + tensor v_111_cast = conv(dilations = var_5669, groups = var_4943, pad = v_111_pad_0, pad_type = v_111_pad_type_0, strides = var_5667, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_111_cast")]; + tensor var_5673 = const()[name = tensor("op_5673"), val = tensor([2, 20, 64, -1])]; + tensor var_5674_cast = reshape(shape = var_5673, x = q_111_cast)[name = tensor("op_5674_cast")]; + tensor var_5675 = const()[name = tensor("op_5675"), val = tensor([2, 20, 64, -1])]; + tensor var_5676_cast = reshape(shape = var_5675, x = k_111_cast)[name = tensor("op_5676_cast")]; + tensor var_5677 = const()[name = tensor("op_5677"), val = tensor([2, 20, 64, -1])]; + tensor var_5678_cast = reshape(shape = var_5677, x = v_111_cast)[name = tensor("op_5678_cast")]; + tensor attn_weights_221_transpose_x_0 = const()[name = tensor("attn_weights_221_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_221_transpose_y_0 = const()[name = tensor("attn_weights_221_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_221_cast = matmul(transpose_x = attn_weights_221_transpose_x_0, transpose_y = attn_weights_221_transpose_y_0, x = var_5674_cast, y = var_5676_cast)[name = tensor("attn_weights_221_cast")]; + tensor attn_weights_223_cast = mul(x = attn_weights_221_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_223_cast")]; + tensor var_5682_cast = softmax(axis = var_4927, x = attn_weights_223_cast)[name = tensor("op_5682_cast")]; + tensor attn_111_transpose_x_0 = const()[name = tensor("attn_111_transpose_x_0"), val = tensor(false)]; + tensor attn_111_transpose_y_0 = const()[name = tensor("attn_111_transpose_y_0"), val = tensor(true)]; + tensor attn_111_cast = matmul(transpose_x = attn_111_transpose_x_0, transpose_y = attn_111_transpose_y_0, x = var_5678_cast, y = var_5682_cast)[name = tensor("attn_111_cast")]; + tensor var_5686 = const()[name = tensor("op_5686"), val = tensor([2, 1280, 1, -1])]; + tensor input_353_cast = reshape(shape = var_5686, x = attn_111_cast)[name = tensor("input_353_cast")]; + tensor var_5691 = const()[name = tensor("op_5691"), val = tensor([1, 1])]; + tensor var_5693 = const()[name = tensor("op_5693"), val = tensor([1, 1])]; + tensor var_5695_pad_type_0 = const()[name = tensor("op_5695_pad_type_0"), val = tensor("custom")]; + tensor var_5695_pad_0 = const()[name = tensor("op_5695_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1961531264)))]; + tensor mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1964808128)))]; + tensor var_5695_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_5693, groups = var_4943, pad = var_5695_pad_0, pad_type = var_5695_pad_type_0, strides = var_5691, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_353_cast)[name = tensor("op_5695_cast")]; + tensor inputs_167_cast = add(x = var_5695_cast, y = inputs_165_cast)[name = tensor("inputs_167_cast")]; + tensor var_5699 = const()[name = tensor("op_5699"), val = tensor([1])]; + tensor channels_mean_167_cast = reduce_mean(axes = var_5699, keep_dims = var_4938, x = inputs_167_cast)[name = tensor("channels_mean_167_cast")]; + tensor zero_mean_167_cast = sub(x = inputs_167_cast, y = channels_mean_167_cast)[name = tensor("zero_mean_167_cast")]; + tensor zero_mean_sq_167_cast = mul(x = zero_mean_167_cast, y = zero_mean_167_cast)[name = tensor("zero_mean_sq_167_cast")]; + tensor var_5703 = const()[name = tensor("op_5703"), val = tensor([1])]; + tensor var_5704_cast = reduce_mean(axes = var_5703, keep_dims = var_4938, x = zero_mean_sq_167_cast)[name = tensor("op_5704_cast")]; + tensor var_5705_to_fp16 = const()[name = tensor("op_5705_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5706_cast = add(x = var_5704_cast, y = var_5705_to_fp16)[name = tensor("op_5706_cast")]; + tensor denom_167_epsilon_0_to_fp16 = const()[name = tensor("denom_167_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_167_cast = rsqrt(epsilon = denom_167_epsilon_0_to_fp16, x = var_5706_cast)[name = tensor("denom_167_cast")]; + tensor out_167_cast = mul(x = zero_mean_167_cast, y = denom_167_cast)[name = tensor("out_167_cast")]; + tensor var_5710_to_fp16 = const()[name = tensor("op_5710_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1964810752)))]; + tensor var_5711_cast = add(x = out_167_cast, y = var_5710_to_fp16)[name = tensor("op_5711_cast")]; + tensor var_5713_to_fp16 = const()[name = tensor("op_5713_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1964813376)))]; + tensor input_355_cast = mul(x = var_5711_cast, y = var_5713_to_fp16)[name = tensor("input_355_cast")]; + tensor var_5721 = const()[name = tensor("op_5721"), val = tensor([1, 1])]; + tensor var_5723 = const()[name = tensor("op_5723"), val = tensor([1, 1])]; + tensor var_5725_pad_type_0 = const()[name = tensor("op_5725_pad_type_0"), val = tensor("custom")]; + tensor var_5725_pad_0 = const()[name = tensor("op_5725_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1964816000)))]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1991030464)))]; + tensor var_5725_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_5723, groups = var_4943, pad = var_5725_pad_0, pad_type = var_5725_pad_type_0, strides = var_5721, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_355_cast)[name = tensor("op_5725_cast")]; + tensor var_5726_split_sizes_0 = const()[name = tensor("op_5726_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5726_axis_0 = const()[name = tensor("op_5726_axis_0"), val = tensor(1)]; + tensor var_5726_cast_0, tensor var_5726_cast_1 = split(axis = var_5726_axis_0, split_sizes = var_5726_split_sizes_0, x = var_5725_cast)[name = tensor("op_5726_cast")]; + tensor var_5728_mode_0 = const()[name = tensor("op_5728_mode_0"), val = tensor("EXACT")]; + tensor var_5728_cast = gelu(mode = var_5728_mode_0, x = var_5726_cast_1)[name = tensor("op_5728_cast")]; + tensor input_357_cast = mul(x = var_5726_cast_0, y = var_5728_cast)[name = tensor("input_357_cast")]; + tensor var_5732 = const()[name = tensor("op_5732"), val = tensor([1, 1])]; + tensor var_5734 = const()[name = tensor("op_5734"), val = tensor([1, 1])]; + tensor var_5736_pad_type_0 = const()[name = tensor("op_5736_pad_type_0"), val = tensor("custom")]; + tensor var_5736_pad_0 = const()[name = tensor("op_5736_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1991051008)))]; + tensor mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2004158272)))]; + tensor var_5736_cast = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_5734, groups = var_4943, pad = var_5736_pad_0, pad_type = var_5736_pad_type_0, strides = var_5732, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_357_cast)[name = tensor("op_5736_cast")]; + tensor inputs_169_cast = add(x = var_5736_cast, y = inputs_167_cast)[name = tensor("inputs_169_cast")]; + tensor var_5746 = const()[name = tensor("op_5746"), val = tensor([1])]; + tensor channels_mean_169_cast = reduce_mean(axes = var_5746, keep_dims = var_4938, x = inputs_169_cast)[name = tensor("channels_mean_169_cast")]; + tensor zero_mean_169_cast = sub(x = inputs_169_cast, y = channels_mean_169_cast)[name = tensor("zero_mean_169_cast")]; + tensor zero_mean_sq_169_cast = mul(x = zero_mean_169_cast, y = zero_mean_169_cast)[name = tensor("zero_mean_sq_169_cast")]; + tensor var_5750 = const()[name = tensor("op_5750"), val = tensor([1])]; + tensor var_5751_cast = reduce_mean(axes = var_5750, keep_dims = var_4938, x = zero_mean_sq_169_cast)[name = tensor("op_5751_cast")]; + tensor var_5752_to_fp16 = const()[name = tensor("op_5752_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5753_cast = add(x = var_5751_cast, y = var_5752_to_fp16)[name = tensor("op_5753_cast")]; + tensor denom_169_epsilon_0_to_fp16 = const()[name = tensor("denom_169_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_169_cast = rsqrt(epsilon = denom_169_epsilon_0_to_fp16, x = var_5753_cast)[name = tensor("denom_169_cast")]; + tensor out_169_cast = mul(x = zero_mean_169_cast, y = denom_169_cast)[name = tensor("out_169_cast")]; + tensor var_5757_to_fp16 = const()[name = tensor("op_5757_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2004160896)))]; + tensor var_5758_cast = add(x = out_169_cast, y = var_5757_to_fp16)[name = tensor("op_5758_cast")]; + tensor var_5760_to_fp16 = const()[name = tensor("op_5760_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2004163520)))]; + tensor hidden_states_233_cast = mul(x = var_5758_cast, y = var_5760_to_fp16)[name = tensor("hidden_states_233_cast")]; + tensor var_5767 = const()[name = tensor("op_5767"), val = tensor([1, 1])]; + tensor var_5769 = const()[name = tensor("op_5769"), val = tensor([1, 1])]; + tensor q_113_pad_type_0 = const()[name = tensor("q_113_pad_type_0"), val = tensor("custom")]; + tensor q_113_pad_0 = const()[name = tensor("q_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2004166144)))]; + tensor q_113_cast = conv(dilations = var_5769, groups = var_4943, pad = q_113_pad_0, pad_type = q_113_pad_type_0, strides = var_5767, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_233_cast)[name = tensor("q_113_cast")]; + tensor var_5773 = const()[name = tensor("op_5773"), val = tensor([1, 1])]; + tensor var_5775 = const()[name = tensor("op_5775"), val = tensor([1, 1])]; + tensor k_113_pad_type_0 = const()[name = tensor("k_113_pad_type_0"), val = tensor("custom")]; + tensor k_113_pad_0 = const()[name = tensor("k_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2007443008)))]; + tensor k_113_cast = conv(dilations = var_5775, groups = var_4943, pad = k_113_pad_0, pad_type = k_113_pad_type_0, strides = var_5773, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_233_cast)[name = tensor("k_113_cast")]; + tensor var_5779 = const()[name = tensor("op_5779"), val = tensor([1, 1])]; + tensor var_5781 = const()[name = tensor("op_5781"), val = tensor([1, 1])]; + tensor v_113_pad_type_0 = const()[name = tensor("v_113_pad_type_0"), val = tensor("custom")]; + tensor v_113_pad_0 = const()[name = tensor("v_113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2010719872)))]; + tensor v_113_cast = conv(dilations = var_5781, groups = var_4943, pad = v_113_pad_0, pad_type = v_113_pad_type_0, strides = var_5779, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_233_cast)[name = tensor("v_113_cast")]; + tensor var_5785 = const()[name = tensor("op_5785"), val = tensor([2, 20, 64, -1])]; + tensor var_5786_cast = reshape(shape = var_5785, x = q_113_cast)[name = tensor("op_5786_cast")]; + tensor var_5787 = const()[name = tensor("op_5787"), val = tensor([2, 20, 64, -1])]; + tensor var_5788_cast = reshape(shape = var_5787, x = k_113_cast)[name = tensor("op_5788_cast")]; + tensor var_5789 = const()[name = tensor("op_5789"), val = tensor([2, 20, 64, -1])]; + tensor var_5790_cast = reshape(shape = var_5789, x = v_113_cast)[name = tensor("op_5790_cast")]; + tensor attn_weights_225_transpose_x_0 = const()[name = tensor("attn_weights_225_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_225_transpose_y_0 = const()[name = tensor("attn_weights_225_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_225_cast = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5786_cast, y = var_5788_cast)[name = tensor("attn_weights_225_cast")]; + tensor attn_weights_227_cast = mul(x = attn_weights_225_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_227_cast")]; + tensor var_5794_cast = softmax(axis = var_4927, x = attn_weights_227_cast)[name = tensor("op_5794_cast")]; + tensor attn_113_transpose_x_0 = const()[name = tensor("attn_113_transpose_x_0"), val = tensor(false)]; + tensor attn_113_transpose_y_0 = const()[name = tensor("attn_113_transpose_y_0"), val = tensor(true)]; + tensor attn_113_cast = matmul(transpose_x = attn_113_transpose_x_0, transpose_y = attn_113_transpose_y_0, x = var_5790_cast, y = var_5794_cast)[name = tensor("attn_113_cast")]; + tensor var_5798 = const()[name = tensor("op_5798"), val = tensor([2, 1280, 1, -1])]; + tensor input_359_cast = reshape(shape = var_5798, x = attn_113_cast)[name = tensor("input_359_cast")]; + tensor var_5803 = const()[name = tensor("op_5803"), val = tensor([1, 1])]; + tensor var_5805 = const()[name = tensor("op_5805"), val = tensor([1, 1])]; + tensor var_5807_pad_type_0 = const()[name = tensor("op_5807_pad_type_0"), val = tensor("custom")]; + tensor var_5807_pad_0 = const()[name = tensor("op_5807_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2013996736)))]; + tensor mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2017273600)))]; + tensor var_5807_cast = conv(bias = mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_5805, groups = var_4943, pad = var_5807_pad_0, pad_type = var_5807_pad_type_0, strides = var_5803, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_359_cast)[name = tensor("op_5807_cast")]; + tensor inputs_171_cast = add(x = var_5807_cast, y = inputs_169_cast)[name = tensor("inputs_171_cast")]; + tensor var_5811 = const()[name = tensor("op_5811"), val = tensor([1])]; + tensor channels_mean_171_cast = reduce_mean(axes = var_5811, keep_dims = var_4938, x = inputs_171_cast)[name = tensor("channels_mean_171_cast")]; + tensor zero_mean_171_cast = sub(x = inputs_171_cast, y = channels_mean_171_cast)[name = tensor("zero_mean_171_cast")]; + tensor zero_mean_sq_171_cast = mul(x = zero_mean_171_cast, y = zero_mean_171_cast)[name = tensor("zero_mean_sq_171_cast")]; + tensor var_5815 = const()[name = tensor("op_5815"), val = tensor([1])]; + tensor var_5816_cast = reduce_mean(axes = var_5815, keep_dims = var_4938, x = zero_mean_sq_171_cast)[name = tensor("op_5816_cast")]; + tensor var_5817_to_fp16 = const()[name = tensor("op_5817_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5818_cast = add(x = var_5816_cast, y = var_5817_to_fp16)[name = tensor("op_5818_cast")]; + tensor denom_171_epsilon_0_to_fp16 = const()[name = tensor("denom_171_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_171_cast = rsqrt(epsilon = denom_171_epsilon_0_to_fp16, x = var_5818_cast)[name = tensor("denom_171_cast")]; + tensor out_171_cast = mul(x = zero_mean_171_cast, y = denom_171_cast)[name = tensor("out_171_cast")]; + tensor var_5822_to_fp16 = const()[name = tensor("op_5822_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2017276224)))]; + tensor var_5823_cast = add(x = out_171_cast, y = var_5822_to_fp16)[name = tensor("op_5823_cast")]; + tensor var_5825_to_fp16 = const()[name = tensor("op_5825_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2017278848)))]; + tensor hidden_states_235_cast = mul(x = var_5823_cast, y = var_5825_to_fp16)[name = tensor("hidden_states_235_cast")]; + tensor var_5832 = const()[name = tensor("op_5832"), val = tensor([1, 1])]; + tensor var_5834 = const()[name = tensor("op_5834"), val = tensor([1, 1])]; + tensor q_115_pad_type_0 = const()[name = tensor("q_115_pad_type_0"), val = tensor("custom")]; + tensor q_115_pad_0 = const()[name = tensor("q_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2017281472)))]; + tensor q_115_cast = conv(dilations = var_5834, groups = var_4943, pad = q_115_pad_0, pad_type = q_115_pad_type_0, strides = var_5832, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_235_cast)[name = tensor("q_115_cast")]; + tensor var_5838 = const()[name = tensor("op_5838"), val = tensor([1, 1])]; + tensor var_5840 = const()[name = tensor("op_5840"), val = tensor([1, 1])]; + tensor k_115_pad_type_0 = const()[name = tensor("k_115_pad_type_0"), val = tensor("custom")]; + tensor k_115_pad_0 = const()[name = tensor("k_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2020558336)))]; + tensor k_115_cast = conv(dilations = var_5840, groups = var_4943, pad = k_115_pad_0, pad_type = k_115_pad_type_0, strides = var_5838, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_115_cast")]; + tensor var_5844 = const()[name = tensor("op_5844"), val = tensor([1, 1])]; + tensor var_5846 = const()[name = tensor("op_5846"), val = tensor([1, 1])]; + tensor v_115_pad_type_0 = const()[name = tensor("v_115_pad_type_0"), val = tensor("custom")]; + tensor v_115_pad_0 = const()[name = tensor("v_115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2025801280)))]; + tensor v_115_cast = conv(dilations = var_5846, groups = var_4943, pad = v_115_pad_0, pad_type = v_115_pad_type_0, strides = var_5844, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_115_cast")]; + tensor var_5850 = const()[name = tensor("op_5850"), val = tensor([2, 20, 64, -1])]; + tensor var_5851_cast = reshape(shape = var_5850, x = q_115_cast)[name = tensor("op_5851_cast")]; + tensor var_5852 = const()[name = tensor("op_5852"), val = tensor([2, 20, 64, -1])]; + tensor var_5853_cast = reshape(shape = var_5852, x = k_115_cast)[name = tensor("op_5853_cast")]; + tensor var_5854 = const()[name = tensor("op_5854"), val = tensor([2, 20, 64, -1])]; + tensor var_5855_cast = reshape(shape = var_5854, x = v_115_cast)[name = tensor("op_5855_cast")]; + tensor attn_weights_229_transpose_x_0 = const()[name = tensor("attn_weights_229_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_229_transpose_y_0 = const()[name = tensor("attn_weights_229_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_229_cast = matmul(transpose_x = attn_weights_229_transpose_x_0, transpose_y = attn_weights_229_transpose_y_0, x = var_5851_cast, y = var_5853_cast)[name = tensor("attn_weights_229_cast")]; + tensor attn_weights_231_cast = mul(x = attn_weights_229_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_231_cast")]; + tensor var_5859_cast = softmax(axis = var_4927, x = attn_weights_231_cast)[name = tensor("op_5859_cast")]; + tensor attn_115_transpose_x_0 = const()[name = tensor("attn_115_transpose_x_0"), val = tensor(false)]; + tensor attn_115_transpose_y_0 = const()[name = tensor("attn_115_transpose_y_0"), val = tensor(true)]; + tensor attn_115_cast = matmul(transpose_x = attn_115_transpose_x_0, transpose_y = attn_115_transpose_y_0, x = var_5855_cast, y = var_5859_cast)[name = tensor("attn_115_cast")]; + tensor var_5863 = const()[name = tensor("op_5863"), val = tensor([2, 1280, 1, -1])]; + tensor input_361_cast = reshape(shape = var_5863, x = attn_115_cast)[name = tensor("input_361_cast")]; + tensor var_5868 = const()[name = tensor("op_5868"), val = tensor([1, 1])]; + tensor var_5870 = const()[name = tensor("op_5870"), val = tensor([1, 1])]; + tensor var_5872_pad_type_0 = const()[name = tensor("op_5872_pad_type_0"), val = tensor("custom")]; + tensor var_5872_pad_0 = const()[name = tensor("op_5872_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2031044224)))]; + tensor mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2034321088)))]; + tensor var_5872_cast = conv(bias = mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_5870, groups = var_4943, pad = var_5872_pad_0, pad_type = var_5872_pad_type_0, strides = var_5868, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_361_cast)[name = tensor("op_5872_cast")]; + tensor inputs_173_cast = add(x = var_5872_cast, y = inputs_171_cast)[name = tensor("inputs_173_cast")]; + tensor var_5876 = const()[name = tensor("op_5876"), val = tensor([1])]; + tensor channels_mean_173_cast = reduce_mean(axes = var_5876, keep_dims = var_4938, x = inputs_173_cast)[name = tensor("channels_mean_173_cast")]; + tensor zero_mean_173_cast = sub(x = inputs_173_cast, y = channels_mean_173_cast)[name = tensor("zero_mean_173_cast")]; + tensor zero_mean_sq_173_cast = mul(x = zero_mean_173_cast, y = zero_mean_173_cast)[name = tensor("zero_mean_sq_173_cast")]; + tensor var_5880 = const()[name = tensor("op_5880"), val = tensor([1])]; + tensor var_5881_cast = reduce_mean(axes = var_5880, keep_dims = var_4938, x = zero_mean_sq_173_cast)[name = tensor("op_5881_cast")]; + tensor var_5882_to_fp16 = const()[name = tensor("op_5882_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5883_cast = add(x = var_5881_cast, y = var_5882_to_fp16)[name = tensor("op_5883_cast")]; + tensor denom_173_epsilon_0_to_fp16 = const()[name = tensor("denom_173_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_173_cast = rsqrt(epsilon = denom_173_epsilon_0_to_fp16, x = var_5883_cast)[name = tensor("denom_173_cast")]; + tensor out_173_cast = mul(x = zero_mean_173_cast, y = denom_173_cast)[name = tensor("out_173_cast")]; + tensor var_5887_to_fp16 = const()[name = tensor("op_5887_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2034323712)))]; + tensor var_5888_cast = add(x = out_173_cast, y = var_5887_to_fp16)[name = tensor("op_5888_cast")]; + tensor var_5890_to_fp16 = const()[name = tensor("op_5890_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2034326336)))]; + tensor input_363_cast = mul(x = var_5888_cast, y = var_5890_to_fp16)[name = tensor("input_363_cast")]; + tensor var_5898 = const()[name = tensor("op_5898"), val = tensor([1, 1])]; + tensor var_5900 = const()[name = tensor("op_5900"), val = tensor([1, 1])]; + tensor var_5902_pad_type_0 = const()[name = tensor("op_5902_pad_type_0"), val = tensor("custom")]; + tensor var_5902_pad_0 = const()[name = tensor("op_5902_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2034328960)))]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2060543424)))]; + tensor var_5902_cast = conv(bias = mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_5900, groups = var_4943, pad = var_5902_pad_0, pad_type = var_5902_pad_type_0, strides = var_5898, weight = mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_363_cast)[name = tensor("op_5902_cast")]; + tensor var_5903_split_sizes_0 = const()[name = tensor("op_5903_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5903_axis_0 = const()[name = tensor("op_5903_axis_0"), val = tensor(1)]; + tensor var_5903_cast_0, tensor var_5903_cast_1 = split(axis = var_5903_axis_0, split_sizes = var_5903_split_sizes_0, x = var_5902_cast)[name = tensor("op_5903_cast")]; + tensor var_5905_mode_0 = const()[name = tensor("op_5905_mode_0"), val = tensor("EXACT")]; + tensor var_5905_cast = gelu(mode = var_5905_mode_0, x = var_5903_cast_1)[name = tensor("op_5905_cast")]; + tensor input_365_cast = mul(x = var_5903_cast_0, y = var_5905_cast)[name = tensor("input_365_cast")]; + tensor var_5909 = const()[name = tensor("op_5909"), val = tensor([1, 1])]; + tensor var_5911 = const()[name = tensor("op_5911"), val = tensor([1, 1])]; + tensor var_5913_pad_type_0 = const()[name = tensor("op_5913_pad_type_0"), val = tensor("custom")]; + tensor var_5913_pad_0 = const()[name = tensor("op_5913_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2060563968)))]; + tensor mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2073671232)))]; + tensor var_5913_cast = conv(bias = mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_5911, groups = var_4943, pad = var_5913_pad_0, pad_type = var_5913_pad_type_0, strides = var_5909, weight = mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_365_cast)[name = tensor("op_5913_cast")]; + tensor inputs_175_cast = add(x = var_5913_cast, y = inputs_173_cast)[name = tensor("inputs_175_cast")]; + tensor var_5923 = const()[name = tensor("op_5923"), val = tensor([1])]; + tensor channels_mean_175_cast = reduce_mean(axes = var_5923, keep_dims = var_4938, x = inputs_175_cast)[name = tensor("channels_mean_175_cast")]; + tensor zero_mean_175_cast = sub(x = inputs_175_cast, y = channels_mean_175_cast)[name = tensor("zero_mean_175_cast")]; + tensor zero_mean_sq_175_cast = mul(x = zero_mean_175_cast, y = zero_mean_175_cast)[name = tensor("zero_mean_sq_175_cast")]; + tensor var_5927 = const()[name = tensor("op_5927"), val = tensor([1])]; + tensor var_5928_cast = reduce_mean(axes = var_5927, keep_dims = var_4938, x = zero_mean_sq_175_cast)[name = tensor("op_5928_cast")]; + tensor var_5929_to_fp16 = const()[name = tensor("op_5929_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5930_cast = add(x = var_5928_cast, y = var_5929_to_fp16)[name = tensor("op_5930_cast")]; + tensor denom_175_epsilon_0_to_fp16 = const()[name = tensor("denom_175_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_175_cast = rsqrt(epsilon = denom_175_epsilon_0_to_fp16, x = var_5930_cast)[name = tensor("denom_175_cast")]; + tensor out_175_cast = mul(x = zero_mean_175_cast, y = denom_175_cast)[name = tensor("out_175_cast")]; + tensor var_5934_to_fp16 = const()[name = tensor("op_5934_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2073673856)))]; + tensor var_5935_cast = add(x = out_175_cast, y = var_5934_to_fp16)[name = tensor("op_5935_cast")]; + tensor var_5937_to_fp16 = const()[name = tensor("op_5937_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2073676480)))]; + tensor hidden_states_239_cast = mul(x = var_5935_cast, y = var_5937_to_fp16)[name = tensor("hidden_states_239_cast")]; + tensor var_5944 = const()[name = tensor("op_5944"), val = tensor([1, 1])]; + tensor var_5946 = const()[name = tensor("op_5946"), val = tensor([1, 1])]; + tensor q_117_pad_type_0 = const()[name = tensor("q_117_pad_type_0"), val = tensor("custom")]; + tensor q_117_pad_0 = const()[name = tensor("q_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2073679104)))]; + tensor q_117_cast = conv(dilations = var_5946, groups = var_4943, pad = q_117_pad_0, pad_type = q_117_pad_type_0, strides = var_5944, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_239_cast)[name = tensor("q_117_cast")]; + tensor var_5950 = const()[name = tensor("op_5950"), val = tensor([1, 1])]; + tensor var_5952 = const()[name = tensor("op_5952"), val = tensor([1, 1])]; + tensor k_117_pad_type_0 = const()[name = tensor("k_117_pad_type_0"), val = tensor("custom")]; + tensor k_117_pad_0 = const()[name = tensor("k_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2076955968)))]; + tensor k_117_cast = conv(dilations = var_5952, groups = var_4943, pad = k_117_pad_0, pad_type = k_117_pad_type_0, strides = var_5950, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_239_cast)[name = tensor("k_117_cast")]; + tensor var_5956 = const()[name = tensor("op_5956"), val = tensor([1, 1])]; + tensor var_5958 = const()[name = tensor("op_5958"), val = tensor([1, 1])]; + tensor v_117_pad_type_0 = const()[name = tensor("v_117_pad_type_0"), val = tensor("custom")]; + tensor v_117_pad_0 = const()[name = tensor("v_117_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2080232832)))]; + tensor v_117_cast = conv(dilations = var_5958, groups = var_4943, pad = v_117_pad_0, pad_type = v_117_pad_type_0, strides = var_5956, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_239_cast)[name = tensor("v_117_cast")]; + tensor var_5962 = const()[name = tensor("op_5962"), val = tensor([2, 20, 64, -1])]; + tensor var_5963_cast = reshape(shape = var_5962, x = q_117_cast)[name = tensor("op_5963_cast")]; + tensor var_5964 = const()[name = tensor("op_5964"), val = tensor([2, 20, 64, -1])]; + tensor var_5965_cast = reshape(shape = var_5964, x = k_117_cast)[name = tensor("op_5965_cast")]; + tensor var_5966 = const()[name = tensor("op_5966"), val = tensor([2, 20, 64, -1])]; + tensor var_5967_cast = reshape(shape = var_5966, x = v_117_cast)[name = tensor("op_5967_cast")]; + tensor attn_weights_233_transpose_x_0 = const()[name = tensor("attn_weights_233_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_233_transpose_y_0 = const()[name = tensor("attn_weights_233_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_233_cast = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5963_cast, y = var_5965_cast)[name = tensor("attn_weights_233_cast")]; + tensor attn_weights_235_cast = mul(x = attn_weights_233_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_235_cast")]; + tensor var_5971_cast = softmax(axis = var_4927, x = attn_weights_235_cast)[name = tensor("op_5971_cast")]; + tensor attn_117_transpose_x_0 = const()[name = tensor("attn_117_transpose_x_0"), val = tensor(false)]; + tensor attn_117_transpose_y_0 = const()[name = tensor("attn_117_transpose_y_0"), val = tensor(true)]; + tensor attn_117_cast = matmul(transpose_x = attn_117_transpose_x_0, transpose_y = attn_117_transpose_y_0, x = var_5967_cast, y = var_5971_cast)[name = tensor("attn_117_cast")]; + tensor var_5975 = const()[name = tensor("op_5975"), val = tensor([2, 1280, 1, -1])]; + tensor input_367_cast = reshape(shape = var_5975, x = attn_117_cast)[name = tensor("input_367_cast")]; + tensor var_5980 = const()[name = tensor("op_5980"), val = tensor([1, 1])]; + tensor var_5982 = const()[name = tensor("op_5982"), val = tensor([1, 1])]; + tensor var_5984_pad_type_0 = const()[name = tensor("op_5984_pad_type_0"), val = tensor("custom")]; + tensor var_5984_pad_0 = const()[name = tensor("op_5984_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2083509696)))]; + tensor mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2086786560)))]; + tensor var_5984_cast = conv(bias = mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_5982, groups = var_4943, pad = var_5984_pad_0, pad_type = var_5984_pad_type_0, strides = var_5980, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_367_cast)[name = tensor("op_5984_cast")]; + tensor inputs_177_cast = add(x = var_5984_cast, y = inputs_175_cast)[name = tensor("inputs_177_cast")]; + tensor var_5988 = const()[name = tensor("op_5988"), val = tensor([1])]; + tensor channels_mean_177_cast = reduce_mean(axes = var_5988, keep_dims = var_4938, x = inputs_177_cast)[name = tensor("channels_mean_177_cast")]; + tensor zero_mean_177_cast = sub(x = inputs_177_cast, y = channels_mean_177_cast)[name = tensor("zero_mean_177_cast")]; + tensor zero_mean_sq_177_cast = mul(x = zero_mean_177_cast, y = zero_mean_177_cast)[name = tensor("zero_mean_sq_177_cast")]; + tensor var_5992 = const()[name = tensor("op_5992"), val = tensor([1])]; + tensor var_5993_cast = reduce_mean(axes = var_5992, keep_dims = var_4938, x = zero_mean_sq_177_cast)[name = tensor("op_5993_cast")]; + tensor var_5994_to_fp16 = const()[name = tensor("op_5994_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5995_cast = add(x = var_5993_cast, y = var_5994_to_fp16)[name = tensor("op_5995_cast")]; + tensor denom_177_epsilon_0_to_fp16 = const()[name = tensor("denom_177_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_177_cast = rsqrt(epsilon = denom_177_epsilon_0_to_fp16, x = var_5995_cast)[name = tensor("denom_177_cast")]; + tensor out_177_cast = mul(x = zero_mean_177_cast, y = denom_177_cast)[name = tensor("out_177_cast")]; + tensor var_5999_to_fp16 = const()[name = tensor("op_5999_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2086789184)))]; + tensor var_6000_cast = add(x = out_177_cast, y = var_5999_to_fp16)[name = tensor("op_6000_cast")]; + tensor var_6002_to_fp16 = const()[name = tensor("op_6002_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2086791808)))]; + tensor hidden_states_241_cast = mul(x = var_6000_cast, y = var_6002_to_fp16)[name = tensor("hidden_states_241_cast")]; + tensor var_6009 = const()[name = tensor("op_6009"), val = tensor([1, 1])]; + tensor var_6011 = const()[name = tensor("op_6011"), val = tensor([1, 1])]; + tensor q_119_pad_type_0 = const()[name = tensor("q_119_pad_type_0"), val = tensor("custom")]; + tensor q_119_pad_0 = const()[name = tensor("q_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2086794432)))]; + tensor q_119_cast = conv(dilations = var_6011, groups = var_4943, pad = q_119_pad_0, pad_type = q_119_pad_type_0, strides = var_6009, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_241_cast)[name = tensor("q_119_cast")]; + tensor var_6015 = const()[name = tensor("op_6015"), val = tensor([1, 1])]; + tensor var_6017 = const()[name = tensor("op_6017"), val = tensor([1, 1])]; + tensor k_119_pad_type_0 = const()[name = tensor("k_119_pad_type_0"), val = tensor("custom")]; + tensor k_119_pad_0 = const()[name = tensor("k_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2090071296)))]; + tensor k_119_cast = conv(dilations = var_6017, groups = var_4943, pad = k_119_pad_0, pad_type = k_119_pad_type_0, strides = var_6015, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_119_cast")]; + tensor var_6021 = const()[name = tensor("op_6021"), val = tensor([1, 1])]; + tensor var_6023 = const()[name = tensor("op_6023"), val = tensor([1, 1])]; + tensor v_119_pad_type_0 = const()[name = tensor("v_119_pad_type_0"), val = tensor("custom")]; + tensor v_119_pad_0 = const()[name = tensor("v_119_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2095314240)))]; + tensor v_119_cast = conv(dilations = var_6023, groups = var_4943, pad = v_119_pad_0, pad_type = v_119_pad_type_0, strides = var_6021, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_119_cast")]; + tensor var_6027 = const()[name = tensor("op_6027"), val = tensor([2, 20, 64, -1])]; + tensor var_6028_cast = reshape(shape = var_6027, x = q_119_cast)[name = tensor("op_6028_cast")]; + tensor var_6029 = const()[name = tensor("op_6029"), val = tensor([2, 20, 64, -1])]; + tensor var_6030_cast = reshape(shape = var_6029, x = k_119_cast)[name = tensor("op_6030_cast")]; + tensor var_6031 = const()[name = tensor("op_6031"), val = tensor([2, 20, 64, -1])]; + tensor var_6032_cast = reshape(shape = var_6031, x = v_119_cast)[name = tensor("op_6032_cast")]; + tensor attn_weights_237_transpose_x_0 = const()[name = tensor("attn_weights_237_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_237_transpose_y_0 = const()[name = tensor("attn_weights_237_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_237_cast = matmul(transpose_x = attn_weights_237_transpose_x_0, transpose_y = attn_weights_237_transpose_y_0, x = var_6028_cast, y = var_6030_cast)[name = tensor("attn_weights_237_cast")]; + tensor attn_weights_239_cast = mul(x = attn_weights_237_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_239_cast")]; + tensor var_6036_cast = softmax(axis = var_4927, x = attn_weights_239_cast)[name = tensor("op_6036_cast")]; + tensor attn_119_transpose_x_0 = const()[name = tensor("attn_119_transpose_x_0"), val = tensor(false)]; + tensor attn_119_transpose_y_0 = const()[name = tensor("attn_119_transpose_y_0"), val = tensor(true)]; + tensor attn_119_cast = matmul(transpose_x = attn_119_transpose_x_0, transpose_y = attn_119_transpose_y_0, x = var_6032_cast, y = var_6036_cast)[name = tensor("attn_119_cast")]; + tensor var_6040 = const()[name = tensor("op_6040"), val = tensor([2, 1280, 1, -1])]; + tensor input_369_cast = reshape(shape = var_6040, x = attn_119_cast)[name = tensor("input_369_cast")]; + tensor var_6045 = const()[name = tensor("op_6045"), val = tensor([1, 1])]; + tensor var_6047 = const()[name = tensor("op_6047"), val = tensor([1, 1])]; + tensor var_6049_pad_type_0 = const()[name = tensor("op_6049_pad_type_0"), val = tensor("custom")]; + tensor var_6049_pad_0 = const()[name = tensor("op_6049_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2100557184)))]; + tensor mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2103834048)))]; + tensor var_6049_cast = conv(bias = mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_6047, groups = var_4943, pad = var_6049_pad_0, pad_type = var_6049_pad_type_0, strides = var_6045, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_369_cast)[name = tensor("op_6049_cast")]; + tensor inputs_179_cast = add(x = var_6049_cast, y = inputs_177_cast)[name = tensor("inputs_179_cast")]; + tensor var_6053 = const()[name = tensor("op_6053"), val = tensor([1])]; + tensor channels_mean_179_cast = reduce_mean(axes = var_6053, keep_dims = var_4938, x = inputs_179_cast)[name = tensor("channels_mean_179_cast")]; + tensor zero_mean_179_cast = sub(x = inputs_179_cast, y = channels_mean_179_cast)[name = tensor("zero_mean_179_cast")]; + tensor zero_mean_sq_179_cast = mul(x = zero_mean_179_cast, y = zero_mean_179_cast)[name = tensor("zero_mean_sq_179_cast")]; + tensor var_6057 = const()[name = tensor("op_6057"), val = tensor([1])]; + tensor var_6058_cast = reduce_mean(axes = var_6057, keep_dims = var_4938, x = zero_mean_sq_179_cast)[name = tensor("op_6058_cast")]; + tensor var_6059_to_fp16 = const()[name = tensor("op_6059_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6060_cast = add(x = var_6058_cast, y = var_6059_to_fp16)[name = tensor("op_6060_cast")]; + tensor denom_179_epsilon_0_to_fp16 = const()[name = tensor("denom_179_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_179_cast = rsqrt(epsilon = denom_179_epsilon_0_to_fp16, x = var_6060_cast)[name = tensor("denom_179_cast")]; + tensor out_179_cast = mul(x = zero_mean_179_cast, y = denom_179_cast)[name = tensor("out_179_cast")]; + tensor var_6064_to_fp16 = const()[name = tensor("op_6064_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2103836672)))]; + tensor var_6065_cast = add(x = out_179_cast, y = var_6064_to_fp16)[name = tensor("op_6065_cast")]; + tensor var_6067_to_fp16 = const()[name = tensor("op_6067_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2103839296)))]; + tensor input_371_cast = mul(x = var_6065_cast, y = var_6067_to_fp16)[name = tensor("input_371_cast")]; + tensor var_6075 = const()[name = tensor("op_6075"), val = tensor([1, 1])]; + tensor var_6077 = const()[name = tensor("op_6077"), val = tensor([1, 1])]; + tensor var_6079_pad_type_0 = const()[name = tensor("op_6079_pad_type_0"), val = tensor("custom")]; + tensor var_6079_pad_0 = const()[name = tensor("op_6079_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2103841920)))]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2130056384)))]; + tensor var_6079_cast = conv(bias = mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_6077, groups = var_4943, pad = var_6079_pad_0, pad_type = var_6079_pad_type_0, strides = var_6075, weight = mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_371_cast)[name = tensor("op_6079_cast")]; + tensor var_6080_split_sizes_0 = const()[name = tensor("op_6080_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6080_axis_0 = const()[name = tensor("op_6080_axis_0"), val = tensor(1)]; + tensor var_6080_cast_0, tensor var_6080_cast_1 = split(axis = var_6080_axis_0, split_sizes = var_6080_split_sizes_0, x = var_6079_cast)[name = tensor("op_6080_cast")]; + tensor var_6082_mode_0 = const()[name = tensor("op_6082_mode_0"), val = tensor("EXACT")]; + tensor var_6082_cast = gelu(mode = var_6082_mode_0, x = var_6080_cast_1)[name = tensor("op_6082_cast")]; + tensor input_373_cast = mul(x = var_6080_cast_0, y = var_6082_cast)[name = tensor("input_373_cast")]; + tensor var_6086 = const()[name = tensor("op_6086"), val = tensor([1, 1])]; + tensor var_6088 = const()[name = tensor("op_6088"), val = tensor([1, 1])]; + tensor var_6090_pad_type_0 = const()[name = tensor("op_6090_pad_type_0"), val = tensor("custom")]; + tensor var_6090_pad_0 = const()[name = tensor("op_6090_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2130076928)))]; + tensor mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2143184192)))]; + tensor var_6090_cast = conv(bias = mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_6088, groups = var_4943, pad = var_6090_pad_0, pad_type = var_6090_pad_type_0, strides = var_6086, weight = mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_373_cast)[name = tensor("op_6090_cast")]; + tensor inputs_181_cast = add(x = var_6090_cast, y = inputs_179_cast)[name = tensor("inputs_181_cast")]; + tensor var_6100 = const()[name = tensor("op_6100"), val = tensor([1])]; + tensor channels_mean_181_cast = reduce_mean(axes = var_6100, keep_dims = var_4938, x = inputs_181_cast)[name = tensor("channels_mean_181_cast")]; + tensor zero_mean_181_cast = sub(x = inputs_181_cast, y = channels_mean_181_cast)[name = tensor("zero_mean_181_cast")]; + tensor zero_mean_sq_181_cast = mul(x = zero_mean_181_cast, y = zero_mean_181_cast)[name = tensor("zero_mean_sq_181_cast")]; + tensor var_6104 = const()[name = tensor("op_6104"), val = tensor([1])]; + tensor var_6105_cast = reduce_mean(axes = var_6104, keep_dims = var_4938, x = zero_mean_sq_181_cast)[name = tensor("op_6105_cast")]; + tensor var_6106_to_fp16 = const()[name = tensor("op_6106_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6107_cast = add(x = var_6105_cast, y = var_6106_to_fp16)[name = tensor("op_6107_cast")]; + tensor denom_181_epsilon_0_to_fp16 = const()[name = tensor("denom_181_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_181_cast = rsqrt(epsilon = denom_181_epsilon_0_to_fp16, x = var_6107_cast)[name = tensor("denom_181_cast")]; + tensor out_181_cast = mul(x = zero_mean_181_cast, y = denom_181_cast)[name = tensor("out_181_cast")]; + tensor var_6111_to_fp16 = const()[name = tensor("op_6111_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2143186816)))]; + tensor var_6112_cast = add(x = out_181_cast, y = var_6111_to_fp16)[name = tensor("op_6112_cast")]; + tensor var_6114_to_fp16 = const()[name = tensor("op_6114_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2143189440)))]; + tensor hidden_states_245_cast = mul(x = var_6112_cast, y = var_6114_to_fp16)[name = tensor("hidden_states_245_cast")]; + tensor var_6121 = const()[name = tensor("op_6121"), val = tensor([1, 1])]; + tensor var_6123 = const()[name = tensor("op_6123"), val = tensor([1, 1])]; + tensor q_121_pad_type_0 = const()[name = tensor("q_121_pad_type_0"), val = tensor("custom")]; + tensor q_121_pad_0 = const()[name = tensor("q_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2143192064)))]; + tensor q_121_cast = conv(dilations = var_6123, groups = var_4943, pad = q_121_pad_0, pad_type = q_121_pad_type_0, strides = var_6121, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_245_cast)[name = tensor("q_121_cast")]; + tensor var_6127 = const()[name = tensor("op_6127"), val = tensor([1, 1])]; + tensor var_6129 = const()[name = tensor("op_6129"), val = tensor([1, 1])]; + tensor k_121_pad_type_0 = const()[name = tensor("k_121_pad_type_0"), val = tensor("custom")]; + tensor k_121_pad_0 = const()[name = tensor("k_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2146468928)))]; + tensor k_121_cast = conv(dilations = var_6129, groups = var_4943, pad = k_121_pad_0, pad_type = k_121_pad_type_0, strides = var_6127, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_245_cast)[name = tensor("k_121_cast")]; + tensor var_6133 = const()[name = tensor("op_6133"), val = tensor([1, 1])]; + tensor var_6135 = const()[name = tensor("op_6135"), val = tensor([1, 1])]; + tensor v_121_pad_type_0 = const()[name = tensor("v_121_pad_type_0"), val = tensor("custom")]; + tensor v_121_pad_0 = const()[name = tensor("v_121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2149745792)))]; + tensor v_121_cast = conv(dilations = var_6135, groups = var_4943, pad = v_121_pad_0, pad_type = v_121_pad_type_0, strides = var_6133, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_245_cast)[name = tensor("v_121_cast")]; + tensor var_6139 = const()[name = tensor("op_6139"), val = tensor([2, 20, 64, -1])]; + tensor var_6140_cast = reshape(shape = var_6139, x = q_121_cast)[name = tensor("op_6140_cast")]; + tensor var_6141 = const()[name = tensor("op_6141"), val = tensor([2, 20, 64, -1])]; + tensor var_6142_cast = reshape(shape = var_6141, x = k_121_cast)[name = tensor("op_6142_cast")]; + tensor var_6143 = const()[name = tensor("op_6143"), val = tensor([2, 20, 64, -1])]; + tensor var_6144_cast = reshape(shape = var_6143, x = v_121_cast)[name = tensor("op_6144_cast")]; + tensor attn_weights_241_transpose_x_0 = const()[name = tensor("attn_weights_241_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_241_transpose_y_0 = const()[name = tensor("attn_weights_241_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_241_cast = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_6140_cast, y = var_6142_cast)[name = tensor("attn_weights_241_cast")]; + tensor attn_weights_243_cast = mul(x = attn_weights_241_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_243_cast")]; + tensor var_6148_cast = softmax(axis = var_4927, x = attn_weights_243_cast)[name = tensor("op_6148_cast")]; + tensor attn_121_transpose_x_0 = const()[name = tensor("attn_121_transpose_x_0"), val = tensor(false)]; + tensor attn_121_transpose_y_0 = const()[name = tensor("attn_121_transpose_y_0"), val = tensor(true)]; + tensor attn_121_cast = matmul(transpose_x = attn_121_transpose_x_0, transpose_y = attn_121_transpose_y_0, x = var_6144_cast, y = var_6148_cast)[name = tensor("attn_121_cast")]; + tensor var_6152 = const()[name = tensor("op_6152"), val = tensor([2, 1280, 1, -1])]; + tensor input_375_cast = reshape(shape = var_6152, x = attn_121_cast)[name = tensor("input_375_cast")]; + tensor var_6157 = const()[name = tensor("op_6157"), val = tensor([1, 1])]; + tensor var_6159 = const()[name = tensor("op_6159"), val = tensor([1, 1])]; + tensor var_6161_pad_type_0 = const()[name = tensor("op_6161_pad_type_0"), val = tensor("custom")]; + tensor var_6161_pad_0 = const()[name = tensor("op_6161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2153022656)))]; + tensor mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2156299520)))]; + tensor var_6161_cast = conv(bias = mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_6159, groups = var_4943, pad = var_6161_pad_0, pad_type = var_6161_pad_type_0, strides = var_6157, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_375_cast)[name = tensor("op_6161_cast")]; + tensor inputs_183_cast = add(x = var_6161_cast, y = inputs_181_cast)[name = tensor("inputs_183_cast")]; + tensor var_6165 = const()[name = tensor("op_6165"), val = tensor([1])]; + tensor channels_mean_183_cast = reduce_mean(axes = var_6165, keep_dims = var_4938, x = inputs_183_cast)[name = tensor("channels_mean_183_cast")]; + tensor zero_mean_183_cast = sub(x = inputs_183_cast, y = channels_mean_183_cast)[name = tensor("zero_mean_183_cast")]; + tensor zero_mean_sq_183_cast = mul(x = zero_mean_183_cast, y = zero_mean_183_cast)[name = tensor("zero_mean_sq_183_cast")]; + tensor var_6169 = const()[name = tensor("op_6169"), val = tensor([1])]; + tensor var_6170_cast = reduce_mean(axes = var_6169, keep_dims = var_4938, x = zero_mean_sq_183_cast)[name = tensor("op_6170_cast")]; + tensor var_6171_to_fp16 = const()[name = tensor("op_6171_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6172_cast = add(x = var_6170_cast, y = var_6171_to_fp16)[name = tensor("op_6172_cast")]; + tensor denom_183_epsilon_0_to_fp16 = const()[name = tensor("denom_183_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_183_cast = rsqrt(epsilon = denom_183_epsilon_0_to_fp16, x = var_6172_cast)[name = tensor("denom_183_cast")]; + tensor out_183_cast = mul(x = zero_mean_183_cast, y = denom_183_cast)[name = tensor("out_183_cast")]; + tensor var_6176_to_fp16 = const()[name = tensor("op_6176_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2156302144)))]; + tensor var_6177_cast = add(x = out_183_cast, y = var_6176_to_fp16)[name = tensor("op_6177_cast")]; + tensor var_6179_to_fp16 = const()[name = tensor("op_6179_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2156304768)))]; + tensor hidden_states_247_cast = mul(x = var_6177_cast, y = var_6179_to_fp16)[name = tensor("hidden_states_247_cast")]; + tensor var_6186 = const()[name = tensor("op_6186"), val = tensor([1, 1])]; + tensor var_6188 = const()[name = tensor("op_6188"), val = tensor([1, 1])]; + tensor q_123_pad_type_0 = const()[name = tensor("q_123_pad_type_0"), val = tensor("custom")]; + tensor q_123_pad_0 = const()[name = tensor("q_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2156307392)))]; + tensor q_123_cast = conv(dilations = var_6188, groups = var_4943, pad = q_123_pad_0, pad_type = q_123_pad_type_0, strides = var_6186, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_247_cast)[name = tensor("q_123_cast")]; + tensor var_6192 = const()[name = tensor("op_6192"), val = tensor([1, 1])]; + tensor var_6194 = const()[name = tensor("op_6194"), val = tensor([1, 1])]; + tensor k_123_pad_type_0 = const()[name = tensor("k_123_pad_type_0"), val = tensor("custom")]; + tensor k_123_pad_0 = const()[name = tensor("k_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2159584256)))]; + tensor k_123_cast = conv(dilations = var_6194, groups = var_4943, pad = k_123_pad_0, pad_type = k_123_pad_type_0, strides = var_6192, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_123_cast")]; + tensor var_6198 = const()[name = tensor("op_6198"), val = tensor([1, 1])]; + tensor var_6200 = const()[name = tensor("op_6200"), val = tensor([1, 1])]; + tensor v_123_pad_type_0 = const()[name = tensor("v_123_pad_type_0"), val = tensor("custom")]; + tensor v_123_pad_0 = const()[name = tensor("v_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2164827200)))]; + tensor v_123_cast = conv(dilations = var_6200, groups = var_4943, pad = v_123_pad_0, pad_type = v_123_pad_type_0, strides = var_6198, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_123_cast")]; + tensor var_6204 = const()[name = tensor("op_6204"), val = tensor([2, 20, 64, -1])]; + tensor var_6205_cast = reshape(shape = var_6204, x = q_123_cast)[name = tensor("op_6205_cast")]; + tensor var_6206 = const()[name = tensor("op_6206"), val = tensor([2, 20, 64, -1])]; + tensor var_6207_cast = reshape(shape = var_6206, x = k_123_cast)[name = tensor("op_6207_cast")]; + tensor var_6208 = const()[name = tensor("op_6208"), val = tensor([2, 20, 64, -1])]; + tensor var_6209_cast = reshape(shape = var_6208, x = v_123_cast)[name = tensor("op_6209_cast")]; + tensor attn_weights_245_transpose_x_0 = const()[name = tensor("attn_weights_245_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_245_transpose_y_0 = const()[name = tensor("attn_weights_245_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_245_cast = matmul(transpose_x = attn_weights_245_transpose_x_0, transpose_y = attn_weights_245_transpose_y_0, x = var_6205_cast, y = var_6207_cast)[name = tensor("attn_weights_245_cast")]; + tensor attn_weights_247_cast = mul(x = attn_weights_245_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_247_cast")]; + tensor var_6213_cast = softmax(axis = var_4927, x = attn_weights_247_cast)[name = tensor("op_6213_cast")]; + tensor attn_123_transpose_x_0 = const()[name = tensor("attn_123_transpose_x_0"), val = tensor(false)]; + tensor attn_123_transpose_y_0 = const()[name = tensor("attn_123_transpose_y_0"), val = tensor(true)]; + tensor attn_123_cast = matmul(transpose_x = attn_123_transpose_x_0, transpose_y = attn_123_transpose_y_0, x = var_6209_cast, y = var_6213_cast)[name = tensor("attn_123_cast")]; + tensor var_6217 = const()[name = tensor("op_6217"), val = tensor([2, 1280, 1, -1])]; + tensor input_377_cast = reshape(shape = var_6217, x = attn_123_cast)[name = tensor("input_377_cast")]; + tensor var_6222 = const()[name = tensor("op_6222"), val = tensor([1, 1])]; + tensor var_6224 = const()[name = tensor("op_6224"), val = tensor([1, 1])]; + tensor var_6226_pad_type_0 = const()[name = tensor("op_6226_pad_type_0"), val = tensor("custom")]; + tensor var_6226_pad_0 = const()[name = tensor("op_6226_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2170070144)))]; + tensor mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2173347008)))]; + tensor var_6226_cast = conv(bias = mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_6224, groups = var_4943, pad = var_6226_pad_0, pad_type = var_6226_pad_type_0, strides = var_6222, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_377_cast)[name = tensor("op_6226_cast")]; + tensor inputs_185_cast = add(x = var_6226_cast, y = inputs_183_cast)[name = tensor("inputs_185_cast")]; + tensor var_6230 = const()[name = tensor("op_6230"), val = tensor([1])]; + tensor channels_mean_185_cast = reduce_mean(axes = var_6230, keep_dims = var_4938, x = inputs_185_cast)[name = tensor("channels_mean_185_cast")]; + tensor zero_mean_185_cast = sub(x = inputs_185_cast, y = channels_mean_185_cast)[name = tensor("zero_mean_185_cast")]; + tensor zero_mean_sq_185_cast = mul(x = zero_mean_185_cast, y = zero_mean_185_cast)[name = tensor("zero_mean_sq_185_cast")]; + tensor var_6234 = const()[name = tensor("op_6234"), val = tensor([1])]; + tensor var_6235_cast = reduce_mean(axes = var_6234, keep_dims = var_4938, x = zero_mean_sq_185_cast)[name = tensor("op_6235_cast")]; + tensor var_6236_to_fp16 = const()[name = tensor("op_6236_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6237_cast = add(x = var_6235_cast, y = var_6236_to_fp16)[name = tensor("op_6237_cast")]; + tensor denom_185_epsilon_0_to_fp16 = const()[name = tensor("denom_185_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_185_cast = rsqrt(epsilon = denom_185_epsilon_0_to_fp16, x = var_6237_cast)[name = tensor("denom_185_cast")]; + tensor out_185_cast = mul(x = zero_mean_185_cast, y = denom_185_cast)[name = tensor("out_185_cast")]; + tensor var_6241_to_fp16 = const()[name = tensor("op_6241_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2173349632)))]; + tensor var_6242_cast = add(x = out_185_cast, y = var_6241_to_fp16)[name = tensor("op_6242_cast")]; + tensor var_6244_to_fp16 = const()[name = tensor("op_6244_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2173352256)))]; + tensor input_379_cast = mul(x = var_6242_cast, y = var_6244_to_fp16)[name = tensor("input_379_cast")]; + tensor var_6252 = const()[name = tensor("op_6252"), val = tensor([1, 1])]; + tensor var_6254 = const()[name = tensor("op_6254"), val = tensor([1, 1])]; + tensor var_6256_pad_type_0 = const()[name = tensor("op_6256_pad_type_0"), val = tensor("custom")]; + tensor var_6256_pad_0 = const()[name = tensor("op_6256_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2173354880)))]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2199569344)))]; + tensor var_6256_cast = conv(bias = mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_6254, groups = var_4943, pad = var_6256_pad_0, pad_type = var_6256_pad_type_0, strides = var_6252, weight = mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_379_cast)[name = tensor("op_6256_cast")]; + tensor var_6257_split_sizes_0 = const()[name = tensor("op_6257_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6257_axis_0 = const()[name = tensor("op_6257_axis_0"), val = tensor(1)]; + tensor var_6257_cast_0, tensor var_6257_cast_1 = split(axis = var_6257_axis_0, split_sizes = var_6257_split_sizes_0, x = var_6256_cast)[name = tensor("op_6257_cast")]; + tensor var_6259_mode_0 = const()[name = tensor("op_6259_mode_0"), val = tensor("EXACT")]; + tensor var_6259_cast = gelu(mode = var_6259_mode_0, x = var_6257_cast_1)[name = tensor("op_6259_cast")]; + tensor input_381_cast = mul(x = var_6257_cast_0, y = var_6259_cast)[name = tensor("input_381_cast")]; + tensor var_6263 = const()[name = tensor("op_6263"), val = tensor([1, 1])]; + tensor var_6265 = const()[name = tensor("op_6265"), val = tensor([1, 1])]; + tensor var_6267_pad_type_0 = const()[name = tensor("op_6267_pad_type_0"), val = tensor("custom")]; + tensor var_6267_pad_0 = const()[name = tensor("op_6267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2199589888)))]; + tensor mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2212697152)))]; + tensor var_6267_cast = conv(bias = mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_6265, groups = var_4943, pad = var_6267_pad_0, pad_type = var_6267_pad_type_0, strides = var_6263, weight = mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_381_cast)[name = tensor("op_6267_cast")]; + tensor inputs_187_cast = add(x = var_6267_cast, y = inputs_185_cast)[name = tensor("inputs_187_cast")]; + tensor var_6277 = const()[name = tensor("op_6277"), val = tensor([1])]; + tensor channels_mean_187_cast = reduce_mean(axes = var_6277, keep_dims = var_4938, x = inputs_187_cast)[name = tensor("channels_mean_187_cast")]; + tensor zero_mean_187_cast = sub(x = inputs_187_cast, y = channels_mean_187_cast)[name = tensor("zero_mean_187_cast")]; + tensor zero_mean_sq_187_cast = mul(x = zero_mean_187_cast, y = zero_mean_187_cast)[name = tensor("zero_mean_sq_187_cast")]; + tensor var_6281 = const()[name = tensor("op_6281"), val = tensor([1])]; + tensor var_6282_cast = reduce_mean(axes = var_6281, keep_dims = var_4938, x = zero_mean_sq_187_cast)[name = tensor("op_6282_cast")]; + tensor var_6283_to_fp16 = const()[name = tensor("op_6283_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6284_cast = add(x = var_6282_cast, y = var_6283_to_fp16)[name = tensor("op_6284_cast")]; + tensor denom_187_epsilon_0_to_fp16 = const()[name = tensor("denom_187_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_187_cast = rsqrt(epsilon = denom_187_epsilon_0_to_fp16, x = var_6284_cast)[name = tensor("denom_187_cast")]; + tensor out_187_cast = mul(x = zero_mean_187_cast, y = denom_187_cast)[name = tensor("out_187_cast")]; + tensor var_6288_to_fp16 = const()[name = tensor("op_6288_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2212699776)))]; + tensor var_6289_cast = add(x = out_187_cast, y = var_6288_to_fp16)[name = tensor("op_6289_cast")]; + tensor var_6291_to_fp16 = const()[name = tensor("op_6291_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2212702400)))]; + tensor hidden_states_251_cast = mul(x = var_6289_cast, y = var_6291_to_fp16)[name = tensor("hidden_states_251_cast")]; + tensor var_6298 = const()[name = tensor("op_6298"), val = tensor([1, 1])]; + tensor var_6300 = const()[name = tensor("op_6300"), val = tensor([1, 1])]; + tensor q_125_pad_type_0 = const()[name = tensor("q_125_pad_type_0"), val = tensor("custom")]; + tensor q_125_pad_0 = const()[name = tensor("q_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2212705024)))]; + tensor q_125_cast = conv(dilations = var_6300, groups = var_4943, pad = q_125_pad_0, pad_type = q_125_pad_type_0, strides = var_6298, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_251_cast)[name = tensor("q_125_cast")]; + tensor var_6304 = const()[name = tensor("op_6304"), val = tensor([1, 1])]; + tensor var_6306 = const()[name = tensor("op_6306"), val = tensor([1, 1])]; + tensor k_125_pad_type_0 = const()[name = tensor("k_125_pad_type_0"), val = tensor("custom")]; + tensor k_125_pad_0 = const()[name = tensor("k_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2215981888)))]; + tensor k_125_cast = conv(dilations = var_6306, groups = var_4943, pad = k_125_pad_0, pad_type = k_125_pad_type_0, strides = var_6304, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_251_cast)[name = tensor("k_125_cast")]; + tensor var_6310 = const()[name = tensor("op_6310"), val = tensor([1, 1])]; + tensor var_6312 = const()[name = tensor("op_6312"), val = tensor([1, 1])]; + tensor v_125_pad_type_0 = const()[name = tensor("v_125_pad_type_0"), val = tensor("custom")]; + tensor v_125_pad_0 = const()[name = tensor("v_125_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2219258752)))]; + tensor v_125_cast = conv(dilations = var_6312, groups = var_4943, pad = v_125_pad_0, pad_type = v_125_pad_type_0, strides = var_6310, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_251_cast)[name = tensor("v_125_cast")]; + tensor var_6316 = const()[name = tensor("op_6316"), val = tensor([2, 20, 64, -1])]; + tensor var_6317_cast = reshape(shape = var_6316, x = q_125_cast)[name = tensor("op_6317_cast")]; + tensor var_6318 = const()[name = tensor("op_6318"), val = tensor([2, 20, 64, -1])]; + tensor var_6319_cast = reshape(shape = var_6318, x = k_125_cast)[name = tensor("op_6319_cast")]; + tensor var_6320 = const()[name = tensor("op_6320"), val = tensor([2, 20, 64, -1])]; + tensor var_6321_cast = reshape(shape = var_6320, x = v_125_cast)[name = tensor("op_6321_cast")]; + tensor attn_weights_249_transpose_x_0 = const()[name = tensor("attn_weights_249_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_249_transpose_y_0 = const()[name = tensor("attn_weights_249_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_249_cast = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_6317_cast, y = var_6319_cast)[name = tensor("attn_weights_249_cast")]; + tensor attn_weights_251_cast = mul(x = attn_weights_249_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_251_cast")]; + tensor var_6325_cast = softmax(axis = var_4927, x = attn_weights_251_cast)[name = tensor("op_6325_cast")]; + tensor attn_125_transpose_x_0 = const()[name = tensor("attn_125_transpose_x_0"), val = tensor(false)]; + tensor attn_125_transpose_y_0 = const()[name = tensor("attn_125_transpose_y_0"), val = tensor(true)]; + tensor attn_125_cast = matmul(transpose_x = attn_125_transpose_x_0, transpose_y = attn_125_transpose_y_0, x = var_6321_cast, y = var_6325_cast)[name = tensor("attn_125_cast")]; + tensor var_6329 = const()[name = tensor("op_6329"), val = tensor([2, 1280, 1, -1])]; + tensor input_383_cast = reshape(shape = var_6329, x = attn_125_cast)[name = tensor("input_383_cast")]; + tensor var_6334 = const()[name = tensor("op_6334"), val = tensor([1, 1])]; + tensor var_6336 = const()[name = tensor("op_6336"), val = tensor([1, 1])]; + tensor var_6338_pad_type_0 = const()[name = tensor("op_6338_pad_type_0"), val = tensor("custom")]; + tensor var_6338_pad_0 = const()[name = tensor("op_6338_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2222535616)))]; + tensor mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2225812480)))]; + tensor var_6338_cast = conv(bias = mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_6336, groups = var_4943, pad = var_6338_pad_0, pad_type = var_6338_pad_type_0, strides = var_6334, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_383_cast)[name = tensor("op_6338_cast")]; + tensor inputs_189_cast = add(x = var_6338_cast, y = inputs_187_cast)[name = tensor("inputs_189_cast")]; + tensor var_6342 = const()[name = tensor("op_6342"), val = tensor([1])]; + tensor channels_mean_189_cast = reduce_mean(axes = var_6342, keep_dims = var_4938, x = inputs_189_cast)[name = tensor("channels_mean_189_cast")]; + tensor zero_mean_189_cast = sub(x = inputs_189_cast, y = channels_mean_189_cast)[name = tensor("zero_mean_189_cast")]; + tensor zero_mean_sq_189_cast = mul(x = zero_mean_189_cast, y = zero_mean_189_cast)[name = tensor("zero_mean_sq_189_cast")]; + tensor var_6346 = const()[name = tensor("op_6346"), val = tensor([1])]; + tensor var_6347_cast = reduce_mean(axes = var_6346, keep_dims = var_4938, x = zero_mean_sq_189_cast)[name = tensor("op_6347_cast")]; + tensor var_6348_to_fp16 = const()[name = tensor("op_6348_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6349_cast = add(x = var_6347_cast, y = var_6348_to_fp16)[name = tensor("op_6349_cast")]; + tensor denom_189_epsilon_0_to_fp16 = const()[name = tensor("denom_189_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_189_cast = rsqrt(epsilon = denom_189_epsilon_0_to_fp16, x = var_6349_cast)[name = tensor("denom_189_cast")]; + tensor out_189_cast = mul(x = zero_mean_189_cast, y = denom_189_cast)[name = tensor("out_189_cast")]; + tensor var_6353_to_fp16 = const()[name = tensor("op_6353_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2225815104)))]; + tensor var_6354_cast = add(x = out_189_cast, y = var_6353_to_fp16)[name = tensor("op_6354_cast")]; + tensor var_6356_to_fp16 = const()[name = tensor("op_6356_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2225817728)))]; + tensor hidden_states_253_cast = mul(x = var_6354_cast, y = var_6356_to_fp16)[name = tensor("hidden_states_253_cast")]; + tensor var_6363 = const()[name = tensor("op_6363"), val = tensor([1, 1])]; + tensor var_6365 = const()[name = tensor("op_6365"), val = tensor([1, 1])]; + tensor q_127_pad_type_0 = const()[name = tensor("q_127_pad_type_0"), val = tensor("custom")]; + tensor q_127_pad_0 = const()[name = tensor("q_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2225820352)))]; + tensor q_127_cast = conv(dilations = var_6365, groups = var_4943, pad = q_127_pad_0, pad_type = q_127_pad_type_0, strides = var_6363, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_253_cast)[name = tensor("q_127_cast")]; + tensor var_6369 = const()[name = tensor("op_6369"), val = tensor([1, 1])]; + tensor var_6371 = const()[name = tensor("op_6371"), val = tensor([1, 1])]; + tensor k_127_pad_type_0 = const()[name = tensor("k_127_pad_type_0"), val = tensor("custom")]; + tensor k_127_pad_0 = const()[name = tensor("k_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2229097216)))]; + tensor k_127_cast = conv(dilations = var_6371, groups = var_4943, pad = k_127_pad_0, pad_type = k_127_pad_type_0, strides = var_6369, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_127_cast")]; + tensor var_6375 = const()[name = tensor("op_6375"), val = tensor([1, 1])]; + tensor var_6377 = const()[name = tensor("op_6377"), val = tensor([1, 1])]; + tensor v_127_pad_type_0 = const()[name = tensor("v_127_pad_type_0"), val = tensor("custom")]; + tensor v_127_pad_0 = const()[name = tensor("v_127_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2234340160)))]; + tensor v_127_cast = conv(dilations = var_6377, groups = var_4943, pad = v_127_pad_0, pad_type = v_127_pad_type_0, strides = var_6375, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_127_cast")]; + tensor var_6381 = const()[name = tensor("op_6381"), val = tensor([2, 20, 64, -1])]; + tensor var_6382_cast = reshape(shape = var_6381, x = q_127_cast)[name = tensor("op_6382_cast")]; + tensor var_6383 = const()[name = tensor("op_6383"), val = tensor([2, 20, 64, -1])]; + tensor var_6384_cast = reshape(shape = var_6383, x = k_127_cast)[name = tensor("op_6384_cast")]; + tensor var_6385 = const()[name = tensor("op_6385"), val = tensor([2, 20, 64, -1])]; + tensor var_6386_cast = reshape(shape = var_6385, x = v_127_cast)[name = tensor("op_6386_cast")]; + tensor attn_weights_253_transpose_x_0 = const()[name = tensor("attn_weights_253_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_253_transpose_y_0 = const()[name = tensor("attn_weights_253_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_253_cast = matmul(transpose_x = attn_weights_253_transpose_x_0, transpose_y = attn_weights_253_transpose_y_0, x = var_6382_cast, y = var_6384_cast)[name = tensor("attn_weights_253_cast")]; + tensor attn_weights_255_cast = mul(x = attn_weights_253_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_255_cast")]; + tensor var_6390_cast = softmax(axis = var_4927, x = attn_weights_255_cast)[name = tensor("op_6390_cast")]; + tensor attn_127_transpose_x_0 = const()[name = tensor("attn_127_transpose_x_0"), val = tensor(false)]; + tensor attn_127_transpose_y_0 = const()[name = tensor("attn_127_transpose_y_0"), val = tensor(true)]; + tensor attn_127_cast = matmul(transpose_x = attn_127_transpose_x_0, transpose_y = attn_127_transpose_y_0, x = var_6386_cast, y = var_6390_cast)[name = tensor("attn_127_cast")]; + tensor var_6394 = const()[name = tensor("op_6394"), val = tensor([2, 1280, 1, -1])]; + tensor input_385_cast = reshape(shape = var_6394, x = attn_127_cast)[name = tensor("input_385_cast")]; + tensor var_6399 = const()[name = tensor("op_6399"), val = tensor([1, 1])]; + tensor var_6401 = const()[name = tensor("op_6401"), val = tensor([1, 1])]; + tensor var_6403_pad_type_0 = const()[name = tensor("op_6403_pad_type_0"), val = tensor("custom")]; + tensor var_6403_pad_0 = const()[name = tensor("op_6403_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2239583104)))]; + tensor mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2242859968)))]; + tensor var_6403_cast = conv(bias = mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_6401, groups = var_4943, pad = var_6403_pad_0, pad_type = var_6403_pad_type_0, strides = var_6399, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_385_cast)[name = tensor("op_6403_cast")]; + tensor inputs_191_cast = add(x = var_6403_cast, y = inputs_189_cast)[name = tensor("inputs_191_cast")]; + tensor var_6407 = const()[name = tensor("op_6407"), val = tensor([1])]; + tensor channels_mean_191_cast = reduce_mean(axes = var_6407, keep_dims = var_4938, x = inputs_191_cast)[name = tensor("channels_mean_191_cast")]; + tensor zero_mean_191_cast = sub(x = inputs_191_cast, y = channels_mean_191_cast)[name = tensor("zero_mean_191_cast")]; + tensor zero_mean_sq_191_cast = mul(x = zero_mean_191_cast, y = zero_mean_191_cast)[name = tensor("zero_mean_sq_191_cast")]; + tensor var_6411 = const()[name = tensor("op_6411"), val = tensor([1])]; + tensor var_6412_cast = reduce_mean(axes = var_6411, keep_dims = var_4938, x = zero_mean_sq_191_cast)[name = tensor("op_6412_cast")]; + tensor var_6413_to_fp16 = const()[name = tensor("op_6413_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6414_cast = add(x = var_6412_cast, y = var_6413_to_fp16)[name = tensor("op_6414_cast")]; + tensor denom_191_epsilon_0_to_fp16 = const()[name = tensor("denom_191_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_191_cast = rsqrt(epsilon = denom_191_epsilon_0_to_fp16, x = var_6414_cast)[name = tensor("denom_191_cast")]; + tensor out_191_cast = mul(x = zero_mean_191_cast, y = denom_191_cast)[name = tensor("out_191_cast")]; + tensor var_6418_to_fp16 = const()[name = tensor("op_6418_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2242862592)))]; + tensor var_6419_cast = add(x = out_191_cast, y = var_6418_to_fp16)[name = tensor("op_6419_cast")]; + tensor var_6421_to_fp16 = const()[name = tensor("op_6421_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2242865216)))]; + tensor input_387_cast = mul(x = var_6419_cast, y = var_6421_to_fp16)[name = tensor("input_387_cast")]; + tensor var_6429 = const()[name = tensor("op_6429"), val = tensor([1, 1])]; + tensor var_6431 = const()[name = tensor("op_6431"), val = tensor([1, 1])]; + tensor var_6433_pad_type_0 = const()[name = tensor("op_6433_pad_type_0"), val = tensor("custom")]; + tensor var_6433_pad_0 = const()[name = tensor("op_6433_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2242867840)))]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2269082304)))]; + tensor var_6433_cast = conv(bias = mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_6431, groups = var_4943, pad = var_6433_pad_0, pad_type = var_6433_pad_type_0, strides = var_6429, weight = mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_387_cast)[name = tensor("op_6433_cast")]; + tensor var_6434_split_sizes_0 = const()[name = tensor("op_6434_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6434_axis_0 = const()[name = tensor("op_6434_axis_0"), val = tensor(1)]; + tensor var_6434_cast_0, tensor var_6434_cast_1 = split(axis = var_6434_axis_0, split_sizes = var_6434_split_sizes_0, x = var_6433_cast)[name = tensor("op_6434_cast")]; + tensor var_6436_mode_0 = const()[name = tensor("op_6436_mode_0"), val = tensor("EXACT")]; + tensor var_6436_cast = gelu(mode = var_6436_mode_0, x = var_6434_cast_1)[name = tensor("op_6436_cast")]; + tensor input_389_cast = mul(x = var_6434_cast_0, y = var_6436_cast)[name = tensor("input_389_cast")]; + tensor var_6440 = const()[name = tensor("op_6440"), val = tensor([1, 1])]; + tensor var_6442 = const()[name = tensor("op_6442"), val = tensor([1, 1])]; + tensor var_6444_pad_type_0 = const()[name = tensor("op_6444_pad_type_0"), val = tensor("custom")]; + tensor var_6444_pad_0 = const()[name = tensor("op_6444_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2269102848)))]; + tensor mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2282210112)))]; + tensor var_6444_cast = conv(bias = mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_6442, groups = var_4943, pad = var_6444_pad_0, pad_type = var_6444_pad_type_0, strides = var_6440, weight = mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_389_cast)[name = tensor("op_6444_cast")]; + tensor inputs_193_cast = add(x = var_6444_cast, y = inputs_191_cast)[name = tensor("inputs_193_cast")]; + tensor var_6454 = const()[name = tensor("op_6454"), val = tensor([1])]; + tensor channels_mean_193_cast = reduce_mean(axes = var_6454, keep_dims = var_4938, x = inputs_193_cast)[name = tensor("channels_mean_193_cast")]; + tensor zero_mean_193_cast = sub(x = inputs_193_cast, y = channels_mean_193_cast)[name = tensor("zero_mean_193_cast")]; + tensor zero_mean_sq_193_cast = mul(x = zero_mean_193_cast, y = zero_mean_193_cast)[name = tensor("zero_mean_sq_193_cast")]; + tensor var_6458 = const()[name = tensor("op_6458"), val = tensor([1])]; + tensor var_6459_cast = reduce_mean(axes = var_6458, keep_dims = var_4938, x = zero_mean_sq_193_cast)[name = tensor("op_6459_cast")]; + tensor var_6460_to_fp16 = const()[name = tensor("op_6460_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6461_cast = add(x = var_6459_cast, y = var_6460_to_fp16)[name = tensor("op_6461_cast")]; + tensor denom_193_epsilon_0_to_fp16 = const()[name = tensor("denom_193_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_193_cast = rsqrt(epsilon = denom_193_epsilon_0_to_fp16, x = var_6461_cast)[name = tensor("denom_193_cast")]; + tensor out_193_cast = mul(x = zero_mean_193_cast, y = denom_193_cast)[name = tensor("out_193_cast")]; + tensor var_6465_to_fp16 = const()[name = tensor("op_6465_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2282212736)))]; + tensor var_6466_cast = add(x = out_193_cast, y = var_6465_to_fp16)[name = tensor("op_6466_cast")]; + tensor var_6468_to_fp16 = const()[name = tensor("op_6468_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2282215360)))]; + tensor hidden_states_257_cast = mul(x = var_6466_cast, y = var_6468_to_fp16)[name = tensor("hidden_states_257_cast")]; + tensor var_6475 = const()[name = tensor("op_6475"), val = tensor([1, 1])]; + tensor var_6477 = const()[name = tensor("op_6477"), val = tensor([1, 1])]; + tensor q_129_pad_type_0 = const()[name = tensor("q_129_pad_type_0"), val = tensor("custom")]; + tensor q_129_pad_0 = const()[name = tensor("q_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2282217984)))]; + tensor q_129_cast = conv(dilations = var_6477, groups = var_4943, pad = q_129_pad_0, pad_type = q_129_pad_type_0, strides = var_6475, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_257_cast)[name = tensor("q_129_cast")]; + tensor var_6481 = const()[name = tensor("op_6481"), val = tensor([1, 1])]; + tensor var_6483 = const()[name = tensor("op_6483"), val = tensor([1, 1])]; + tensor k_129_pad_type_0 = const()[name = tensor("k_129_pad_type_0"), val = tensor("custom")]; + tensor k_129_pad_0 = const()[name = tensor("k_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2285494848)))]; + tensor k_129_cast = conv(dilations = var_6483, groups = var_4943, pad = k_129_pad_0, pad_type = k_129_pad_type_0, strides = var_6481, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_257_cast)[name = tensor("k_129_cast")]; + tensor var_6487 = const()[name = tensor("op_6487"), val = tensor([1, 1])]; + tensor var_6489 = const()[name = tensor("op_6489"), val = tensor([1, 1])]; + tensor v_129_pad_type_0 = const()[name = tensor("v_129_pad_type_0"), val = tensor("custom")]; + tensor v_129_pad_0 = const()[name = tensor("v_129_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2288771712)))]; + tensor v_129_cast = conv(dilations = var_6489, groups = var_4943, pad = v_129_pad_0, pad_type = v_129_pad_type_0, strides = var_6487, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_257_cast)[name = tensor("v_129_cast")]; + tensor var_6493 = const()[name = tensor("op_6493"), val = tensor([2, 20, 64, -1])]; + tensor var_6494_cast = reshape(shape = var_6493, x = q_129_cast)[name = tensor("op_6494_cast")]; + tensor var_6495 = const()[name = tensor("op_6495"), val = tensor([2, 20, 64, -1])]; + tensor var_6496_cast = reshape(shape = var_6495, x = k_129_cast)[name = tensor("op_6496_cast")]; + tensor var_6497 = const()[name = tensor("op_6497"), val = tensor([2, 20, 64, -1])]; + tensor var_6498_cast = reshape(shape = var_6497, x = v_129_cast)[name = tensor("op_6498_cast")]; + tensor attn_weights_257_transpose_x_0 = const()[name = tensor("attn_weights_257_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_257_transpose_y_0 = const()[name = tensor("attn_weights_257_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_257_cast = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6494_cast, y = var_6496_cast)[name = tensor("attn_weights_257_cast")]; + tensor attn_weights_259_cast = mul(x = attn_weights_257_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_259_cast")]; + tensor var_6502_cast = softmax(axis = var_4927, x = attn_weights_259_cast)[name = tensor("op_6502_cast")]; + tensor attn_129_transpose_x_0 = const()[name = tensor("attn_129_transpose_x_0"), val = tensor(false)]; + tensor attn_129_transpose_y_0 = const()[name = tensor("attn_129_transpose_y_0"), val = tensor(true)]; + tensor attn_129_cast = matmul(transpose_x = attn_129_transpose_x_0, transpose_y = attn_129_transpose_y_0, x = var_6498_cast, y = var_6502_cast)[name = tensor("attn_129_cast")]; + tensor var_6506 = const()[name = tensor("op_6506"), val = tensor([2, 1280, 1, -1])]; + tensor input_391_cast = reshape(shape = var_6506, x = attn_129_cast)[name = tensor("input_391_cast")]; + tensor var_6511 = const()[name = tensor("op_6511"), val = tensor([1, 1])]; + tensor var_6513 = const()[name = tensor("op_6513"), val = tensor([1, 1])]; + tensor var_6515_pad_type_0 = const()[name = tensor("op_6515_pad_type_0"), val = tensor("custom")]; + tensor var_6515_pad_0 = const()[name = tensor("op_6515_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2292048576)))]; + tensor mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2295325440)))]; + tensor var_6515_cast = conv(bias = mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_6513, groups = var_4943, pad = var_6515_pad_0, pad_type = var_6515_pad_type_0, strides = var_6511, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_391_cast)[name = tensor("op_6515_cast")]; + tensor inputs_195_cast = add(x = var_6515_cast, y = inputs_193_cast)[name = tensor("inputs_195_cast")]; + tensor var_6519 = const()[name = tensor("op_6519"), val = tensor([1])]; + tensor channels_mean_195_cast = reduce_mean(axes = var_6519, keep_dims = var_4938, x = inputs_195_cast)[name = tensor("channels_mean_195_cast")]; + tensor zero_mean_195_cast = sub(x = inputs_195_cast, y = channels_mean_195_cast)[name = tensor("zero_mean_195_cast")]; + tensor zero_mean_sq_195_cast = mul(x = zero_mean_195_cast, y = zero_mean_195_cast)[name = tensor("zero_mean_sq_195_cast")]; + tensor var_6523 = const()[name = tensor("op_6523"), val = tensor([1])]; + tensor var_6524_cast = reduce_mean(axes = var_6523, keep_dims = var_4938, x = zero_mean_sq_195_cast)[name = tensor("op_6524_cast")]; + tensor var_6525_to_fp16 = const()[name = tensor("op_6525_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6526_cast = add(x = var_6524_cast, y = var_6525_to_fp16)[name = tensor("op_6526_cast")]; + tensor denom_195_epsilon_0_to_fp16 = const()[name = tensor("denom_195_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_195_cast = rsqrt(epsilon = denom_195_epsilon_0_to_fp16, x = var_6526_cast)[name = tensor("denom_195_cast")]; + tensor out_195_cast = mul(x = zero_mean_195_cast, y = denom_195_cast)[name = tensor("out_195_cast")]; + tensor var_6530_to_fp16 = const()[name = tensor("op_6530_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2295328064)))]; + tensor var_6531_cast = add(x = out_195_cast, y = var_6530_to_fp16)[name = tensor("op_6531_cast")]; + tensor var_6533_to_fp16 = const()[name = tensor("op_6533_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2295330688)))]; + tensor hidden_states_259_cast = mul(x = var_6531_cast, y = var_6533_to_fp16)[name = tensor("hidden_states_259_cast")]; + tensor var_6540 = const()[name = tensor("op_6540"), val = tensor([1, 1])]; + tensor var_6542 = const()[name = tensor("op_6542"), val = tensor([1, 1])]; + tensor q_131_pad_type_0 = const()[name = tensor("q_131_pad_type_0"), val = tensor("custom")]; + tensor q_131_pad_0 = const()[name = tensor("q_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2295333312)))]; + tensor q_131_cast = conv(dilations = var_6542, groups = var_4943, pad = q_131_pad_0, pad_type = q_131_pad_type_0, strides = var_6540, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_259_cast)[name = tensor("q_131_cast")]; + tensor var_6546 = const()[name = tensor("op_6546"), val = tensor([1, 1])]; + tensor var_6548 = const()[name = tensor("op_6548"), val = tensor([1, 1])]; + tensor k_131_pad_type_0 = const()[name = tensor("k_131_pad_type_0"), val = tensor("custom")]; + tensor k_131_pad_0 = const()[name = tensor("k_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2298610176)))]; + tensor k_131_cast = conv(dilations = var_6548, groups = var_4943, pad = k_131_pad_0, pad_type = k_131_pad_type_0, strides = var_6546, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_131_cast")]; + tensor var_6552 = const()[name = tensor("op_6552"), val = tensor([1, 1])]; + tensor var_6554 = const()[name = tensor("op_6554"), val = tensor([1, 1])]; + tensor v_131_pad_type_0 = const()[name = tensor("v_131_pad_type_0"), val = tensor("custom")]; + tensor v_131_pad_0 = const()[name = tensor("v_131_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2303853120)))]; + tensor v_131_cast = conv(dilations = var_6554, groups = var_4943, pad = v_131_pad_0, pad_type = v_131_pad_type_0, strides = var_6552, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_131_cast")]; + tensor var_6558 = const()[name = tensor("op_6558"), val = tensor([2, 20, 64, -1])]; + tensor var_6559_cast = reshape(shape = var_6558, x = q_131_cast)[name = tensor("op_6559_cast")]; + tensor var_6560 = const()[name = tensor("op_6560"), val = tensor([2, 20, 64, -1])]; + tensor var_6561_cast = reshape(shape = var_6560, x = k_131_cast)[name = tensor("op_6561_cast")]; + tensor var_6562 = const()[name = tensor("op_6562"), val = tensor([2, 20, 64, -1])]; + tensor var_6563_cast = reshape(shape = var_6562, x = v_131_cast)[name = tensor("op_6563_cast")]; + tensor attn_weights_261_transpose_x_0 = const()[name = tensor("attn_weights_261_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_261_transpose_y_0 = const()[name = tensor("attn_weights_261_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_261_cast = matmul(transpose_x = attn_weights_261_transpose_x_0, transpose_y = attn_weights_261_transpose_y_0, x = var_6559_cast, y = var_6561_cast)[name = tensor("attn_weights_261_cast")]; + tensor attn_weights_263_cast = mul(x = attn_weights_261_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_263_cast")]; + tensor var_6567_cast = softmax(axis = var_4927, x = attn_weights_263_cast)[name = tensor("op_6567_cast")]; + tensor attn_131_transpose_x_0 = const()[name = tensor("attn_131_transpose_x_0"), val = tensor(false)]; + tensor attn_131_transpose_y_0 = const()[name = tensor("attn_131_transpose_y_0"), val = tensor(true)]; + tensor attn_131_cast = matmul(transpose_x = attn_131_transpose_x_0, transpose_y = attn_131_transpose_y_0, x = var_6563_cast, y = var_6567_cast)[name = tensor("attn_131_cast")]; + tensor var_6571 = const()[name = tensor("op_6571"), val = tensor([2, 1280, 1, -1])]; + tensor input_393_cast = reshape(shape = var_6571, x = attn_131_cast)[name = tensor("input_393_cast")]; + tensor var_6576 = const()[name = tensor("op_6576"), val = tensor([1, 1])]; + tensor var_6578 = const()[name = tensor("op_6578"), val = tensor([1, 1])]; + tensor var_6580_pad_type_0 = const()[name = tensor("op_6580_pad_type_0"), val = tensor("custom")]; + tensor var_6580_pad_0 = const()[name = tensor("op_6580_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2309096064)))]; + tensor mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2312372928)))]; + tensor var_6580_cast = conv(bias = mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_6578, groups = var_4943, pad = var_6580_pad_0, pad_type = var_6580_pad_type_0, strides = var_6576, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_393_cast)[name = tensor("op_6580_cast")]; + tensor inputs_197_cast = add(x = var_6580_cast, y = inputs_195_cast)[name = tensor("inputs_197_cast")]; + tensor var_6584 = const()[name = tensor("op_6584"), val = tensor([1])]; + tensor channels_mean_197_cast = reduce_mean(axes = var_6584, keep_dims = var_4938, x = inputs_197_cast)[name = tensor("channels_mean_197_cast")]; + tensor zero_mean_197_cast = sub(x = inputs_197_cast, y = channels_mean_197_cast)[name = tensor("zero_mean_197_cast")]; + tensor zero_mean_sq_197_cast = mul(x = zero_mean_197_cast, y = zero_mean_197_cast)[name = tensor("zero_mean_sq_197_cast")]; + tensor var_6588 = const()[name = tensor("op_6588"), val = tensor([1])]; + tensor var_6589_cast = reduce_mean(axes = var_6588, keep_dims = var_4938, x = zero_mean_sq_197_cast)[name = tensor("op_6589_cast")]; + tensor var_6590_to_fp16 = const()[name = tensor("op_6590_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6591_cast = add(x = var_6589_cast, y = var_6590_to_fp16)[name = tensor("op_6591_cast")]; + tensor denom_197_epsilon_0_to_fp16 = const()[name = tensor("denom_197_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_197_cast = rsqrt(epsilon = denom_197_epsilon_0_to_fp16, x = var_6591_cast)[name = tensor("denom_197_cast")]; + tensor out_197_cast = mul(x = zero_mean_197_cast, y = denom_197_cast)[name = tensor("out_197_cast")]; + tensor var_6595_to_fp16 = const()[name = tensor("op_6595_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2312375552)))]; + tensor var_6596_cast = add(x = out_197_cast, y = var_6595_to_fp16)[name = tensor("op_6596_cast")]; + tensor var_6598_to_fp16 = const()[name = tensor("op_6598_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2312378176)))]; + tensor input_395_cast = mul(x = var_6596_cast, y = var_6598_to_fp16)[name = tensor("input_395_cast")]; + tensor var_6606 = const()[name = tensor("op_6606"), val = tensor([1, 1])]; + tensor var_6608 = const()[name = tensor("op_6608"), val = tensor([1, 1])]; + tensor var_6610_pad_type_0 = const()[name = tensor("op_6610_pad_type_0"), val = tensor("custom")]; + tensor var_6610_pad_0 = const()[name = tensor("op_6610_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2312380800)))]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2338595264)))]; + tensor var_6610_cast = conv(bias = mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_6608, groups = var_4943, pad = var_6610_pad_0, pad_type = var_6610_pad_type_0, strides = var_6606, weight = mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_395_cast)[name = tensor("op_6610_cast")]; + tensor var_6611_split_sizes_0 = const()[name = tensor("op_6611_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6611_axis_0 = const()[name = tensor("op_6611_axis_0"), val = tensor(1)]; + tensor var_6611_cast_0, tensor var_6611_cast_1 = split(axis = var_6611_axis_0, split_sizes = var_6611_split_sizes_0, x = var_6610_cast)[name = tensor("op_6611_cast")]; + tensor var_6613_mode_0 = const()[name = tensor("op_6613_mode_0"), val = tensor("EXACT")]; + tensor var_6613_cast = gelu(mode = var_6613_mode_0, x = var_6611_cast_1)[name = tensor("op_6613_cast")]; + tensor input_397_cast = mul(x = var_6611_cast_0, y = var_6613_cast)[name = tensor("input_397_cast")]; + tensor var_6617 = const()[name = tensor("op_6617"), val = tensor([1, 1])]; + tensor var_6619 = const()[name = tensor("op_6619"), val = tensor([1, 1])]; + tensor var_6621_pad_type_0 = const()[name = tensor("op_6621_pad_type_0"), val = tensor("custom")]; + tensor var_6621_pad_0 = const()[name = tensor("op_6621_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2338615808)))]; + tensor mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2351723072)))]; + tensor var_6621_cast = conv(bias = mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_6619, groups = var_4943, pad = var_6621_pad_0, pad_type = var_6621_pad_type_0, strides = var_6617, weight = mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_397_cast)[name = tensor("op_6621_cast")]; + tensor inputs_199_cast = add(x = var_6621_cast, y = inputs_197_cast)[name = tensor("inputs_199_cast")]; + tensor var_6631 = const()[name = tensor("op_6631"), val = tensor([1])]; + tensor channels_mean_199_cast = reduce_mean(axes = var_6631, keep_dims = var_4938, x = inputs_199_cast)[name = tensor("channels_mean_199_cast")]; + tensor zero_mean_199_cast = sub(x = inputs_199_cast, y = channels_mean_199_cast)[name = tensor("zero_mean_199_cast")]; + tensor zero_mean_sq_199_cast = mul(x = zero_mean_199_cast, y = zero_mean_199_cast)[name = tensor("zero_mean_sq_199_cast")]; + tensor var_6635 = const()[name = tensor("op_6635"), val = tensor([1])]; + tensor var_6636_cast = reduce_mean(axes = var_6635, keep_dims = var_4938, x = zero_mean_sq_199_cast)[name = tensor("op_6636_cast")]; + tensor var_6637_to_fp16 = const()[name = tensor("op_6637_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6638_cast = add(x = var_6636_cast, y = var_6637_to_fp16)[name = tensor("op_6638_cast")]; + tensor denom_199_epsilon_0_to_fp16 = const()[name = tensor("denom_199_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_199_cast = rsqrt(epsilon = denom_199_epsilon_0_to_fp16, x = var_6638_cast)[name = tensor("denom_199_cast")]; + tensor out_199_cast = mul(x = zero_mean_199_cast, y = denom_199_cast)[name = tensor("out_199_cast")]; + tensor var_6642_to_fp16 = const()[name = tensor("op_6642_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2351725696)))]; + tensor var_6643_cast = add(x = out_199_cast, y = var_6642_to_fp16)[name = tensor("op_6643_cast")]; + tensor var_6645_to_fp16 = const()[name = tensor("op_6645_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2351728320)))]; + tensor hidden_states_263_cast = mul(x = var_6643_cast, y = var_6645_to_fp16)[name = tensor("hidden_states_263_cast")]; + tensor var_6652 = const()[name = tensor("op_6652"), val = tensor([1, 1])]; + tensor var_6654 = const()[name = tensor("op_6654"), val = tensor([1, 1])]; + tensor q_133_pad_type_0 = const()[name = tensor("q_133_pad_type_0"), val = tensor("custom")]; + tensor q_133_pad_0 = const()[name = tensor("q_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2351730944)))]; + tensor q_133_cast = conv(dilations = var_6654, groups = var_4943, pad = q_133_pad_0, pad_type = q_133_pad_type_0, strides = var_6652, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_263_cast)[name = tensor("q_133_cast")]; + tensor var_6658 = const()[name = tensor("op_6658"), val = tensor([1, 1])]; + tensor var_6660 = const()[name = tensor("op_6660"), val = tensor([1, 1])]; + tensor k_133_pad_type_0 = const()[name = tensor("k_133_pad_type_0"), val = tensor("custom")]; + tensor k_133_pad_0 = const()[name = tensor("k_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2355007808)))]; + tensor k_133_cast = conv(dilations = var_6660, groups = var_4943, pad = k_133_pad_0, pad_type = k_133_pad_type_0, strides = var_6658, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_263_cast)[name = tensor("k_133_cast")]; + tensor var_6664 = const()[name = tensor("op_6664"), val = tensor([1, 1])]; + tensor var_6666 = const()[name = tensor("op_6666"), val = tensor([1, 1])]; + tensor v_133_pad_type_0 = const()[name = tensor("v_133_pad_type_0"), val = tensor("custom")]; + tensor v_133_pad_0 = const()[name = tensor("v_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2358284672)))]; + tensor v_133_cast = conv(dilations = var_6666, groups = var_4943, pad = v_133_pad_0, pad_type = v_133_pad_type_0, strides = var_6664, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_263_cast)[name = tensor("v_133_cast")]; + tensor var_6670 = const()[name = tensor("op_6670"), val = tensor([2, 20, 64, -1])]; + tensor var_6671_cast = reshape(shape = var_6670, x = q_133_cast)[name = tensor("op_6671_cast")]; + tensor var_6672 = const()[name = tensor("op_6672"), val = tensor([2, 20, 64, -1])]; + tensor var_6673_cast = reshape(shape = var_6672, x = k_133_cast)[name = tensor("op_6673_cast")]; + tensor var_6674 = const()[name = tensor("op_6674"), val = tensor([2, 20, 64, -1])]; + tensor var_6675_cast = reshape(shape = var_6674, x = v_133_cast)[name = tensor("op_6675_cast")]; + tensor attn_weights_265_transpose_x_0 = const()[name = tensor("attn_weights_265_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_265_transpose_y_0 = const()[name = tensor("attn_weights_265_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_265_cast = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6671_cast, y = var_6673_cast)[name = tensor("attn_weights_265_cast")]; + tensor attn_weights_267_cast = mul(x = attn_weights_265_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_267_cast")]; + tensor var_6679_cast = softmax(axis = var_4927, x = attn_weights_267_cast)[name = tensor("op_6679_cast")]; + tensor attn_133_transpose_x_0 = const()[name = tensor("attn_133_transpose_x_0"), val = tensor(false)]; + tensor attn_133_transpose_y_0 = const()[name = tensor("attn_133_transpose_y_0"), val = tensor(true)]; + tensor attn_133_cast = matmul(transpose_x = attn_133_transpose_x_0, transpose_y = attn_133_transpose_y_0, x = var_6675_cast, y = var_6679_cast)[name = tensor("attn_133_cast")]; + tensor var_6683 = const()[name = tensor("op_6683"), val = tensor([2, 1280, 1, -1])]; + tensor input_399_cast = reshape(shape = var_6683, x = attn_133_cast)[name = tensor("input_399_cast")]; + tensor var_6688 = const()[name = tensor("op_6688"), val = tensor([1, 1])]; + tensor var_6690 = const()[name = tensor("op_6690"), val = tensor([1, 1])]; + tensor var_6692_pad_type_0 = const()[name = tensor("op_6692_pad_type_0"), val = tensor("custom")]; + tensor var_6692_pad_0 = const()[name = tensor("op_6692_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2361561536)))]; + tensor mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2364838400)))]; + tensor var_6692_cast = conv(bias = mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_6690, groups = var_4943, pad = var_6692_pad_0, pad_type = var_6692_pad_type_0, strides = var_6688, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_399_cast)[name = tensor("op_6692_cast")]; + tensor inputs_201_cast = add(x = var_6692_cast, y = inputs_199_cast)[name = tensor("inputs_201_cast")]; + tensor var_6696 = const()[name = tensor("op_6696"), val = tensor([1])]; + tensor channels_mean_201_cast = reduce_mean(axes = var_6696, keep_dims = var_4938, x = inputs_201_cast)[name = tensor("channels_mean_201_cast")]; + tensor zero_mean_201_cast = sub(x = inputs_201_cast, y = channels_mean_201_cast)[name = tensor("zero_mean_201_cast")]; + tensor zero_mean_sq_201_cast = mul(x = zero_mean_201_cast, y = zero_mean_201_cast)[name = tensor("zero_mean_sq_201_cast")]; + tensor var_6700 = const()[name = tensor("op_6700"), val = tensor([1])]; + tensor var_6701_cast = reduce_mean(axes = var_6700, keep_dims = var_4938, x = zero_mean_sq_201_cast)[name = tensor("op_6701_cast")]; + tensor var_6702_to_fp16 = const()[name = tensor("op_6702_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6703_cast = add(x = var_6701_cast, y = var_6702_to_fp16)[name = tensor("op_6703_cast")]; + tensor denom_201_epsilon_0_to_fp16 = const()[name = tensor("denom_201_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_201_cast = rsqrt(epsilon = denom_201_epsilon_0_to_fp16, x = var_6703_cast)[name = tensor("denom_201_cast")]; + tensor out_201_cast = mul(x = zero_mean_201_cast, y = denom_201_cast)[name = tensor("out_201_cast")]; + tensor var_6707_to_fp16 = const()[name = tensor("op_6707_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2364841024)))]; + tensor var_6708_cast = add(x = out_201_cast, y = var_6707_to_fp16)[name = tensor("op_6708_cast")]; + tensor var_6710_to_fp16 = const()[name = tensor("op_6710_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2364843648)))]; + tensor hidden_states_265_cast = mul(x = var_6708_cast, y = var_6710_to_fp16)[name = tensor("hidden_states_265_cast")]; + tensor var_6717 = const()[name = tensor("op_6717"), val = tensor([1, 1])]; + tensor var_6719 = const()[name = tensor("op_6719"), val = tensor([1, 1])]; + tensor q_135_pad_type_0 = const()[name = tensor("q_135_pad_type_0"), val = tensor("custom")]; + tensor q_135_pad_0 = const()[name = tensor("q_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2364846272)))]; + tensor q_135_cast = conv(dilations = var_6719, groups = var_4943, pad = q_135_pad_0, pad_type = q_135_pad_type_0, strides = var_6717, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_265_cast)[name = tensor("q_135_cast")]; + tensor var_6723 = const()[name = tensor("op_6723"), val = tensor([1, 1])]; + tensor var_6725 = const()[name = tensor("op_6725"), val = tensor([1, 1])]; + tensor k_135_pad_type_0 = const()[name = tensor("k_135_pad_type_0"), val = tensor("custom")]; + tensor k_135_pad_0 = const()[name = tensor("k_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2368123136)))]; + tensor k_135_cast = conv(dilations = var_6725, groups = var_4943, pad = k_135_pad_0, pad_type = k_135_pad_type_0, strides = var_6723, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_135_cast")]; + tensor var_6729 = const()[name = tensor("op_6729"), val = tensor([1, 1])]; + tensor var_6731 = const()[name = tensor("op_6731"), val = tensor([1, 1])]; + tensor v_135_pad_type_0 = const()[name = tensor("v_135_pad_type_0"), val = tensor("custom")]; + tensor v_135_pad_0 = const()[name = tensor("v_135_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2373366080)))]; + tensor v_135_cast = conv(dilations = var_6731, groups = var_4943, pad = v_135_pad_0, pad_type = v_135_pad_type_0, strides = var_6729, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_135_cast")]; + tensor var_6735 = const()[name = tensor("op_6735"), val = tensor([2, 20, 64, -1])]; + tensor var_6736_cast = reshape(shape = var_6735, x = q_135_cast)[name = tensor("op_6736_cast")]; + tensor var_6737 = const()[name = tensor("op_6737"), val = tensor([2, 20, 64, -1])]; + tensor var_6738_cast = reshape(shape = var_6737, x = k_135_cast)[name = tensor("op_6738_cast")]; + tensor var_6739 = const()[name = tensor("op_6739"), val = tensor([2, 20, 64, -1])]; + tensor var_6740_cast = reshape(shape = var_6739, x = v_135_cast)[name = tensor("op_6740_cast")]; + tensor attn_weights_269_transpose_x_0 = const()[name = tensor("attn_weights_269_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_269_transpose_y_0 = const()[name = tensor("attn_weights_269_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_269_cast = matmul(transpose_x = attn_weights_269_transpose_x_0, transpose_y = attn_weights_269_transpose_y_0, x = var_6736_cast, y = var_6738_cast)[name = tensor("attn_weights_269_cast")]; + tensor attn_weights_271_cast = mul(x = attn_weights_269_cast, y = var_4934_to_fp16)[name = tensor("attn_weights_271_cast")]; + tensor var_6744_cast = softmax(axis = var_4927, x = attn_weights_271_cast)[name = tensor("op_6744_cast")]; + tensor attn_135_transpose_x_0 = const()[name = tensor("attn_135_transpose_x_0"), val = tensor(false)]; + tensor attn_135_transpose_y_0 = const()[name = tensor("attn_135_transpose_y_0"), val = tensor(true)]; + tensor attn_135_cast = matmul(transpose_x = attn_135_transpose_x_0, transpose_y = attn_135_transpose_y_0, x = var_6740_cast, y = var_6744_cast)[name = tensor("attn_135_cast")]; + tensor var_6748 = const()[name = tensor("op_6748"), val = tensor([2, 1280, 1, -1])]; + tensor input_401_cast = reshape(shape = var_6748, x = attn_135_cast)[name = tensor("input_401_cast")]; + tensor var_6753 = const()[name = tensor("op_6753"), val = tensor([1, 1])]; + tensor var_6755 = const()[name = tensor("op_6755"), val = tensor([1, 1])]; + tensor var_6757_pad_type_0 = const()[name = tensor("op_6757_pad_type_0"), val = tensor("custom")]; + tensor var_6757_pad_0 = const()[name = tensor("op_6757_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2378609024)))]; + tensor mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2381885888)))]; + tensor var_6757_cast = conv(bias = mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_6755, groups = var_4943, pad = var_6757_pad_0, pad_type = var_6757_pad_type_0, strides = var_6753, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_401_cast)[name = tensor("op_6757_cast")]; + tensor inputs_203_cast = add(x = var_6757_cast, y = inputs_201_cast)[name = tensor("inputs_203_cast")]; + tensor var_6761 = const()[name = tensor("op_6761"), val = tensor([1])]; + tensor channels_mean_203_cast = reduce_mean(axes = var_6761, keep_dims = var_4938, x = inputs_203_cast)[name = tensor("channels_mean_203_cast")]; + tensor zero_mean_203_cast = sub(x = inputs_203_cast, y = channels_mean_203_cast)[name = tensor("zero_mean_203_cast")]; + tensor zero_mean_sq_203_cast = mul(x = zero_mean_203_cast, y = zero_mean_203_cast)[name = tensor("zero_mean_sq_203_cast")]; + tensor var_6765 = const()[name = tensor("op_6765"), val = tensor([1])]; + tensor var_6766_cast = reduce_mean(axes = var_6765, keep_dims = var_4938, x = zero_mean_sq_203_cast)[name = tensor("op_6766_cast")]; + tensor var_6767_to_fp16 = const()[name = tensor("op_6767_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6768_cast = add(x = var_6766_cast, y = var_6767_to_fp16)[name = tensor("op_6768_cast")]; + tensor denom_203_epsilon_0_to_fp16 = const()[name = tensor("denom_203_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_203_cast = rsqrt(epsilon = denom_203_epsilon_0_to_fp16, x = var_6768_cast)[name = tensor("denom_203_cast")]; + tensor out_203_cast = mul(x = zero_mean_203_cast, y = denom_203_cast)[name = tensor("out_203_cast")]; + tensor var_6772_to_fp16 = const()[name = tensor("op_6772_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2381888512)))]; + tensor var_6773_cast = add(x = out_203_cast, y = var_6772_to_fp16)[name = tensor("op_6773_cast")]; + tensor var_6775_to_fp16 = const()[name = tensor("op_6775_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2381891136)))]; + tensor input_403_cast = mul(x = var_6773_cast, y = var_6775_to_fp16)[name = tensor("input_403_cast")]; + tensor var_6783 = const()[name = tensor("op_6783"), val = tensor([1, 1])]; + tensor var_6785 = const()[name = tensor("op_6785"), val = tensor([1, 1])]; + tensor var_6787_pad_type_0 = const()[name = tensor("op_6787_pad_type_0"), val = tensor("custom")]; + tensor var_6787_pad_0 = const()[name = tensor("op_6787_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2381893760)))]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2408108224)))]; + tensor var_6787_cast = conv(bias = mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_6785, groups = var_4943, pad = var_6787_pad_0, pad_type = var_6787_pad_type_0, strides = var_6783, weight = mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_403_cast)[name = tensor("op_6787_cast")]; + tensor var_6788_split_sizes_0 = const()[name = tensor("op_6788_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6788_axis_0 = const()[name = tensor("op_6788_axis_0"), val = tensor(1)]; + tensor var_6788_cast_0, tensor var_6788_cast_1 = split(axis = var_6788_axis_0, split_sizes = var_6788_split_sizes_0, x = var_6787_cast)[name = tensor("op_6788_cast")]; + tensor var_6790_mode_0 = const()[name = tensor("op_6790_mode_0"), val = tensor("EXACT")]; + tensor var_6790_cast = gelu(mode = var_6790_mode_0, x = var_6788_cast_1)[name = tensor("op_6790_cast")]; + tensor input_405_cast = mul(x = var_6788_cast_0, y = var_6790_cast)[name = tensor("input_405_cast")]; + tensor var_6794 = const()[name = tensor("op_6794"), val = tensor([1, 1])]; + tensor var_6796 = const()[name = tensor("op_6796"), val = tensor([1, 1])]; + tensor var_6798_pad_type_0 = const()[name = tensor("op_6798_pad_type_0"), val = tensor("custom")]; + tensor var_6798_pad_0 = const()[name = tensor("op_6798_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2408128768)))]; + tensor mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2421236032)))]; + tensor var_6798_cast = conv(bias = mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_6796, groups = var_4943, pad = var_6798_pad_0, pad_type = var_6798_pad_type_0, strides = var_6794, weight = mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_405_cast)[name = tensor("op_6798_cast")]; + tensor hidden_states_269_cast = add(x = var_6798_cast, y = inputs_203_cast)[name = tensor("hidden_states_269_cast")]; + tensor var_6800 = const()[name = tensor("op_6800"), val = tensor([2, 1280, 32, 32])]; + tensor input_407_cast = reshape(shape = var_6800, x = hidden_states_269_cast)[name = tensor("input_407_cast")]; + tensor var_6804 = const()[name = tensor("op_6804"), val = tensor([1, 1])]; + tensor var_6806 = const()[name = tensor("op_6806"), val = tensor([1, 1])]; + tensor hidden_states_271_pad_type_0 = const()[name = tensor("hidden_states_271_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_271_pad_0 = const()[name = tensor("hidden_states_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2421238656)))]; + tensor mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2424515520)))]; + tensor hidden_states_271_cast = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_6806, groups = var_4943, pad = hidden_states_271_pad_0, pad_type = hidden_states_271_pad_type_0, strides = var_6804, weight = mid_block_attentions_0_proj_out_weight_to_fp16, x = input_407_cast)[name = tensor("hidden_states_271_cast")]; + tensor input_409_cast = add(x = hidden_states_271_cast, y = hidden_states_205_cast)[name = tensor("input_409_cast")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_76_cast = reshape(shape = reshape_76_shape_0, x = input_409_cast)[name = tensor("reshape_76_cast")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57_cast = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast)[name = tensor("reduce_mean_57_cast")]; + tensor sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast)[name = tensor("sub_38_cast")]; + tensor square_19_cast = square(x = sub_38_cast)[name = tensor("square_19_cast")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59_cast = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast)[name = tensor("reduce_mean_59_cast")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_38_cast = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast")]; + tensor sqrt_19_cast = sqrt(x = add_38_cast)[name = tensor("sqrt_19_cast")]; + tensor real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast)[name = tensor("real_div_19_cast")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast)[name = tensor("reshape_77_cast")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2424518144)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2424520768)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_77_cast)[name = tensor("add_39_cast")]; + tensor input_413_cast = silu(x = add_39_cast)[name = tensor("input_413_cast")]; + tensor var_6821 = const()[name = tensor("op_6821"), val = tensor([1, 1])]; + tensor var_6823 = const()[name = tensor("op_6823"), val = tensor([1, 1])]; + tensor hidden_states_273_pad_type_0 = const()[name = tensor("hidden_states_273_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_273_pad_0 = const()[name = tensor("hidden_states_273_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2424523392)))]; + tensor mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2454014656)))]; + tensor hidden_states_273_cast = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_6823, groups = var_4943, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = var_6821, weight = mid_block_resnets_1_conv1_weight_to_fp16, x = input_413_cast)[name = tensor("hidden_states_273_cast")]; + tensor var_6829 = const()[name = tensor("op_6829"), val = tensor([1, 1])]; + tensor var_6831 = const()[name = tensor("op_6831"), val = tensor([1, 1])]; + tensor temb_15_pad_type_0 = const()[name = tensor("temb_15_pad_type_0"), val = tensor("custom")]; + tensor temb_15_pad_0 = const()[name = tensor("temb_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2454017280)))]; + tensor mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2457294144)))]; + tensor temb_15_cast = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_6831, groups = var_4943, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_6829, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_15_cast")]; + tensor input_417_cast = add(x = hidden_states_273_cast, y = temb_15_cast)[name = tensor("input_417_cast")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_417_cast)[name = tensor("reshape_80_cast")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60_cast = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast)[name = tensor("reduce_mean_60_cast")]; + tensor sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast)[name = tensor("sub_40_cast")]; + tensor square_20_cast = square(x = sub_40_cast)[name = tensor("square_20_cast")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62_cast = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast)[name = tensor("reduce_mean_62_cast")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_40_cast = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast")]; + tensor sqrt_20_cast = sqrt(x = add_40_cast)[name = tensor("sqrt_20_cast")]; + tensor real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast)[name = tensor("real_div_20_cast")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast)[name = tensor("reshape_81_cast")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2457296768)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2457299392)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_81_cast)[name = tensor("add_41_cast")]; + tensor input_421_cast = silu(x = add_41_cast)[name = tensor("input_421_cast")]; + tensor var_6841 = const()[name = tensor("op_6841"), val = tensor([1, 1])]; + tensor var_6843 = const()[name = tensor("op_6843"), val = tensor([1, 1])]; + tensor hidden_states_275_pad_type_0 = const()[name = tensor("hidden_states_275_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_275_pad_0 = const()[name = tensor("hidden_states_275_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2457302016)))]; + tensor mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486793280)))]; + tensor hidden_states_275_cast = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_6843, groups = var_4943, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_6841, weight = mid_block_resnets_1_conv2_weight_to_fp16, x = input_421_cast)[name = tensor("hidden_states_275_cast")]; + tensor hidden_states_277_cast = add(x = input_409_cast, y = hidden_states_275_cast)[name = tensor("hidden_states_277_cast")]; + tensor var_6849 = const()[name = tensor("op_6849"), val = tensor(3)]; + tensor var_6860 = const()[name = tensor("op_6860"), val = tensor(true)]; + tensor var_6865 = const()[name = tensor("op_6865"), val = tensor(1)]; + tensor input_423_interleave_0 = const()[name = tensor("input_423_interleave_0"), val = tensor(false)]; + tensor input_423_cast = concat(axis = var_6865, interleave = input_423_interleave_0, values = (hidden_states_277_cast, input_311_cast))[name = tensor("input_423_cast")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([2, 32, 80, 32, 32])]; + tensor reshape_84_cast = reshape(shape = reshape_84_shape_0, x = input_423_cast)[name = tensor("reshape_84_cast")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63_cast = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast)[name = tensor("reduce_mean_63_cast")]; + tensor sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast)[name = tensor("sub_42_cast")]; + tensor square_21_cast = square(x = sub_42_cast)[name = tensor("square_21_cast")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65_cast = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast)[name = tensor("reduce_mean_65_cast")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_42_cast = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast")]; + tensor sqrt_21_cast = sqrt(x = add_42_cast)[name = tensor("sqrt_21_cast")]; + tensor real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast)[name = tensor("real_div_21_cast")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([2, 2560, 32, 32])]; + tensor reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast)[name = tensor("reshape_85_cast")]; + tensor add_43_mean_0_to_fp16 = const()[name = tensor("add_43_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486795904)))]; + tensor add_43_variance_0_to_fp16 = const()[name = tensor("add_43_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486801088)))]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486806272)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486811456)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_43_mean_0_to_fp16, variance = add_43_variance_0_to_fp16, x = reshape_85_cast)[name = tensor("add_43_cast")]; + tensor input_427_cast = silu(x = add_43_cast)[name = tensor("input_427_cast")]; + tensor var_6894 = const()[name = tensor("op_6894"), val = tensor([1, 1])]; + tensor var_6896 = const()[name = tensor("op_6896"), val = tensor([1, 1])]; + tensor hidden_states_279_pad_type_0 = const()[name = tensor("hidden_states_279_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_279_pad_0 = const()[name = tensor("hidden_states_279_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486816640)))]; + tensor up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2545799104)))]; + tensor hidden_states_279_cast = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_6896, groups = var_6865, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_6894, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_427_cast)[name = tensor("hidden_states_279_cast")]; + tensor var_6902 = const()[name = tensor("op_6902"), val = tensor([1, 1])]; + tensor var_6904 = const()[name = tensor("op_6904"), val = tensor([1, 1])]; + tensor temb_17_pad_type_0 = const()[name = tensor("temb_17_pad_type_0"), val = tensor("custom")]; + tensor temb_17_pad_0 = const()[name = tensor("temb_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2545801728)))]; + tensor up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2549078592)))]; + tensor temb_17_cast = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_6904, groups = var_6865, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_6902, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_17_cast")]; + tensor input_431_cast = add(x = hidden_states_279_cast, y = temb_17_cast)[name = tensor("input_431_cast")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_88_cast = reshape(shape = reshape_88_shape_0, x = input_431_cast)[name = tensor("reshape_88_cast")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66_cast = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast)[name = tensor("reduce_mean_66_cast")]; + tensor sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast)[name = tensor("sub_44_cast")]; + tensor square_22_cast = square(x = sub_44_cast)[name = tensor("square_22_cast")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68_cast = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast)[name = tensor("reduce_mean_68_cast")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_44_cast = add(x = reduce_mean_68_cast, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast")]; + tensor sqrt_22_cast = sqrt(x = add_44_cast)[name = tensor("sqrt_22_cast")]; + tensor real_div_22_cast = real_div(x = sub_44_cast, y = sqrt_22_cast)[name = tensor("real_div_22_cast")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_89_cast = reshape(shape = reshape_89_shape_0, x = real_div_22_cast)[name = tensor("reshape_89_cast")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2549081216)))]; + tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2549083840)))]; + tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_45_cast = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_89_cast)[name = tensor("add_45_cast")]; + tensor input_435_cast = silu(x = add_45_cast)[name = tensor("input_435_cast")]; + tensor var_6914 = const()[name = tensor("op_6914"), val = tensor([1, 1])]; + tensor var_6916 = const()[name = tensor("op_6916"), val = tensor([1, 1])]; + tensor hidden_states_281_pad_type_0 = const()[name = tensor("hidden_states_281_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_281_pad_0 = const()[name = tensor("hidden_states_281_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2549086464)))]; + tensor up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2578577728)))]; + tensor hidden_states_281_cast = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_6916, groups = var_6865, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_6914, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_435_cast)[name = tensor("hidden_states_281_cast")]; + tensor var_6921 = const()[name = tensor("op_6921"), val = tensor([1, 1])]; + tensor var_6923 = const()[name = tensor("op_6923"), val = tensor([1, 1])]; + tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("custom")]; + tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2578580352)))]; + tensor up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2585134016)))]; + tensor x_5_cast = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_6923, groups = var_6865, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_6921, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16, x = input_423_cast)[name = tensor("x_5_cast")]; + tensor hidden_states_283_cast = add(x = x_5_cast, y = hidden_states_281_cast)[name = tensor("hidden_states_283_cast")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_92_cast = reshape(shape = reshape_92_shape_0, x = hidden_states_283_cast)[name = tensor("reshape_92_cast")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69_cast = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast)[name = tensor("reduce_mean_69_cast")]; + tensor sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast)[name = tensor("sub_46_cast")]; + tensor square_23_cast = square(x = sub_46_cast)[name = tensor("square_23_cast")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71_cast = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast)[name = tensor("reduce_mean_71_cast")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_46_cast = add(x = reduce_mean_71_cast, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast")]; + tensor sqrt_23_cast = sqrt(x = add_46_cast)[name = tensor("sqrt_23_cast")]; + tensor real_div_23_cast = real_div(x = sub_46_cast, y = sqrt_23_cast)[name = tensor("real_div_23_cast")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_93_cast = reshape(shape = reshape_93_shape_0, x = real_div_23_cast)[name = tensor("reshape_93_cast")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2585136640)))]; + tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2585139264)))]; + tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_47_cast = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_93_cast)[name = tensor("add_47_cast")]; + tensor var_6961 = const()[name = tensor("op_6961"), val = tensor([1, 1])]; + tensor var_6963 = const()[name = tensor("op_6963"), val = tensor([1, 1])]; + tensor hidden_states_285_pad_type_0 = const()[name = tensor("hidden_states_285_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_285_pad_0 = const()[name = tensor("hidden_states_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2585141888)))]; + tensor up_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2588418752)))]; + tensor hidden_states_285_cast = conv(bias = up_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_6963, groups = var_6865, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_6961, weight = up_blocks_0_attentions_0_proj_in_weight_to_fp16, x = add_47_cast)[name = tensor("hidden_states_285_cast")]; + tensor var_6968 = const()[name = tensor("op_6968"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_205_cast = reshape(shape = var_6968, x = hidden_states_285_cast)[name = tensor("inputs_205_cast")]; + tensor var_6978 = const()[name = tensor("op_6978"), val = tensor([1])]; + tensor channels_mean_205_cast = reduce_mean(axes = var_6978, keep_dims = var_6860, x = inputs_205_cast)[name = tensor("channels_mean_205_cast")]; + tensor zero_mean_205_cast = sub(x = inputs_205_cast, y = channels_mean_205_cast)[name = tensor("zero_mean_205_cast")]; + tensor zero_mean_sq_205_cast = mul(x = zero_mean_205_cast, y = zero_mean_205_cast)[name = tensor("zero_mean_sq_205_cast")]; + tensor var_6982 = const()[name = tensor("op_6982"), val = tensor([1])]; + tensor var_6983_cast = reduce_mean(axes = var_6982, keep_dims = var_6860, x = zero_mean_sq_205_cast)[name = tensor("op_6983_cast")]; + tensor var_6984_to_fp16 = const()[name = tensor("op_6984_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6985_cast = add(x = var_6983_cast, y = var_6984_to_fp16)[name = tensor("op_6985_cast")]; + tensor denom_205_epsilon_0_to_fp16 = const()[name = tensor("denom_205_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_205_cast = rsqrt(epsilon = denom_205_epsilon_0_to_fp16, x = var_6985_cast)[name = tensor("denom_205_cast")]; + tensor out_205_cast = mul(x = zero_mean_205_cast, y = denom_205_cast)[name = tensor("out_205_cast")]; + tensor var_6989_to_fp16 = const()[name = tensor("op_6989_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2588421376)))]; + tensor var_6990_cast = add(x = out_205_cast, y = var_6989_to_fp16)[name = tensor("op_6990_cast")]; + tensor var_6992_to_fp16 = const()[name = tensor("op_6992_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2588424000)))]; + tensor hidden_states_287_cast = mul(x = var_6990_cast, y = var_6992_to_fp16)[name = tensor("hidden_states_287_cast")]; + tensor var_6999 = const()[name = tensor("op_6999"), val = tensor([1, 1])]; + tensor var_7001 = const()[name = tensor("op_7001"), val = tensor([1, 1])]; + tensor q_137_pad_type_0 = const()[name = tensor("q_137_pad_type_0"), val = tensor("custom")]; + tensor q_137_pad_0 = const()[name = tensor("q_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2588426624)))]; + tensor q_137_cast = conv(dilations = var_7001, groups = var_6865, pad = q_137_pad_0, pad_type = q_137_pad_type_0, strides = var_6999, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_287_cast)[name = tensor("q_137_cast")]; + tensor var_7005 = const()[name = tensor("op_7005"), val = tensor([1, 1])]; + tensor var_7007 = const()[name = tensor("op_7007"), val = tensor([1, 1])]; + tensor k_137_pad_type_0 = const()[name = tensor("k_137_pad_type_0"), val = tensor("custom")]; + tensor k_137_pad_0 = const()[name = tensor("k_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2591703488)))]; + tensor k_137_cast = conv(dilations = var_7007, groups = var_6865, pad = k_137_pad_0, pad_type = k_137_pad_type_0, strides = var_7005, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_287_cast)[name = tensor("k_137_cast")]; + tensor var_7011 = const()[name = tensor("op_7011"), val = tensor([1, 1])]; + tensor var_7013 = const()[name = tensor("op_7013"), val = tensor([1, 1])]; + tensor v_137_pad_type_0 = const()[name = tensor("v_137_pad_type_0"), val = tensor("custom")]; + tensor v_137_pad_0 = const()[name = tensor("v_137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2594980352)))]; + tensor v_137_cast = conv(dilations = var_7013, groups = var_6865, pad = v_137_pad_0, pad_type = v_137_pad_type_0, strides = var_7011, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_287_cast)[name = tensor("v_137_cast")]; + tensor var_7017 = const()[name = tensor("op_7017"), val = tensor([2, 20, 64, -1])]; + tensor var_7018_cast = reshape(shape = var_7017, x = q_137_cast)[name = tensor("op_7018_cast")]; + tensor var_7019 = const()[name = tensor("op_7019"), val = tensor([2, 20, 64, -1])]; + tensor var_7020_cast = reshape(shape = var_7019, x = k_137_cast)[name = tensor("op_7020_cast")]; + tensor var_7021 = const()[name = tensor("op_7021"), val = tensor([2, 20, 64, -1])]; + tensor var_7022_cast = reshape(shape = var_7021, x = v_137_cast)[name = tensor("op_7022_cast")]; + tensor attn_weights_273_transpose_x_0 = const()[name = tensor("attn_weights_273_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_273_transpose_y_0 = const()[name = tensor("attn_weights_273_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_273_cast = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_7018_cast, y = var_7020_cast)[name = tensor("attn_weights_273_cast")]; + tensor var_6856_to_fp16 = const()[name = tensor("op_6856_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_275_cast = mul(x = attn_weights_273_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_275_cast")]; + tensor var_7026_cast = softmax(axis = var_6849, x = attn_weights_275_cast)[name = tensor("op_7026_cast")]; + tensor attn_137_transpose_x_0 = const()[name = tensor("attn_137_transpose_x_0"), val = tensor(false)]; + tensor attn_137_transpose_y_0 = const()[name = tensor("attn_137_transpose_y_0"), val = tensor(true)]; + tensor attn_137_cast = matmul(transpose_x = attn_137_transpose_x_0, transpose_y = attn_137_transpose_y_0, x = var_7022_cast, y = var_7026_cast)[name = tensor("attn_137_cast")]; + tensor var_7030 = const()[name = tensor("op_7030"), val = tensor([2, 1280, 1, -1])]; + tensor input_439_cast = reshape(shape = var_7030, x = attn_137_cast)[name = tensor("input_439_cast")]; + tensor var_7035 = const()[name = tensor("op_7035"), val = tensor([1, 1])]; + tensor var_7037 = const()[name = tensor("op_7037"), val = tensor([1, 1])]; + tensor var_7039_pad_type_0 = const()[name = tensor("op_7039_pad_type_0"), val = tensor("custom")]; + tensor var_7039_pad_0 = const()[name = tensor("op_7039_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2598257216)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2601534080)))]; + tensor var_7039_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_7037, groups = var_6865, pad = var_7039_pad_0, pad_type = var_7039_pad_type_0, strides = var_7035, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_439_cast)[name = tensor("op_7039_cast")]; + tensor inputs_207_cast = add(x = var_7039_cast, y = inputs_205_cast)[name = tensor("inputs_207_cast")]; + tensor var_7043 = const()[name = tensor("op_7043"), val = tensor([1])]; + tensor channels_mean_207_cast = reduce_mean(axes = var_7043, keep_dims = var_6860, x = inputs_207_cast)[name = tensor("channels_mean_207_cast")]; + tensor zero_mean_207_cast = sub(x = inputs_207_cast, y = channels_mean_207_cast)[name = tensor("zero_mean_207_cast")]; + tensor zero_mean_sq_207_cast = mul(x = zero_mean_207_cast, y = zero_mean_207_cast)[name = tensor("zero_mean_sq_207_cast")]; + tensor var_7047 = const()[name = tensor("op_7047"), val = tensor([1])]; + tensor var_7048_cast = reduce_mean(axes = var_7047, keep_dims = var_6860, x = zero_mean_sq_207_cast)[name = tensor("op_7048_cast")]; + tensor var_7049_to_fp16 = const()[name = tensor("op_7049_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7050_cast = add(x = var_7048_cast, y = var_7049_to_fp16)[name = tensor("op_7050_cast")]; + tensor denom_207_epsilon_0_to_fp16 = const()[name = tensor("denom_207_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_207_cast = rsqrt(epsilon = denom_207_epsilon_0_to_fp16, x = var_7050_cast)[name = tensor("denom_207_cast")]; + tensor out_207_cast = mul(x = zero_mean_207_cast, y = denom_207_cast)[name = tensor("out_207_cast")]; + tensor var_7054_to_fp16 = const()[name = tensor("op_7054_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2601536704)))]; + tensor var_7055_cast = add(x = out_207_cast, y = var_7054_to_fp16)[name = tensor("op_7055_cast")]; + tensor var_7057_to_fp16 = const()[name = tensor("op_7057_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2601539328)))]; + tensor hidden_states_289_cast = mul(x = var_7055_cast, y = var_7057_to_fp16)[name = tensor("hidden_states_289_cast")]; + tensor var_7064 = const()[name = tensor("op_7064"), val = tensor([1, 1])]; + tensor var_7066 = const()[name = tensor("op_7066"), val = tensor([1, 1])]; + tensor q_139_pad_type_0 = const()[name = tensor("q_139_pad_type_0"), val = tensor("custom")]; + tensor q_139_pad_0 = const()[name = tensor("q_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2601541952)))]; + tensor q_139_cast = conv(dilations = var_7066, groups = var_6865, pad = q_139_pad_0, pad_type = q_139_pad_type_0, strides = var_7064, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_289_cast)[name = tensor("q_139_cast")]; + tensor var_7070 = const()[name = tensor("op_7070"), val = tensor([1, 1])]; + tensor var_7072 = const()[name = tensor("op_7072"), val = tensor([1, 1])]; + tensor k_139_pad_type_0 = const()[name = tensor("k_139_pad_type_0"), val = tensor("custom")]; + tensor k_139_pad_0 = const()[name = tensor("k_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2604818816)))]; + tensor k_139_cast = conv(dilations = var_7072, groups = var_6865, pad = k_139_pad_0, pad_type = k_139_pad_type_0, strides = var_7070, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_139_cast")]; + tensor var_7076 = const()[name = tensor("op_7076"), val = tensor([1, 1])]; + tensor var_7078 = const()[name = tensor("op_7078"), val = tensor([1, 1])]; + tensor v_139_pad_type_0 = const()[name = tensor("v_139_pad_type_0"), val = tensor("custom")]; + tensor v_139_pad_0 = const()[name = tensor("v_139_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2610061760)))]; + tensor v_139_cast = conv(dilations = var_7078, groups = var_6865, pad = v_139_pad_0, pad_type = v_139_pad_type_0, strides = var_7076, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_139_cast")]; + tensor var_7082 = const()[name = tensor("op_7082"), val = tensor([2, 20, 64, -1])]; + tensor var_7083_cast = reshape(shape = var_7082, x = q_139_cast)[name = tensor("op_7083_cast")]; + tensor var_7084 = const()[name = tensor("op_7084"), val = tensor([2, 20, 64, -1])]; + tensor var_7085_cast = reshape(shape = var_7084, x = k_139_cast)[name = tensor("op_7085_cast")]; + tensor var_7086 = const()[name = tensor("op_7086"), val = tensor([2, 20, 64, -1])]; + tensor var_7087_cast = reshape(shape = var_7086, x = v_139_cast)[name = tensor("op_7087_cast")]; + tensor attn_weights_277_transpose_x_0 = const()[name = tensor("attn_weights_277_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_277_transpose_y_0 = const()[name = tensor("attn_weights_277_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_277_cast = matmul(transpose_x = attn_weights_277_transpose_x_0, transpose_y = attn_weights_277_transpose_y_0, x = var_7083_cast, y = var_7085_cast)[name = tensor("attn_weights_277_cast")]; + tensor attn_weights_279_cast = mul(x = attn_weights_277_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_279_cast")]; + tensor var_7091_cast = softmax(axis = var_6849, x = attn_weights_279_cast)[name = tensor("op_7091_cast")]; + tensor attn_139_transpose_x_0 = const()[name = tensor("attn_139_transpose_x_0"), val = tensor(false)]; + tensor attn_139_transpose_y_0 = const()[name = tensor("attn_139_transpose_y_0"), val = tensor(true)]; + tensor attn_139_cast = matmul(transpose_x = attn_139_transpose_x_0, transpose_y = attn_139_transpose_y_0, x = var_7087_cast, y = var_7091_cast)[name = tensor("attn_139_cast")]; + tensor var_7095 = const()[name = tensor("op_7095"), val = tensor([2, 1280, 1, -1])]; + tensor input_441_cast = reshape(shape = var_7095, x = attn_139_cast)[name = tensor("input_441_cast")]; + tensor var_7100 = const()[name = tensor("op_7100"), val = tensor([1, 1])]; + tensor var_7102 = const()[name = tensor("op_7102"), val = tensor([1, 1])]; + tensor var_7104_pad_type_0 = const()[name = tensor("op_7104_pad_type_0"), val = tensor("custom")]; + tensor var_7104_pad_0 = const()[name = tensor("op_7104_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2615304704)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2618581568)))]; + tensor var_7104_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_7102, groups = var_6865, pad = var_7104_pad_0, pad_type = var_7104_pad_type_0, strides = var_7100, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_441_cast)[name = tensor("op_7104_cast")]; + tensor inputs_209_cast = add(x = var_7104_cast, y = inputs_207_cast)[name = tensor("inputs_209_cast")]; + tensor var_7108 = const()[name = tensor("op_7108"), val = tensor([1])]; + tensor channels_mean_209_cast = reduce_mean(axes = var_7108, keep_dims = var_6860, x = inputs_209_cast)[name = tensor("channels_mean_209_cast")]; + tensor zero_mean_209_cast = sub(x = inputs_209_cast, y = channels_mean_209_cast)[name = tensor("zero_mean_209_cast")]; + tensor zero_mean_sq_209_cast = mul(x = zero_mean_209_cast, y = zero_mean_209_cast)[name = tensor("zero_mean_sq_209_cast")]; + tensor var_7112 = const()[name = tensor("op_7112"), val = tensor([1])]; + tensor var_7113_cast = reduce_mean(axes = var_7112, keep_dims = var_6860, x = zero_mean_sq_209_cast)[name = tensor("op_7113_cast")]; + tensor var_7114_to_fp16 = const()[name = tensor("op_7114_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7115_cast = add(x = var_7113_cast, y = var_7114_to_fp16)[name = tensor("op_7115_cast")]; + tensor denom_209_epsilon_0_to_fp16 = const()[name = tensor("denom_209_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_209_cast = rsqrt(epsilon = denom_209_epsilon_0_to_fp16, x = var_7115_cast)[name = tensor("denom_209_cast")]; + tensor out_209_cast = mul(x = zero_mean_209_cast, y = denom_209_cast)[name = tensor("out_209_cast")]; + tensor var_7119_to_fp16 = const()[name = tensor("op_7119_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2618584192)))]; + tensor var_7120_cast = add(x = out_209_cast, y = var_7119_to_fp16)[name = tensor("op_7120_cast")]; + tensor var_7122_to_fp16 = const()[name = tensor("op_7122_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2618586816)))]; + tensor input_443_cast = mul(x = var_7120_cast, y = var_7122_to_fp16)[name = tensor("input_443_cast")]; + tensor var_7130 = const()[name = tensor("op_7130"), val = tensor([1, 1])]; + tensor var_7132 = const()[name = tensor("op_7132"), val = tensor([1, 1])]; + tensor var_7134_pad_type_0 = const()[name = tensor("op_7134_pad_type_0"), val = tensor("custom")]; + tensor var_7134_pad_0 = const()[name = tensor("op_7134_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2618589440)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2644803904)))]; + tensor var_7134_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_7132, groups = var_6865, pad = var_7134_pad_0, pad_type = var_7134_pad_type_0, strides = var_7130, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_443_cast)[name = tensor("op_7134_cast")]; + tensor var_7135_split_sizes_0 = const()[name = tensor("op_7135_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7135_axis_0 = const()[name = tensor("op_7135_axis_0"), val = tensor(1)]; + tensor var_7135_cast_0, tensor var_7135_cast_1 = split(axis = var_7135_axis_0, split_sizes = var_7135_split_sizes_0, x = var_7134_cast)[name = tensor("op_7135_cast")]; + tensor var_7137_mode_0 = const()[name = tensor("op_7137_mode_0"), val = tensor("EXACT")]; + tensor var_7137_cast = gelu(mode = var_7137_mode_0, x = var_7135_cast_1)[name = tensor("op_7137_cast")]; + tensor input_445_cast = mul(x = var_7135_cast_0, y = var_7137_cast)[name = tensor("input_445_cast")]; + tensor var_7141 = const()[name = tensor("op_7141"), val = tensor([1, 1])]; + tensor var_7143 = const()[name = tensor("op_7143"), val = tensor([1, 1])]; + tensor var_7145_pad_type_0 = const()[name = tensor("op_7145_pad_type_0"), val = tensor("custom")]; + tensor var_7145_pad_0 = const()[name = tensor("op_7145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2644824448)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2657931712)))]; + tensor var_7145_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_7143, groups = var_6865, pad = var_7145_pad_0, pad_type = var_7145_pad_type_0, strides = var_7141, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_445_cast)[name = tensor("op_7145_cast")]; + tensor inputs_211_cast = add(x = var_7145_cast, y = inputs_209_cast)[name = tensor("inputs_211_cast")]; + tensor var_7155 = const()[name = tensor("op_7155"), val = tensor([1])]; + tensor channels_mean_211_cast = reduce_mean(axes = var_7155, keep_dims = var_6860, x = inputs_211_cast)[name = tensor("channels_mean_211_cast")]; + tensor zero_mean_211_cast = sub(x = inputs_211_cast, y = channels_mean_211_cast)[name = tensor("zero_mean_211_cast")]; + tensor zero_mean_sq_211_cast = mul(x = zero_mean_211_cast, y = zero_mean_211_cast)[name = tensor("zero_mean_sq_211_cast")]; + tensor var_7159 = const()[name = tensor("op_7159"), val = tensor([1])]; + tensor var_7160_cast = reduce_mean(axes = var_7159, keep_dims = var_6860, x = zero_mean_sq_211_cast)[name = tensor("op_7160_cast")]; + tensor var_7161_to_fp16 = const()[name = tensor("op_7161_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7162_cast = add(x = var_7160_cast, y = var_7161_to_fp16)[name = tensor("op_7162_cast")]; + tensor denom_211_epsilon_0_to_fp16 = const()[name = tensor("denom_211_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_211_cast = rsqrt(epsilon = denom_211_epsilon_0_to_fp16, x = var_7162_cast)[name = tensor("denom_211_cast")]; + tensor out_211_cast = mul(x = zero_mean_211_cast, y = denom_211_cast)[name = tensor("out_211_cast")]; + tensor var_7166_to_fp16 = const()[name = tensor("op_7166_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2657934336)))]; + tensor var_7167_cast = add(x = out_211_cast, y = var_7166_to_fp16)[name = tensor("op_7167_cast")]; + tensor var_7169_to_fp16 = const()[name = tensor("op_7169_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2657936960)))]; + tensor hidden_states_293_cast = mul(x = var_7167_cast, y = var_7169_to_fp16)[name = tensor("hidden_states_293_cast")]; + tensor var_7176 = const()[name = tensor("op_7176"), val = tensor([1, 1])]; + tensor var_7178 = const()[name = tensor("op_7178"), val = tensor([1, 1])]; + tensor q_141_pad_type_0 = const()[name = tensor("q_141_pad_type_0"), val = tensor("custom")]; + tensor q_141_pad_0 = const()[name = tensor("q_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2657939584)))]; + tensor q_141_cast = conv(dilations = var_7178, groups = var_6865, pad = q_141_pad_0, pad_type = q_141_pad_type_0, strides = var_7176, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_293_cast)[name = tensor("q_141_cast")]; + tensor var_7182 = const()[name = tensor("op_7182"), val = tensor([1, 1])]; + tensor var_7184 = const()[name = tensor("op_7184"), val = tensor([1, 1])]; + tensor k_141_pad_type_0 = const()[name = tensor("k_141_pad_type_0"), val = tensor("custom")]; + tensor k_141_pad_0 = const()[name = tensor("k_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2661216448)))]; + tensor k_141_cast = conv(dilations = var_7184, groups = var_6865, pad = k_141_pad_0, pad_type = k_141_pad_type_0, strides = var_7182, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_293_cast)[name = tensor("k_141_cast")]; + tensor var_7188 = const()[name = tensor("op_7188"), val = tensor([1, 1])]; + tensor var_7190 = const()[name = tensor("op_7190"), val = tensor([1, 1])]; + tensor v_141_pad_type_0 = const()[name = tensor("v_141_pad_type_0"), val = tensor("custom")]; + tensor v_141_pad_0 = const()[name = tensor("v_141_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2664493312)))]; + tensor v_141_cast = conv(dilations = var_7190, groups = var_6865, pad = v_141_pad_0, pad_type = v_141_pad_type_0, strides = var_7188, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_293_cast)[name = tensor("v_141_cast")]; + tensor var_7194 = const()[name = tensor("op_7194"), val = tensor([2, 20, 64, -1])]; + tensor var_7195_cast = reshape(shape = var_7194, x = q_141_cast)[name = tensor("op_7195_cast")]; + tensor var_7196 = const()[name = tensor("op_7196"), val = tensor([2, 20, 64, -1])]; + tensor var_7197_cast = reshape(shape = var_7196, x = k_141_cast)[name = tensor("op_7197_cast")]; + tensor var_7198 = const()[name = tensor("op_7198"), val = tensor([2, 20, 64, -1])]; + tensor var_7199_cast = reshape(shape = var_7198, x = v_141_cast)[name = tensor("op_7199_cast")]; + tensor attn_weights_281_transpose_x_0 = const()[name = tensor("attn_weights_281_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_281_transpose_y_0 = const()[name = tensor("attn_weights_281_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_281_cast = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_7195_cast, y = var_7197_cast)[name = tensor("attn_weights_281_cast")]; + tensor attn_weights_283_cast = mul(x = attn_weights_281_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_283_cast")]; + tensor var_7203_cast = softmax(axis = var_6849, x = attn_weights_283_cast)[name = tensor("op_7203_cast")]; + tensor attn_141_transpose_x_0 = const()[name = tensor("attn_141_transpose_x_0"), val = tensor(false)]; + tensor attn_141_transpose_y_0 = const()[name = tensor("attn_141_transpose_y_0"), val = tensor(true)]; + tensor attn_141_cast = matmul(transpose_x = attn_141_transpose_x_0, transpose_y = attn_141_transpose_y_0, x = var_7199_cast, y = var_7203_cast)[name = tensor("attn_141_cast")]; + tensor var_7207 = const()[name = tensor("op_7207"), val = tensor([2, 1280, 1, -1])]; + tensor input_447_cast = reshape(shape = var_7207, x = attn_141_cast)[name = tensor("input_447_cast")]; + tensor var_7212 = const()[name = tensor("op_7212"), val = tensor([1, 1])]; + tensor var_7214 = const()[name = tensor("op_7214"), val = tensor([1, 1])]; + tensor var_7216_pad_type_0 = const()[name = tensor("op_7216_pad_type_0"), val = tensor("custom")]; + tensor var_7216_pad_0 = const()[name = tensor("op_7216_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2667770176)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2671047040)))]; + tensor var_7216_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_7214, groups = var_6865, pad = var_7216_pad_0, pad_type = var_7216_pad_type_0, strides = var_7212, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_447_cast)[name = tensor("op_7216_cast")]; + tensor inputs_213_cast = add(x = var_7216_cast, y = inputs_211_cast)[name = tensor("inputs_213_cast")]; + tensor var_7220 = const()[name = tensor("op_7220"), val = tensor([1])]; + tensor channels_mean_213_cast = reduce_mean(axes = var_7220, keep_dims = var_6860, x = inputs_213_cast)[name = tensor("channels_mean_213_cast")]; + tensor zero_mean_213_cast = sub(x = inputs_213_cast, y = channels_mean_213_cast)[name = tensor("zero_mean_213_cast")]; + tensor zero_mean_sq_213_cast = mul(x = zero_mean_213_cast, y = zero_mean_213_cast)[name = tensor("zero_mean_sq_213_cast")]; + tensor var_7224 = const()[name = tensor("op_7224"), val = tensor([1])]; + tensor var_7225_cast = reduce_mean(axes = var_7224, keep_dims = var_6860, x = zero_mean_sq_213_cast)[name = tensor("op_7225_cast")]; + tensor var_7226_to_fp16 = const()[name = tensor("op_7226_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7227_cast = add(x = var_7225_cast, y = var_7226_to_fp16)[name = tensor("op_7227_cast")]; + tensor denom_213_epsilon_0_to_fp16 = const()[name = tensor("denom_213_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_213_cast = rsqrt(epsilon = denom_213_epsilon_0_to_fp16, x = var_7227_cast)[name = tensor("denom_213_cast")]; + tensor out_213_cast = mul(x = zero_mean_213_cast, y = denom_213_cast)[name = tensor("out_213_cast")]; + tensor var_7231_to_fp16 = const()[name = tensor("op_7231_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2671049664)))]; + tensor var_7232_cast = add(x = out_213_cast, y = var_7231_to_fp16)[name = tensor("op_7232_cast")]; + tensor var_7234_to_fp16 = const()[name = tensor("op_7234_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2671052288)))]; + tensor hidden_states_295_cast = mul(x = var_7232_cast, y = var_7234_to_fp16)[name = tensor("hidden_states_295_cast")]; + tensor var_7241 = const()[name = tensor("op_7241"), val = tensor([1, 1])]; + tensor var_7243 = const()[name = tensor("op_7243"), val = tensor([1, 1])]; + tensor q_143_pad_type_0 = const()[name = tensor("q_143_pad_type_0"), val = tensor("custom")]; + tensor q_143_pad_0 = const()[name = tensor("q_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2671054912)))]; + tensor q_143_cast = conv(dilations = var_7243, groups = var_6865, pad = q_143_pad_0, pad_type = q_143_pad_type_0, strides = var_7241, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_295_cast)[name = tensor("q_143_cast")]; + tensor var_7247 = const()[name = tensor("op_7247"), val = tensor([1, 1])]; + tensor var_7249 = const()[name = tensor("op_7249"), val = tensor([1, 1])]; + tensor k_143_pad_type_0 = const()[name = tensor("k_143_pad_type_0"), val = tensor("custom")]; + tensor k_143_pad_0 = const()[name = tensor("k_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2674331776)))]; + tensor k_143_cast = conv(dilations = var_7249, groups = var_6865, pad = k_143_pad_0, pad_type = k_143_pad_type_0, strides = var_7247, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_143_cast")]; + tensor var_7253 = const()[name = tensor("op_7253"), val = tensor([1, 1])]; + tensor var_7255 = const()[name = tensor("op_7255"), val = tensor([1, 1])]; + tensor v_143_pad_type_0 = const()[name = tensor("v_143_pad_type_0"), val = tensor("custom")]; + tensor v_143_pad_0 = const()[name = tensor("v_143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2679574720)))]; + tensor v_143_cast = conv(dilations = var_7255, groups = var_6865, pad = v_143_pad_0, pad_type = v_143_pad_type_0, strides = var_7253, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_143_cast")]; + tensor var_7259 = const()[name = tensor("op_7259"), val = tensor([2, 20, 64, -1])]; + tensor var_7260_cast = reshape(shape = var_7259, x = q_143_cast)[name = tensor("op_7260_cast")]; + tensor var_7261 = const()[name = tensor("op_7261"), val = tensor([2, 20, 64, -1])]; + tensor var_7262_cast = reshape(shape = var_7261, x = k_143_cast)[name = tensor("op_7262_cast")]; + tensor var_7263 = const()[name = tensor("op_7263"), val = tensor([2, 20, 64, -1])]; + tensor var_7264_cast = reshape(shape = var_7263, x = v_143_cast)[name = tensor("op_7264_cast")]; + tensor attn_weights_285_transpose_x_0 = const()[name = tensor("attn_weights_285_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_285_transpose_y_0 = const()[name = tensor("attn_weights_285_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_285_cast = matmul(transpose_x = attn_weights_285_transpose_x_0, transpose_y = attn_weights_285_transpose_y_0, x = var_7260_cast, y = var_7262_cast)[name = tensor("attn_weights_285_cast")]; + tensor attn_weights_287_cast = mul(x = attn_weights_285_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_287_cast")]; + tensor var_7268_cast = softmax(axis = var_6849, x = attn_weights_287_cast)[name = tensor("op_7268_cast")]; + tensor attn_143_transpose_x_0 = const()[name = tensor("attn_143_transpose_x_0"), val = tensor(false)]; + tensor attn_143_transpose_y_0 = const()[name = tensor("attn_143_transpose_y_0"), val = tensor(true)]; + tensor attn_143_cast = matmul(transpose_x = attn_143_transpose_x_0, transpose_y = attn_143_transpose_y_0, x = var_7264_cast, y = var_7268_cast)[name = tensor("attn_143_cast")]; + tensor var_7272 = const()[name = tensor("op_7272"), val = tensor([2, 1280, 1, -1])]; + tensor input_449_cast = reshape(shape = var_7272, x = attn_143_cast)[name = tensor("input_449_cast")]; + tensor var_7277 = const()[name = tensor("op_7277"), val = tensor([1, 1])]; + tensor var_7279 = const()[name = tensor("op_7279"), val = tensor([1, 1])]; + tensor var_7281_pad_type_0 = const()[name = tensor("op_7281_pad_type_0"), val = tensor("custom")]; + tensor var_7281_pad_0 = const()[name = tensor("op_7281_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2684817664)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2688094528)))]; + tensor var_7281_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_7279, groups = var_6865, pad = var_7281_pad_0, pad_type = var_7281_pad_type_0, strides = var_7277, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_449_cast)[name = tensor("op_7281_cast")]; + tensor inputs_215_cast = add(x = var_7281_cast, y = inputs_213_cast)[name = tensor("inputs_215_cast")]; + tensor var_7285 = const()[name = tensor("op_7285"), val = tensor([1])]; + tensor channels_mean_215_cast = reduce_mean(axes = var_7285, keep_dims = var_6860, x = inputs_215_cast)[name = tensor("channels_mean_215_cast")]; + tensor zero_mean_215_cast = sub(x = inputs_215_cast, y = channels_mean_215_cast)[name = tensor("zero_mean_215_cast")]; + tensor zero_mean_sq_215_cast = mul(x = zero_mean_215_cast, y = zero_mean_215_cast)[name = tensor("zero_mean_sq_215_cast")]; + tensor var_7289 = const()[name = tensor("op_7289"), val = tensor([1])]; + tensor var_7290_cast = reduce_mean(axes = var_7289, keep_dims = var_6860, x = zero_mean_sq_215_cast)[name = tensor("op_7290_cast")]; + tensor var_7291_to_fp16 = const()[name = tensor("op_7291_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7292_cast = add(x = var_7290_cast, y = var_7291_to_fp16)[name = tensor("op_7292_cast")]; + tensor denom_215_epsilon_0_to_fp16 = const()[name = tensor("denom_215_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_215_cast = rsqrt(epsilon = denom_215_epsilon_0_to_fp16, x = var_7292_cast)[name = tensor("denom_215_cast")]; + tensor out_215_cast = mul(x = zero_mean_215_cast, y = denom_215_cast)[name = tensor("out_215_cast")]; + tensor var_7296_to_fp16 = const()[name = tensor("op_7296_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2688097152)))]; + tensor var_7297_cast = add(x = out_215_cast, y = var_7296_to_fp16)[name = tensor("op_7297_cast")]; + tensor var_7299_to_fp16 = const()[name = tensor("op_7299_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2688099776)))]; + tensor input_451_cast = mul(x = var_7297_cast, y = var_7299_to_fp16)[name = tensor("input_451_cast")]; + tensor var_7307 = const()[name = tensor("op_7307"), val = tensor([1, 1])]; + tensor var_7309 = const()[name = tensor("op_7309"), val = tensor([1, 1])]; + tensor var_7311_pad_type_0 = const()[name = tensor("op_7311_pad_type_0"), val = tensor("custom")]; + tensor var_7311_pad_0 = const()[name = tensor("op_7311_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2688102400)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2714316864)))]; + tensor var_7311_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_7309, groups = var_6865, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7307, weight = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_451_cast)[name = tensor("op_7311_cast")]; + tensor var_7312_split_sizes_0 = const()[name = tensor("op_7312_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7312_axis_0 = const()[name = tensor("op_7312_axis_0"), val = tensor(1)]; + tensor var_7312_cast_0, tensor var_7312_cast_1 = split(axis = var_7312_axis_0, split_sizes = var_7312_split_sizes_0, x = var_7311_cast)[name = tensor("op_7312_cast")]; + tensor var_7314_mode_0 = const()[name = tensor("op_7314_mode_0"), val = tensor("EXACT")]; + tensor var_7314_cast = gelu(mode = var_7314_mode_0, x = var_7312_cast_1)[name = tensor("op_7314_cast")]; + tensor input_453_cast = mul(x = var_7312_cast_0, y = var_7314_cast)[name = tensor("input_453_cast")]; + tensor var_7318 = const()[name = tensor("op_7318"), val = tensor([1, 1])]; + tensor var_7320 = const()[name = tensor("op_7320"), val = tensor([1, 1])]; + tensor var_7322_pad_type_0 = const()[name = tensor("op_7322_pad_type_0"), val = tensor("custom")]; + tensor var_7322_pad_0 = const()[name = tensor("op_7322_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2714337408)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2727444672)))]; + tensor var_7322_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_7320, groups = var_6865, pad = var_7322_pad_0, pad_type = var_7322_pad_type_0, strides = var_7318, weight = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_453_cast)[name = tensor("op_7322_cast")]; + tensor inputs_217_cast = add(x = var_7322_cast, y = inputs_215_cast)[name = tensor("inputs_217_cast")]; + tensor var_7332 = const()[name = tensor("op_7332"), val = tensor([1])]; + tensor channels_mean_217_cast = reduce_mean(axes = var_7332, keep_dims = var_6860, x = inputs_217_cast)[name = tensor("channels_mean_217_cast")]; + tensor zero_mean_217_cast = sub(x = inputs_217_cast, y = channels_mean_217_cast)[name = tensor("zero_mean_217_cast")]; + tensor zero_mean_sq_217_cast = mul(x = zero_mean_217_cast, y = zero_mean_217_cast)[name = tensor("zero_mean_sq_217_cast")]; + tensor var_7336 = const()[name = tensor("op_7336"), val = tensor([1])]; + tensor var_7337_cast = reduce_mean(axes = var_7336, keep_dims = var_6860, x = zero_mean_sq_217_cast)[name = tensor("op_7337_cast")]; + tensor var_7338_to_fp16 = const()[name = tensor("op_7338_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7339_cast = add(x = var_7337_cast, y = var_7338_to_fp16)[name = tensor("op_7339_cast")]; + tensor denom_217_epsilon_0_to_fp16 = const()[name = tensor("denom_217_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_217_cast = rsqrt(epsilon = denom_217_epsilon_0_to_fp16, x = var_7339_cast)[name = tensor("denom_217_cast")]; + tensor out_217_cast = mul(x = zero_mean_217_cast, y = denom_217_cast)[name = tensor("out_217_cast")]; + tensor var_7343_to_fp16 = const()[name = tensor("op_7343_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2727447296)))]; + tensor var_7344_cast = add(x = out_217_cast, y = var_7343_to_fp16)[name = tensor("op_7344_cast")]; + tensor var_7346_to_fp16 = const()[name = tensor("op_7346_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2727449920)))]; + tensor hidden_states_299_cast = mul(x = var_7344_cast, y = var_7346_to_fp16)[name = tensor("hidden_states_299_cast")]; + tensor var_7353 = const()[name = tensor("op_7353"), val = tensor([1, 1])]; + tensor var_7355 = const()[name = tensor("op_7355"), val = tensor([1, 1])]; + tensor q_145_pad_type_0 = const()[name = tensor("q_145_pad_type_0"), val = tensor("custom")]; + tensor q_145_pad_0 = const()[name = tensor("q_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2727452544)))]; + tensor q_145_cast = conv(dilations = var_7355, groups = var_6865, pad = q_145_pad_0, pad_type = q_145_pad_type_0, strides = var_7353, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_299_cast)[name = tensor("q_145_cast")]; + tensor var_7359 = const()[name = tensor("op_7359"), val = tensor([1, 1])]; + tensor var_7361 = const()[name = tensor("op_7361"), val = tensor([1, 1])]; + tensor k_145_pad_type_0 = const()[name = tensor("k_145_pad_type_0"), val = tensor("custom")]; + tensor k_145_pad_0 = const()[name = tensor("k_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2730729408)))]; + tensor k_145_cast = conv(dilations = var_7361, groups = var_6865, pad = k_145_pad_0, pad_type = k_145_pad_type_0, strides = var_7359, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_299_cast)[name = tensor("k_145_cast")]; + tensor var_7365 = const()[name = tensor("op_7365"), val = tensor([1, 1])]; + tensor var_7367 = const()[name = tensor("op_7367"), val = tensor([1, 1])]; + tensor v_145_pad_type_0 = const()[name = tensor("v_145_pad_type_0"), val = tensor("custom")]; + tensor v_145_pad_0 = const()[name = tensor("v_145_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2734006272)))]; + tensor v_145_cast = conv(dilations = var_7367, groups = var_6865, pad = v_145_pad_0, pad_type = v_145_pad_type_0, strides = var_7365, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_299_cast)[name = tensor("v_145_cast")]; + tensor var_7371 = const()[name = tensor("op_7371"), val = tensor([2, 20, 64, -1])]; + tensor var_7372_cast = reshape(shape = var_7371, x = q_145_cast)[name = tensor("op_7372_cast")]; + tensor var_7373 = const()[name = tensor("op_7373"), val = tensor([2, 20, 64, -1])]; + tensor var_7374_cast = reshape(shape = var_7373, x = k_145_cast)[name = tensor("op_7374_cast")]; + tensor var_7375 = const()[name = tensor("op_7375"), val = tensor([2, 20, 64, -1])]; + tensor var_7376_cast = reshape(shape = var_7375, x = v_145_cast)[name = tensor("op_7376_cast")]; + tensor attn_weights_289_transpose_x_0 = const()[name = tensor("attn_weights_289_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_289_transpose_y_0 = const()[name = tensor("attn_weights_289_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_289_cast = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_7372_cast, y = var_7374_cast)[name = tensor("attn_weights_289_cast")]; + tensor attn_weights_291_cast = mul(x = attn_weights_289_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_291_cast")]; + tensor var_7380_cast = softmax(axis = var_6849, x = attn_weights_291_cast)[name = tensor("op_7380_cast")]; + tensor attn_145_transpose_x_0 = const()[name = tensor("attn_145_transpose_x_0"), val = tensor(false)]; + tensor attn_145_transpose_y_0 = const()[name = tensor("attn_145_transpose_y_0"), val = tensor(true)]; + tensor attn_145_cast = matmul(transpose_x = attn_145_transpose_x_0, transpose_y = attn_145_transpose_y_0, x = var_7376_cast, y = var_7380_cast)[name = tensor("attn_145_cast")]; + tensor var_7384 = const()[name = tensor("op_7384"), val = tensor([2, 1280, 1, -1])]; + tensor input_455_cast = reshape(shape = var_7384, x = attn_145_cast)[name = tensor("input_455_cast")]; + tensor var_7389 = const()[name = tensor("op_7389"), val = tensor([1, 1])]; + tensor var_7391 = const()[name = tensor("op_7391"), val = tensor([1, 1])]; + tensor var_7393_pad_type_0 = const()[name = tensor("op_7393_pad_type_0"), val = tensor("custom")]; + tensor var_7393_pad_0 = const()[name = tensor("op_7393_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2737283136)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740560000)))]; + tensor var_7393_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_7391, groups = var_6865, pad = var_7393_pad_0, pad_type = var_7393_pad_type_0, strides = var_7389, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_455_cast)[name = tensor("op_7393_cast")]; + tensor inputs_219_cast = add(x = var_7393_cast, y = inputs_217_cast)[name = tensor("inputs_219_cast")]; + tensor var_7397 = const()[name = tensor("op_7397"), val = tensor([1])]; + tensor channels_mean_219_cast = reduce_mean(axes = var_7397, keep_dims = var_6860, x = inputs_219_cast)[name = tensor("channels_mean_219_cast")]; + tensor zero_mean_219_cast = sub(x = inputs_219_cast, y = channels_mean_219_cast)[name = tensor("zero_mean_219_cast")]; + tensor zero_mean_sq_219_cast = mul(x = zero_mean_219_cast, y = zero_mean_219_cast)[name = tensor("zero_mean_sq_219_cast")]; + tensor var_7401 = const()[name = tensor("op_7401"), val = tensor([1])]; + tensor var_7402_cast = reduce_mean(axes = var_7401, keep_dims = var_6860, x = zero_mean_sq_219_cast)[name = tensor("op_7402_cast")]; + tensor var_7403_to_fp16 = const()[name = tensor("op_7403_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7404_cast = add(x = var_7402_cast, y = var_7403_to_fp16)[name = tensor("op_7404_cast")]; + tensor denom_219_epsilon_0_to_fp16 = const()[name = tensor("denom_219_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_219_cast = rsqrt(epsilon = denom_219_epsilon_0_to_fp16, x = var_7404_cast)[name = tensor("denom_219_cast")]; + tensor out_219_cast = mul(x = zero_mean_219_cast, y = denom_219_cast)[name = tensor("out_219_cast")]; + tensor var_7408_to_fp16 = const()[name = tensor("op_7408_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740562624)))]; + tensor var_7409_cast = add(x = out_219_cast, y = var_7408_to_fp16)[name = tensor("op_7409_cast")]; + tensor var_7411_to_fp16 = const()[name = tensor("op_7411_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740565248)))]; + tensor hidden_states_301_cast = mul(x = var_7409_cast, y = var_7411_to_fp16)[name = tensor("hidden_states_301_cast")]; + tensor var_7418 = const()[name = tensor("op_7418"), val = tensor([1, 1])]; + tensor var_7420 = const()[name = tensor("op_7420"), val = tensor([1, 1])]; + tensor q_147_pad_type_0 = const()[name = tensor("q_147_pad_type_0"), val = tensor("custom")]; + tensor q_147_pad_0 = const()[name = tensor("q_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740567872)))]; + tensor q_147_cast = conv(dilations = var_7420, groups = var_6865, pad = q_147_pad_0, pad_type = q_147_pad_type_0, strides = var_7418, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_301_cast)[name = tensor("q_147_cast")]; + tensor var_7424 = const()[name = tensor("op_7424"), val = tensor([1, 1])]; + tensor var_7426 = const()[name = tensor("op_7426"), val = tensor([1, 1])]; + tensor k_147_pad_type_0 = const()[name = tensor("k_147_pad_type_0"), val = tensor("custom")]; + tensor k_147_pad_0 = const()[name = tensor("k_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2743844736)))]; + tensor k_147_cast = conv(dilations = var_7426, groups = var_6865, pad = k_147_pad_0, pad_type = k_147_pad_type_0, strides = var_7424, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_147_cast")]; + tensor var_7430 = const()[name = tensor("op_7430"), val = tensor([1, 1])]; + tensor var_7432 = const()[name = tensor("op_7432"), val = tensor([1, 1])]; + tensor v_147_pad_type_0 = const()[name = tensor("v_147_pad_type_0"), val = tensor("custom")]; + tensor v_147_pad_0 = const()[name = tensor("v_147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2749087680)))]; + tensor v_147_cast = conv(dilations = var_7432, groups = var_6865, pad = v_147_pad_0, pad_type = v_147_pad_type_0, strides = var_7430, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_147_cast")]; + tensor var_7436 = const()[name = tensor("op_7436"), val = tensor([2, 20, 64, -1])]; + tensor var_7437_cast = reshape(shape = var_7436, x = q_147_cast)[name = tensor("op_7437_cast")]; + tensor var_7438 = const()[name = tensor("op_7438"), val = tensor([2, 20, 64, -1])]; + tensor var_7439_cast = reshape(shape = var_7438, x = k_147_cast)[name = tensor("op_7439_cast")]; + tensor var_7440 = const()[name = tensor("op_7440"), val = tensor([2, 20, 64, -1])]; + tensor var_7441_cast = reshape(shape = var_7440, x = v_147_cast)[name = tensor("op_7441_cast")]; + tensor attn_weights_293_transpose_x_0 = const()[name = tensor("attn_weights_293_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_293_transpose_y_0 = const()[name = tensor("attn_weights_293_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_293_cast = matmul(transpose_x = attn_weights_293_transpose_x_0, transpose_y = attn_weights_293_transpose_y_0, x = var_7437_cast, y = var_7439_cast)[name = tensor("attn_weights_293_cast")]; + tensor attn_weights_295_cast = mul(x = attn_weights_293_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_295_cast")]; + tensor var_7445_cast = softmax(axis = var_6849, x = attn_weights_295_cast)[name = tensor("op_7445_cast")]; + tensor attn_147_transpose_x_0 = const()[name = tensor("attn_147_transpose_x_0"), val = tensor(false)]; + tensor attn_147_transpose_y_0 = const()[name = tensor("attn_147_transpose_y_0"), val = tensor(true)]; + tensor attn_147_cast = matmul(transpose_x = attn_147_transpose_x_0, transpose_y = attn_147_transpose_y_0, x = var_7441_cast, y = var_7445_cast)[name = tensor("attn_147_cast")]; + tensor var_7449 = const()[name = tensor("op_7449"), val = tensor([2, 1280, 1, -1])]; + tensor input_457_cast = reshape(shape = var_7449, x = attn_147_cast)[name = tensor("input_457_cast")]; + tensor var_7454 = const()[name = tensor("op_7454"), val = tensor([1, 1])]; + tensor var_7456 = const()[name = tensor("op_7456"), val = tensor([1, 1])]; + tensor var_7458_pad_type_0 = const()[name = tensor("op_7458_pad_type_0"), val = tensor("custom")]; + tensor var_7458_pad_0 = const()[name = tensor("op_7458_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2754330624)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2757607488)))]; + tensor var_7458_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_7456, groups = var_6865, pad = var_7458_pad_0, pad_type = var_7458_pad_type_0, strides = var_7454, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_457_cast)[name = tensor("op_7458_cast")]; + tensor inputs_221_cast = add(x = var_7458_cast, y = inputs_219_cast)[name = tensor("inputs_221_cast")]; + tensor var_7462 = const()[name = tensor("op_7462"), val = tensor([1])]; + tensor channels_mean_221_cast = reduce_mean(axes = var_7462, keep_dims = var_6860, x = inputs_221_cast)[name = tensor("channels_mean_221_cast")]; + tensor zero_mean_221_cast = sub(x = inputs_221_cast, y = channels_mean_221_cast)[name = tensor("zero_mean_221_cast")]; + tensor zero_mean_sq_221_cast = mul(x = zero_mean_221_cast, y = zero_mean_221_cast)[name = tensor("zero_mean_sq_221_cast")]; + tensor var_7466 = const()[name = tensor("op_7466"), val = tensor([1])]; + tensor var_7467_cast = reduce_mean(axes = var_7466, keep_dims = var_6860, x = zero_mean_sq_221_cast)[name = tensor("op_7467_cast")]; + tensor var_7468_to_fp16 = const()[name = tensor("op_7468_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7469_cast = add(x = var_7467_cast, y = var_7468_to_fp16)[name = tensor("op_7469_cast")]; + tensor denom_221_epsilon_0_to_fp16 = const()[name = tensor("denom_221_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_221_cast = rsqrt(epsilon = denom_221_epsilon_0_to_fp16, x = var_7469_cast)[name = tensor("denom_221_cast")]; + tensor out_221_cast = mul(x = zero_mean_221_cast, y = denom_221_cast)[name = tensor("out_221_cast")]; + tensor var_7473_to_fp16 = const()[name = tensor("op_7473_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2757610112)))]; + tensor var_7474_cast = add(x = out_221_cast, y = var_7473_to_fp16)[name = tensor("op_7474_cast")]; + tensor var_7476_to_fp16 = const()[name = tensor("op_7476_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2757612736)))]; + tensor input_459_cast = mul(x = var_7474_cast, y = var_7476_to_fp16)[name = tensor("input_459_cast")]; + tensor var_7484 = const()[name = tensor("op_7484"), val = tensor([1, 1])]; + tensor var_7486 = const()[name = tensor("op_7486"), val = tensor([1, 1])]; + tensor var_7488_pad_type_0 = const()[name = tensor("op_7488_pad_type_0"), val = tensor("custom")]; + tensor var_7488_pad_0 = const()[name = tensor("op_7488_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2757615360)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2783829824)))]; + tensor var_7488_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_7486, groups = var_6865, pad = var_7488_pad_0, pad_type = var_7488_pad_type_0, strides = var_7484, weight = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_459_cast)[name = tensor("op_7488_cast")]; + tensor var_7489_split_sizes_0 = const()[name = tensor("op_7489_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7489_axis_0 = const()[name = tensor("op_7489_axis_0"), val = tensor(1)]; + tensor var_7489_cast_0, tensor var_7489_cast_1 = split(axis = var_7489_axis_0, split_sizes = var_7489_split_sizes_0, x = var_7488_cast)[name = tensor("op_7489_cast")]; + tensor var_7491_mode_0 = const()[name = tensor("op_7491_mode_0"), val = tensor("EXACT")]; + tensor var_7491_cast = gelu(mode = var_7491_mode_0, x = var_7489_cast_1)[name = tensor("op_7491_cast")]; + tensor input_461_cast = mul(x = var_7489_cast_0, y = var_7491_cast)[name = tensor("input_461_cast")]; + tensor var_7495 = const()[name = tensor("op_7495"), val = tensor([1, 1])]; + tensor var_7497 = const()[name = tensor("op_7497"), val = tensor([1, 1])]; + tensor var_7499_pad_type_0 = const()[name = tensor("op_7499_pad_type_0"), val = tensor("custom")]; + tensor var_7499_pad_0 = const()[name = tensor("op_7499_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2783850368)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2796957632)))]; + tensor var_7499_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_7497, groups = var_6865, pad = var_7499_pad_0, pad_type = var_7499_pad_type_0, strides = var_7495, weight = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_461_cast)[name = tensor("op_7499_cast")]; + tensor inputs_223_cast = add(x = var_7499_cast, y = inputs_221_cast)[name = tensor("inputs_223_cast")]; + tensor var_7509 = const()[name = tensor("op_7509"), val = tensor([1])]; + tensor channels_mean_223_cast = reduce_mean(axes = var_7509, keep_dims = var_6860, x = inputs_223_cast)[name = tensor("channels_mean_223_cast")]; + tensor zero_mean_223_cast = sub(x = inputs_223_cast, y = channels_mean_223_cast)[name = tensor("zero_mean_223_cast")]; + tensor zero_mean_sq_223_cast = mul(x = zero_mean_223_cast, y = zero_mean_223_cast)[name = tensor("zero_mean_sq_223_cast")]; + tensor var_7513 = const()[name = tensor("op_7513"), val = tensor([1])]; + tensor var_7514_cast = reduce_mean(axes = var_7513, keep_dims = var_6860, x = zero_mean_sq_223_cast)[name = tensor("op_7514_cast")]; + tensor var_7515_to_fp16 = const()[name = tensor("op_7515_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7516_cast = add(x = var_7514_cast, y = var_7515_to_fp16)[name = tensor("op_7516_cast")]; + tensor denom_223_epsilon_0_to_fp16 = const()[name = tensor("denom_223_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_223_cast = rsqrt(epsilon = denom_223_epsilon_0_to_fp16, x = var_7516_cast)[name = tensor("denom_223_cast")]; + tensor out_223_cast = mul(x = zero_mean_223_cast, y = denom_223_cast)[name = tensor("out_223_cast")]; + tensor var_7520_to_fp16 = const()[name = tensor("op_7520_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2796960256)))]; + tensor var_7521_cast = add(x = out_223_cast, y = var_7520_to_fp16)[name = tensor("op_7521_cast")]; + tensor var_7523_to_fp16 = const()[name = tensor("op_7523_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2796962880)))]; + tensor hidden_states_305_cast = mul(x = var_7521_cast, y = var_7523_to_fp16)[name = tensor("hidden_states_305_cast")]; + tensor var_7530 = const()[name = tensor("op_7530"), val = tensor([1, 1])]; + tensor var_7532 = const()[name = tensor("op_7532"), val = tensor([1, 1])]; + tensor q_149_pad_type_0 = const()[name = tensor("q_149_pad_type_0"), val = tensor("custom")]; + tensor q_149_pad_0 = const()[name = tensor("q_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2796965504)))]; + tensor q_149_cast = conv(dilations = var_7532, groups = var_6865, pad = q_149_pad_0, pad_type = q_149_pad_type_0, strides = var_7530, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_305_cast)[name = tensor("q_149_cast")]; + tensor var_7536 = const()[name = tensor("op_7536"), val = tensor([1, 1])]; + tensor var_7538 = const()[name = tensor("op_7538"), val = tensor([1, 1])]; + tensor k_149_pad_type_0 = const()[name = tensor("k_149_pad_type_0"), val = tensor("custom")]; + tensor k_149_pad_0 = const()[name = tensor("k_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2800242368)))]; + tensor k_149_cast = conv(dilations = var_7538, groups = var_6865, pad = k_149_pad_0, pad_type = k_149_pad_type_0, strides = var_7536, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_305_cast)[name = tensor("k_149_cast")]; + tensor var_7542 = const()[name = tensor("op_7542"), val = tensor([1, 1])]; + tensor var_7544 = const()[name = tensor("op_7544"), val = tensor([1, 1])]; + tensor v_149_pad_type_0 = const()[name = tensor("v_149_pad_type_0"), val = tensor("custom")]; + tensor v_149_pad_0 = const()[name = tensor("v_149_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2803519232)))]; + tensor v_149_cast = conv(dilations = var_7544, groups = var_6865, pad = v_149_pad_0, pad_type = v_149_pad_type_0, strides = var_7542, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_305_cast)[name = tensor("v_149_cast")]; + tensor var_7548 = const()[name = tensor("op_7548"), val = tensor([2, 20, 64, -1])]; + tensor var_7549_cast = reshape(shape = var_7548, x = q_149_cast)[name = tensor("op_7549_cast")]; + tensor var_7550 = const()[name = tensor("op_7550"), val = tensor([2, 20, 64, -1])]; + tensor var_7551_cast = reshape(shape = var_7550, x = k_149_cast)[name = tensor("op_7551_cast")]; + tensor var_7552 = const()[name = tensor("op_7552"), val = tensor([2, 20, 64, -1])]; + tensor var_7553_cast = reshape(shape = var_7552, x = v_149_cast)[name = tensor("op_7553_cast")]; + tensor attn_weights_297_transpose_x_0 = const()[name = tensor("attn_weights_297_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_297_transpose_y_0 = const()[name = tensor("attn_weights_297_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_297_cast = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_7549_cast, y = var_7551_cast)[name = tensor("attn_weights_297_cast")]; + tensor attn_weights_299_cast = mul(x = attn_weights_297_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_299_cast")]; + tensor var_7557_cast = softmax(axis = var_6849, x = attn_weights_299_cast)[name = tensor("op_7557_cast")]; + tensor attn_149_transpose_x_0 = const()[name = tensor("attn_149_transpose_x_0"), val = tensor(false)]; + tensor attn_149_transpose_y_0 = const()[name = tensor("attn_149_transpose_y_0"), val = tensor(true)]; + tensor attn_149_cast = matmul(transpose_x = attn_149_transpose_x_0, transpose_y = attn_149_transpose_y_0, x = var_7553_cast, y = var_7557_cast)[name = tensor("attn_149_cast")]; + tensor var_7561 = const()[name = tensor("op_7561"), val = tensor([2, 1280, 1, -1])]; + tensor input_463_cast = reshape(shape = var_7561, x = attn_149_cast)[name = tensor("input_463_cast")]; + tensor var_7566 = const()[name = tensor("op_7566"), val = tensor([1, 1])]; + tensor var_7568 = const()[name = tensor("op_7568"), val = tensor([1, 1])]; + tensor var_7570_pad_type_0 = const()[name = tensor("op_7570_pad_type_0"), val = tensor("custom")]; + tensor var_7570_pad_0 = const()[name = tensor("op_7570_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2806796096)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2810072960)))]; + tensor var_7570_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_7568, groups = var_6865, pad = var_7570_pad_0, pad_type = var_7570_pad_type_0, strides = var_7566, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_463_cast)[name = tensor("op_7570_cast")]; + tensor inputs_225_cast = add(x = var_7570_cast, y = inputs_223_cast)[name = tensor("inputs_225_cast")]; + tensor var_7574 = const()[name = tensor("op_7574"), val = tensor([1])]; + tensor channels_mean_225_cast = reduce_mean(axes = var_7574, keep_dims = var_6860, x = inputs_225_cast)[name = tensor("channels_mean_225_cast")]; + tensor zero_mean_225_cast = sub(x = inputs_225_cast, y = channels_mean_225_cast)[name = tensor("zero_mean_225_cast")]; + tensor zero_mean_sq_225_cast = mul(x = zero_mean_225_cast, y = zero_mean_225_cast)[name = tensor("zero_mean_sq_225_cast")]; + tensor var_7578 = const()[name = tensor("op_7578"), val = tensor([1])]; + tensor var_7579_cast = reduce_mean(axes = var_7578, keep_dims = var_6860, x = zero_mean_sq_225_cast)[name = tensor("op_7579_cast")]; + tensor var_7580_to_fp16 = const()[name = tensor("op_7580_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7581_cast = add(x = var_7579_cast, y = var_7580_to_fp16)[name = tensor("op_7581_cast")]; + tensor denom_225_epsilon_0_to_fp16 = const()[name = tensor("denom_225_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_225_cast = rsqrt(epsilon = denom_225_epsilon_0_to_fp16, x = var_7581_cast)[name = tensor("denom_225_cast")]; + tensor out_225_cast = mul(x = zero_mean_225_cast, y = denom_225_cast)[name = tensor("out_225_cast")]; + tensor var_7585_to_fp16 = const()[name = tensor("op_7585_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2810075584)))]; + tensor var_7586_cast = add(x = out_225_cast, y = var_7585_to_fp16)[name = tensor("op_7586_cast")]; + tensor var_7588_to_fp16 = const()[name = tensor("op_7588_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2810078208)))]; + tensor hidden_states_307_cast = mul(x = var_7586_cast, y = var_7588_to_fp16)[name = tensor("hidden_states_307_cast")]; + tensor var_7595 = const()[name = tensor("op_7595"), val = tensor([1, 1])]; + tensor var_7597 = const()[name = tensor("op_7597"), val = tensor([1, 1])]; + tensor q_151_pad_type_0 = const()[name = tensor("q_151_pad_type_0"), val = tensor("custom")]; + tensor q_151_pad_0 = const()[name = tensor("q_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2810080832)))]; + tensor q_151_cast = conv(dilations = var_7597, groups = var_6865, pad = q_151_pad_0, pad_type = q_151_pad_type_0, strides = var_7595, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_307_cast)[name = tensor("q_151_cast")]; + tensor var_7601 = const()[name = tensor("op_7601"), val = tensor([1, 1])]; + tensor var_7603 = const()[name = tensor("op_7603"), val = tensor([1, 1])]; + tensor k_151_pad_type_0 = const()[name = tensor("k_151_pad_type_0"), val = tensor("custom")]; + tensor k_151_pad_0 = const()[name = tensor("k_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2813357696)))]; + tensor k_151_cast = conv(dilations = var_7603, groups = var_6865, pad = k_151_pad_0, pad_type = k_151_pad_type_0, strides = var_7601, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_151_cast")]; + tensor var_7607 = const()[name = tensor("op_7607"), val = tensor([1, 1])]; + tensor var_7609 = const()[name = tensor("op_7609"), val = tensor([1, 1])]; + tensor v_151_pad_type_0 = const()[name = tensor("v_151_pad_type_0"), val = tensor("custom")]; + tensor v_151_pad_0 = const()[name = tensor("v_151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2818600640)))]; + tensor v_151_cast = conv(dilations = var_7609, groups = var_6865, pad = v_151_pad_0, pad_type = v_151_pad_type_0, strides = var_7607, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_151_cast")]; + tensor var_7613 = const()[name = tensor("op_7613"), val = tensor([2, 20, 64, -1])]; + tensor var_7614_cast = reshape(shape = var_7613, x = q_151_cast)[name = tensor("op_7614_cast")]; + tensor var_7615 = const()[name = tensor("op_7615"), val = tensor([2, 20, 64, -1])]; + tensor var_7616_cast = reshape(shape = var_7615, x = k_151_cast)[name = tensor("op_7616_cast")]; + tensor var_7617 = const()[name = tensor("op_7617"), val = tensor([2, 20, 64, -1])]; + tensor var_7618_cast = reshape(shape = var_7617, x = v_151_cast)[name = tensor("op_7618_cast")]; + tensor attn_weights_301_transpose_x_0 = const()[name = tensor("attn_weights_301_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_301_transpose_y_0 = const()[name = tensor("attn_weights_301_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_301_cast = matmul(transpose_x = attn_weights_301_transpose_x_0, transpose_y = attn_weights_301_transpose_y_0, x = var_7614_cast, y = var_7616_cast)[name = tensor("attn_weights_301_cast")]; + tensor attn_weights_303_cast = mul(x = attn_weights_301_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_303_cast")]; + tensor var_7622_cast = softmax(axis = var_6849, x = attn_weights_303_cast)[name = tensor("op_7622_cast")]; + tensor attn_151_transpose_x_0 = const()[name = tensor("attn_151_transpose_x_0"), val = tensor(false)]; + tensor attn_151_transpose_y_0 = const()[name = tensor("attn_151_transpose_y_0"), val = tensor(true)]; + tensor attn_151_cast = matmul(transpose_x = attn_151_transpose_x_0, transpose_y = attn_151_transpose_y_0, x = var_7618_cast, y = var_7622_cast)[name = tensor("attn_151_cast")]; + tensor var_7626 = const()[name = tensor("op_7626"), val = tensor([2, 1280, 1, -1])]; + tensor input_465_cast = reshape(shape = var_7626, x = attn_151_cast)[name = tensor("input_465_cast")]; + tensor var_7631 = const()[name = tensor("op_7631"), val = tensor([1, 1])]; + tensor var_7633 = const()[name = tensor("op_7633"), val = tensor([1, 1])]; + tensor var_7635_pad_type_0 = const()[name = tensor("op_7635_pad_type_0"), val = tensor("custom")]; + tensor var_7635_pad_0 = const()[name = tensor("op_7635_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2823843584)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2827120448)))]; + tensor var_7635_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_7633, groups = var_6865, pad = var_7635_pad_0, pad_type = var_7635_pad_type_0, strides = var_7631, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_465_cast)[name = tensor("op_7635_cast")]; + tensor inputs_227_cast = add(x = var_7635_cast, y = inputs_225_cast)[name = tensor("inputs_227_cast")]; + tensor var_7639 = const()[name = tensor("op_7639"), val = tensor([1])]; + tensor channels_mean_227_cast = reduce_mean(axes = var_7639, keep_dims = var_6860, x = inputs_227_cast)[name = tensor("channels_mean_227_cast")]; + tensor zero_mean_227_cast = sub(x = inputs_227_cast, y = channels_mean_227_cast)[name = tensor("zero_mean_227_cast")]; + tensor zero_mean_sq_227_cast = mul(x = zero_mean_227_cast, y = zero_mean_227_cast)[name = tensor("zero_mean_sq_227_cast")]; + tensor var_7643 = const()[name = tensor("op_7643"), val = tensor([1])]; + tensor var_7644_cast = reduce_mean(axes = var_7643, keep_dims = var_6860, x = zero_mean_sq_227_cast)[name = tensor("op_7644_cast")]; + tensor var_7645_to_fp16 = const()[name = tensor("op_7645_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7646_cast = add(x = var_7644_cast, y = var_7645_to_fp16)[name = tensor("op_7646_cast")]; + tensor denom_227_epsilon_0_to_fp16 = const()[name = tensor("denom_227_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_227_cast = rsqrt(epsilon = denom_227_epsilon_0_to_fp16, x = var_7646_cast)[name = tensor("denom_227_cast")]; + tensor out_227_cast = mul(x = zero_mean_227_cast, y = denom_227_cast)[name = tensor("out_227_cast")]; + tensor var_7650_to_fp16 = const()[name = tensor("op_7650_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2827123072)))]; + tensor var_7651_cast = add(x = out_227_cast, y = var_7650_to_fp16)[name = tensor("op_7651_cast")]; + tensor var_7653_to_fp16 = const()[name = tensor("op_7653_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2827125696)))]; + tensor input_467_cast = mul(x = var_7651_cast, y = var_7653_to_fp16)[name = tensor("input_467_cast")]; + tensor var_7661 = const()[name = tensor("op_7661"), val = tensor([1, 1])]; + tensor var_7663 = const()[name = tensor("op_7663"), val = tensor([1, 1])]; + tensor var_7665_pad_type_0 = const()[name = tensor("op_7665_pad_type_0"), val = tensor("custom")]; + tensor var_7665_pad_0 = const()[name = tensor("op_7665_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2827128320)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2853342784)))]; + tensor var_7665_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_7663, groups = var_6865, pad = var_7665_pad_0, pad_type = var_7665_pad_type_0, strides = var_7661, weight = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_467_cast)[name = tensor("op_7665_cast")]; + tensor var_7666_split_sizes_0 = const()[name = tensor("op_7666_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7666_axis_0 = const()[name = tensor("op_7666_axis_0"), val = tensor(1)]; + tensor var_7666_cast_0, tensor var_7666_cast_1 = split(axis = var_7666_axis_0, split_sizes = var_7666_split_sizes_0, x = var_7665_cast)[name = tensor("op_7666_cast")]; + tensor var_7668_mode_0 = const()[name = tensor("op_7668_mode_0"), val = tensor("EXACT")]; + tensor var_7668_cast = gelu(mode = var_7668_mode_0, x = var_7666_cast_1)[name = tensor("op_7668_cast")]; + tensor input_469_cast = mul(x = var_7666_cast_0, y = var_7668_cast)[name = tensor("input_469_cast")]; + tensor var_7672 = const()[name = tensor("op_7672"), val = tensor([1, 1])]; + tensor var_7674 = const()[name = tensor("op_7674"), val = tensor([1, 1])]; + tensor var_7676_pad_type_0 = const()[name = tensor("op_7676_pad_type_0"), val = tensor("custom")]; + tensor var_7676_pad_0 = const()[name = tensor("op_7676_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2853363328)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2866470592)))]; + tensor var_7676_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_7674, groups = var_6865, pad = var_7676_pad_0, pad_type = var_7676_pad_type_0, strides = var_7672, weight = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_469_cast)[name = tensor("op_7676_cast")]; + tensor inputs_229_cast = add(x = var_7676_cast, y = inputs_227_cast)[name = tensor("inputs_229_cast")]; + tensor var_7686 = const()[name = tensor("op_7686"), val = tensor([1])]; + tensor channels_mean_229_cast = reduce_mean(axes = var_7686, keep_dims = var_6860, x = inputs_229_cast)[name = tensor("channels_mean_229_cast")]; + tensor zero_mean_229_cast = sub(x = inputs_229_cast, y = channels_mean_229_cast)[name = tensor("zero_mean_229_cast")]; + tensor zero_mean_sq_229_cast = mul(x = zero_mean_229_cast, y = zero_mean_229_cast)[name = tensor("zero_mean_sq_229_cast")]; + tensor var_7690 = const()[name = tensor("op_7690"), val = tensor([1])]; + tensor var_7691_cast = reduce_mean(axes = var_7690, keep_dims = var_6860, x = zero_mean_sq_229_cast)[name = tensor("op_7691_cast")]; + tensor var_7692_to_fp16 = const()[name = tensor("op_7692_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7693_cast = add(x = var_7691_cast, y = var_7692_to_fp16)[name = tensor("op_7693_cast")]; + tensor denom_229_epsilon_0_to_fp16 = const()[name = tensor("denom_229_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_229_cast = rsqrt(epsilon = denom_229_epsilon_0_to_fp16, x = var_7693_cast)[name = tensor("denom_229_cast")]; + tensor out_229_cast = mul(x = zero_mean_229_cast, y = denom_229_cast)[name = tensor("out_229_cast")]; + tensor var_7697_to_fp16 = const()[name = tensor("op_7697_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2866473216)))]; + tensor var_7698_cast = add(x = out_229_cast, y = var_7697_to_fp16)[name = tensor("op_7698_cast")]; + tensor var_7700_to_fp16 = const()[name = tensor("op_7700_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2866475840)))]; + tensor hidden_states_311_cast = mul(x = var_7698_cast, y = var_7700_to_fp16)[name = tensor("hidden_states_311_cast")]; + tensor var_7707 = const()[name = tensor("op_7707"), val = tensor([1, 1])]; + tensor var_7709 = const()[name = tensor("op_7709"), val = tensor([1, 1])]; + tensor q_153_pad_type_0 = const()[name = tensor("q_153_pad_type_0"), val = tensor("custom")]; + tensor q_153_pad_0 = const()[name = tensor("q_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2866478464)))]; + tensor q_153_cast = conv(dilations = var_7709, groups = var_6865, pad = q_153_pad_0, pad_type = q_153_pad_type_0, strides = var_7707, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_311_cast)[name = tensor("q_153_cast")]; + tensor var_7713 = const()[name = tensor("op_7713"), val = tensor([1, 1])]; + tensor var_7715 = const()[name = tensor("op_7715"), val = tensor([1, 1])]; + tensor k_153_pad_type_0 = const()[name = tensor("k_153_pad_type_0"), val = tensor("custom")]; + tensor k_153_pad_0 = const()[name = tensor("k_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2869755328)))]; + tensor k_153_cast = conv(dilations = var_7715, groups = var_6865, pad = k_153_pad_0, pad_type = k_153_pad_type_0, strides = var_7713, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_311_cast)[name = tensor("k_153_cast")]; + tensor var_7719 = const()[name = tensor("op_7719"), val = tensor([1, 1])]; + tensor var_7721 = const()[name = tensor("op_7721"), val = tensor([1, 1])]; + tensor v_153_pad_type_0 = const()[name = tensor("v_153_pad_type_0"), val = tensor("custom")]; + tensor v_153_pad_0 = const()[name = tensor("v_153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2873032192)))]; + tensor v_153_cast = conv(dilations = var_7721, groups = var_6865, pad = v_153_pad_0, pad_type = v_153_pad_type_0, strides = var_7719, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_311_cast)[name = tensor("v_153_cast")]; + tensor var_7725 = const()[name = tensor("op_7725"), val = tensor([2, 20, 64, -1])]; + tensor var_7726_cast = reshape(shape = var_7725, x = q_153_cast)[name = tensor("op_7726_cast")]; + tensor var_7727 = const()[name = tensor("op_7727"), val = tensor([2, 20, 64, -1])]; + tensor var_7728_cast = reshape(shape = var_7727, x = k_153_cast)[name = tensor("op_7728_cast")]; + tensor var_7729 = const()[name = tensor("op_7729"), val = tensor([2, 20, 64, -1])]; + tensor var_7730_cast = reshape(shape = var_7729, x = v_153_cast)[name = tensor("op_7730_cast")]; + tensor attn_weights_305_transpose_x_0 = const()[name = tensor("attn_weights_305_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_305_transpose_y_0 = const()[name = tensor("attn_weights_305_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_305_cast = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7726_cast, y = var_7728_cast)[name = tensor("attn_weights_305_cast")]; + tensor attn_weights_307_cast = mul(x = attn_weights_305_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_307_cast")]; + tensor var_7734_cast = softmax(axis = var_6849, x = attn_weights_307_cast)[name = tensor("op_7734_cast")]; + tensor attn_153_transpose_x_0 = const()[name = tensor("attn_153_transpose_x_0"), val = tensor(false)]; + tensor attn_153_transpose_y_0 = const()[name = tensor("attn_153_transpose_y_0"), val = tensor(true)]; + tensor attn_153_cast = matmul(transpose_x = attn_153_transpose_x_0, transpose_y = attn_153_transpose_y_0, x = var_7730_cast, y = var_7734_cast)[name = tensor("attn_153_cast")]; + tensor var_7738 = const()[name = tensor("op_7738"), val = tensor([2, 1280, 1, -1])]; + tensor input_471_cast = reshape(shape = var_7738, x = attn_153_cast)[name = tensor("input_471_cast")]; + tensor var_7743 = const()[name = tensor("op_7743"), val = tensor([1, 1])]; + tensor var_7745 = const()[name = tensor("op_7745"), val = tensor([1, 1])]; + tensor var_7747_pad_type_0 = const()[name = tensor("op_7747_pad_type_0"), val = tensor("custom")]; + tensor var_7747_pad_0 = const()[name = tensor("op_7747_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2876309056)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2879585920)))]; + tensor var_7747_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_7745, groups = var_6865, pad = var_7747_pad_0, pad_type = var_7747_pad_type_0, strides = var_7743, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_471_cast)[name = tensor("op_7747_cast")]; + tensor inputs_231_cast = add(x = var_7747_cast, y = inputs_229_cast)[name = tensor("inputs_231_cast")]; + tensor var_7751 = const()[name = tensor("op_7751"), val = tensor([1])]; + tensor channels_mean_231_cast = reduce_mean(axes = var_7751, keep_dims = var_6860, x = inputs_231_cast)[name = tensor("channels_mean_231_cast")]; + tensor zero_mean_231_cast = sub(x = inputs_231_cast, y = channels_mean_231_cast)[name = tensor("zero_mean_231_cast")]; + tensor zero_mean_sq_231_cast = mul(x = zero_mean_231_cast, y = zero_mean_231_cast)[name = tensor("zero_mean_sq_231_cast")]; + tensor var_7755 = const()[name = tensor("op_7755"), val = tensor([1])]; + tensor var_7756_cast = reduce_mean(axes = var_7755, keep_dims = var_6860, x = zero_mean_sq_231_cast)[name = tensor("op_7756_cast")]; + tensor var_7757_to_fp16 = const()[name = tensor("op_7757_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7758_cast = add(x = var_7756_cast, y = var_7757_to_fp16)[name = tensor("op_7758_cast")]; + tensor denom_231_epsilon_0_to_fp16 = const()[name = tensor("denom_231_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_231_cast = rsqrt(epsilon = denom_231_epsilon_0_to_fp16, x = var_7758_cast)[name = tensor("denom_231_cast")]; + tensor out_231_cast = mul(x = zero_mean_231_cast, y = denom_231_cast)[name = tensor("out_231_cast")]; + tensor var_7762_to_fp16 = const()[name = tensor("op_7762_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2879588544)))]; + tensor var_7763_cast = add(x = out_231_cast, y = var_7762_to_fp16)[name = tensor("op_7763_cast")]; + tensor var_7765_to_fp16 = const()[name = tensor("op_7765_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2879591168)))]; + tensor hidden_states_313_cast = mul(x = var_7763_cast, y = var_7765_to_fp16)[name = tensor("hidden_states_313_cast")]; + tensor var_7772 = const()[name = tensor("op_7772"), val = tensor([1, 1])]; + tensor var_7774 = const()[name = tensor("op_7774"), val = tensor([1, 1])]; + tensor q_155_pad_type_0 = const()[name = tensor("q_155_pad_type_0"), val = tensor("custom")]; + tensor q_155_pad_0 = const()[name = tensor("q_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2879593792)))]; + tensor q_155_cast = conv(dilations = var_7774, groups = var_6865, pad = q_155_pad_0, pad_type = q_155_pad_type_0, strides = var_7772, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_313_cast)[name = tensor("q_155_cast")]; + tensor var_7778 = const()[name = tensor("op_7778"), val = tensor([1, 1])]; + tensor var_7780 = const()[name = tensor("op_7780"), val = tensor([1, 1])]; + tensor k_155_pad_type_0 = const()[name = tensor("k_155_pad_type_0"), val = tensor("custom")]; + tensor k_155_pad_0 = const()[name = tensor("k_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2882870656)))]; + tensor k_155_cast = conv(dilations = var_7780, groups = var_6865, pad = k_155_pad_0, pad_type = k_155_pad_type_0, strides = var_7778, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_155_cast")]; + tensor var_7784 = const()[name = tensor("op_7784"), val = tensor([1, 1])]; + tensor var_7786 = const()[name = tensor("op_7786"), val = tensor([1, 1])]; + tensor v_155_pad_type_0 = const()[name = tensor("v_155_pad_type_0"), val = tensor("custom")]; + tensor v_155_pad_0 = const()[name = tensor("v_155_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2888113600)))]; + tensor v_155_cast = conv(dilations = var_7786, groups = var_6865, pad = v_155_pad_0, pad_type = v_155_pad_type_0, strides = var_7784, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_155_cast")]; + tensor var_7790 = const()[name = tensor("op_7790"), val = tensor([2, 20, 64, -1])]; + tensor var_7791_cast = reshape(shape = var_7790, x = q_155_cast)[name = tensor("op_7791_cast")]; + tensor var_7792 = const()[name = tensor("op_7792"), val = tensor([2, 20, 64, -1])]; + tensor var_7793_cast = reshape(shape = var_7792, x = k_155_cast)[name = tensor("op_7793_cast")]; + tensor var_7794 = const()[name = tensor("op_7794"), val = tensor([2, 20, 64, -1])]; + tensor var_7795_cast = reshape(shape = var_7794, x = v_155_cast)[name = tensor("op_7795_cast")]; + tensor attn_weights_309_transpose_x_0 = const()[name = tensor("attn_weights_309_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_309_transpose_y_0 = const()[name = tensor("attn_weights_309_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_309_cast = matmul(transpose_x = attn_weights_309_transpose_x_0, transpose_y = attn_weights_309_transpose_y_0, x = var_7791_cast, y = var_7793_cast)[name = tensor("attn_weights_309_cast")]; + tensor attn_weights_311_cast = mul(x = attn_weights_309_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_311_cast")]; + tensor var_7799_cast = softmax(axis = var_6849, x = attn_weights_311_cast)[name = tensor("op_7799_cast")]; + tensor attn_155_transpose_x_0 = const()[name = tensor("attn_155_transpose_x_0"), val = tensor(false)]; + tensor attn_155_transpose_y_0 = const()[name = tensor("attn_155_transpose_y_0"), val = tensor(true)]; + tensor attn_155_cast = matmul(transpose_x = attn_155_transpose_x_0, transpose_y = attn_155_transpose_y_0, x = var_7795_cast, y = var_7799_cast)[name = tensor("attn_155_cast")]; + tensor var_7803 = const()[name = tensor("op_7803"), val = tensor([2, 1280, 1, -1])]; + tensor input_473_cast = reshape(shape = var_7803, x = attn_155_cast)[name = tensor("input_473_cast")]; + tensor var_7808 = const()[name = tensor("op_7808"), val = tensor([1, 1])]; + tensor var_7810 = const()[name = tensor("op_7810"), val = tensor([1, 1])]; + tensor var_7812_pad_type_0 = const()[name = tensor("op_7812_pad_type_0"), val = tensor("custom")]; + tensor var_7812_pad_0 = const()[name = tensor("op_7812_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2893356544)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2896633408)))]; + tensor var_7812_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_7810, groups = var_6865, pad = var_7812_pad_0, pad_type = var_7812_pad_type_0, strides = var_7808, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_473_cast)[name = tensor("op_7812_cast")]; + tensor inputs_233_cast = add(x = var_7812_cast, y = inputs_231_cast)[name = tensor("inputs_233_cast")]; + tensor var_7816 = const()[name = tensor("op_7816"), val = tensor([1])]; + tensor channels_mean_233_cast = reduce_mean(axes = var_7816, keep_dims = var_6860, x = inputs_233_cast)[name = tensor("channels_mean_233_cast")]; + tensor zero_mean_233_cast = sub(x = inputs_233_cast, y = channels_mean_233_cast)[name = tensor("zero_mean_233_cast")]; + tensor zero_mean_sq_233_cast = mul(x = zero_mean_233_cast, y = zero_mean_233_cast)[name = tensor("zero_mean_sq_233_cast")]; + tensor var_7820 = const()[name = tensor("op_7820"), val = tensor([1])]; + tensor var_7821_cast = reduce_mean(axes = var_7820, keep_dims = var_6860, x = zero_mean_sq_233_cast)[name = tensor("op_7821_cast")]; + tensor var_7822_to_fp16 = const()[name = tensor("op_7822_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7823_cast = add(x = var_7821_cast, y = var_7822_to_fp16)[name = tensor("op_7823_cast")]; + tensor denom_233_epsilon_0_to_fp16 = const()[name = tensor("denom_233_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_233_cast = rsqrt(epsilon = denom_233_epsilon_0_to_fp16, x = var_7823_cast)[name = tensor("denom_233_cast")]; + tensor out_233_cast = mul(x = zero_mean_233_cast, y = denom_233_cast)[name = tensor("out_233_cast")]; + tensor var_7827_to_fp16 = const()[name = tensor("op_7827_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2896636032)))]; + tensor var_7828_cast = add(x = out_233_cast, y = var_7827_to_fp16)[name = tensor("op_7828_cast")]; + tensor var_7830_to_fp16 = const()[name = tensor("op_7830_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2896638656)))]; + tensor input_475_cast = mul(x = var_7828_cast, y = var_7830_to_fp16)[name = tensor("input_475_cast")]; + tensor var_7838 = const()[name = tensor("op_7838"), val = tensor([1, 1])]; + tensor var_7840 = const()[name = tensor("op_7840"), val = tensor([1, 1])]; + tensor var_7842_pad_type_0 = const()[name = tensor("op_7842_pad_type_0"), val = tensor("custom")]; + tensor var_7842_pad_0 = const()[name = tensor("op_7842_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2896641280)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2922855744)))]; + tensor var_7842_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_7840, groups = var_6865, pad = var_7842_pad_0, pad_type = var_7842_pad_type_0, strides = var_7838, weight = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_475_cast)[name = tensor("op_7842_cast")]; + tensor var_7843_split_sizes_0 = const()[name = tensor("op_7843_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7843_axis_0 = const()[name = tensor("op_7843_axis_0"), val = tensor(1)]; + tensor var_7843_cast_0, tensor var_7843_cast_1 = split(axis = var_7843_axis_0, split_sizes = var_7843_split_sizes_0, x = var_7842_cast)[name = tensor("op_7843_cast")]; + tensor var_7845_mode_0 = const()[name = tensor("op_7845_mode_0"), val = tensor("EXACT")]; + tensor var_7845_cast = gelu(mode = var_7845_mode_0, x = var_7843_cast_1)[name = tensor("op_7845_cast")]; + tensor input_477_cast = mul(x = var_7843_cast_0, y = var_7845_cast)[name = tensor("input_477_cast")]; + tensor var_7849 = const()[name = tensor("op_7849"), val = tensor([1, 1])]; + tensor var_7851 = const()[name = tensor("op_7851"), val = tensor([1, 1])]; + tensor var_7853_pad_type_0 = const()[name = tensor("op_7853_pad_type_0"), val = tensor("custom")]; + tensor var_7853_pad_0 = const()[name = tensor("op_7853_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2922876288)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2935983552)))]; + tensor var_7853_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_7851, groups = var_6865, pad = var_7853_pad_0, pad_type = var_7853_pad_type_0, strides = var_7849, weight = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_477_cast)[name = tensor("op_7853_cast")]; + tensor inputs_235_cast = add(x = var_7853_cast, y = inputs_233_cast)[name = tensor("inputs_235_cast")]; + tensor var_7863 = const()[name = tensor("op_7863"), val = tensor([1])]; + tensor channels_mean_235_cast = reduce_mean(axes = var_7863, keep_dims = var_6860, x = inputs_235_cast)[name = tensor("channels_mean_235_cast")]; + tensor zero_mean_235_cast = sub(x = inputs_235_cast, y = channels_mean_235_cast)[name = tensor("zero_mean_235_cast")]; + tensor zero_mean_sq_235_cast = mul(x = zero_mean_235_cast, y = zero_mean_235_cast)[name = tensor("zero_mean_sq_235_cast")]; + tensor var_7867 = const()[name = tensor("op_7867"), val = tensor([1])]; + tensor var_7868_cast = reduce_mean(axes = var_7867, keep_dims = var_6860, x = zero_mean_sq_235_cast)[name = tensor("op_7868_cast")]; + tensor var_7869_to_fp16 = const()[name = tensor("op_7869_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7870_cast = add(x = var_7868_cast, y = var_7869_to_fp16)[name = tensor("op_7870_cast")]; + tensor denom_235_epsilon_0_to_fp16 = const()[name = tensor("denom_235_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_235_cast = rsqrt(epsilon = denom_235_epsilon_0_to_fp16, x = var_7870_cast)[name = tensor("denom_235_cast")]; + tensor out_235_cast = mul(x = zero_mean_235_cast, y = denom_235_cast)[name = tensor("out_235_cast")]; + tensor var_7874_to_fp16 = const()[name = tensor("op_7874_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2935986176)))]; + tensor var_7875_cast = add(x = out_235_cast, y = var_7874_to_fp16)[name = tensor("op_7875_cast")]; + tensor var_7877_to_fp16 = const()[name = tensor("op_7877_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2935988800)))]; + tensor hidden_states_317_cast = mul(x = var_7875_cast, y = var_7877_to_fp16)[name = tensor("hidden_states_317_cast")]; + tensor var_7884 = const()[name = tensor("op_7884"), val = tensor([1, 1])]; + tensor var_7886 = const()[name = tensor("op_7886"), val = tensor([1, 1])]; + tensor q_157_pad_type_0 = const()[name = tensor("q_157_pad_type_0"), val = tensor("custom")]; + tensor q_157_pad_0 = const()[name = tensor("q_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2935991424)))]; + tensor q_157_cast = conv(dilations = var_7886, groups = var_6865, pad = q_157_pad_0, pad_type = q_157_pad_type_0, strides = var_7884, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_317_cast)[name = tensor("q_157_cast")]; + tensor var_7890 = const()[name = tensor("op_7890"), val = tensor([1, 1])]; + tensor var_7892 = const()[name = tensor("op_7892"), val = tensor([1, 1])]; + tensor k_157_pad_type_0 = const()[name = tensor("k_157_pad_type_0"), val = tensor("custom")]; + tensor k_157_pad_0 = const()[name = tensor("k_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2939268288)))]; + tensor k_157_cast = conv(dilations = var_7892, groups = var_6865, pad = k_157_pad_0, pad_type = k_157_pad_type_0, strides = var_7890, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_317_cast)[name = tensor("k_157_cast")]; + tensor var_7896 = const()[name = tensor("op_7896"), val = tensor([1, 1])]; + tensor var_7898 = const()[name = tensor("op_7898"), val = tensor([1, 1])]; + tensor v_157_pad_type_0 = const()[name = tensor("v_157_pad_type_0"), val = tensor("custom")]; + tensor v_157_pad_0 = const()[name = tensor("v_157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2942545152)))]; + tensor v_157_cast = conv(dilations = var_7898, groups = var_6865, pad = v_157_pad_0, pad_type = v_157_pad_type_0, strides = var_7896, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_317_cast)[name = tensor("v_157_cast")]; + tensor var_7902 = const()[name = tensor("op_7902"), val = tensor([2, 20, 64, -1])]; + tensor var_7903_cast = reshape(shape = var_7902, x = q_157_cast)[name = tensor("op_7903_cast")]; + tensor var_7904 = const()[name = tensor("op_7904"), val = tensor([2, 20, 64, -1])]; + tensor var_7905_cast = reshape(shape = var_7904, x = k_157_cast)[name = tensor("op_7905_cast")]; + tensor var_7906 = const()[name = tensor("op_7906"), val = tensor([2, 20, 64, -1])]; + tensor var_7907_cast = reshape(shape = var_7906, x = v_157_cast)[name = tensor("op_7907_cast")]; + tensor attn_weights_313_transpose_x_0 = const()[name = tensor("attn_weights_313_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_313_transpose_y_0 = const()[name = tensor("attn_weights_313_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_313_cast = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7903_cast, y = var_7905_cast)[name = tensor("attn_weights_313_cast")]; + tensor attn_weights_315_cast = mul(x = attn_weights_313_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_315_cast")]; + tensor var_7911_cast = softmax(axis = var_6849, x = attn_weights_315_cast)[name = tensor("op_7911_cast")]; + tensor attn_157_transpose_x_0 = const()[name = tensor("attn_157_transpose_x_0"), val = tensor(false)]; + tensor attn_157_transpose_y_0 = const()[name = tensor("attn_157_transpose_y_0"), val = tensor(true)]; + tensor attn_157_cast = matmul(transpose_x = attn_157_transpose_x_0, transpose_y = attn_157_transpose_y_0, x = var_7907_cast, y = var_7911_cast)[name = tensor("attn_157_cast")]; + tensor var_7915 = const()[name = tensor("op_7915"), val = tensor([2, 1280, 1, -1])]; + tensor input_479_cast = reshape(shape = var_7915, x = attn_157_cast)[name = tensor("input_479_cast")]; + tensor var_7920 = const()[name = tensor("op_7920"), val = tensor([1, 1])]; + tensor var_7922 = const()[name = tensor("op_7922"), val = tensor([1, 1])]; + tensor var_7924_pad_type_0 = const()[name = tensor("op_7924_pad_type_0"), val = tensor("custom")]; + tensor var_7924_pad_0 = const()[name = tensor("op_7924_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2945822016)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2949098880)))]; + tensor var_7924_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_7922, groups = var_6865, pad = var_7924_pad_0, pad_type = var_7924_pad_type_0, strides = var_7920, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_479_cast)[name = tensor("op_7924_cast")]; + tensor inputs_237_cast = add(x = var_7924_cast, y = inputs_235_cast)[name = tensor("inputs_237_cast")]; + tensor var_7928 = const()[name = tensor("op_7928"), val = tensor([1])]; + tensor channels_mean_237_cast = reduce_mean(axes = var_7928, keep_dims = var_6860, x = inputs_237_cast)[name = tensor("channels_mean_237_cast")]; + tensor zero_mean_237_cast = sub(x = inputs_237_cast, y = channels_mean_237_cast)[name = tensor("zero_mean_237_cast")]; + tensor zero_mean_sq_237_cast = mul(x = zero_mean_237_cast, y = zero_mean_237_cast)[name = tensor("zero_mean_sq_237_cast")]; + tensor var_7932 = const()[name = tensor("op_7932"), val = tensor([1])]; + tensor var_7933_cast = reduce_mean(axes = var_7932, keep_dims = var_6860, x = zero_mean_sq_237_cast)[name = tensor("op_7933_cast")]; + tensor var_7934_to_fp16 = const()[name = tensor("op_7934_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7935_cast = add(x = var_7933_cast, y = var_7934_to_fp16)[name = tensor("op_7935_cast")]; + tensor denom_237_epsilon_0_to_fp16 = const()[name = tensor("denom_237_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_237_cast = rsqrt(epsilon = denom_237_epsilon_0_to_fp16, x = var_7935_cast)[name = tensor("denom_237_cast")]; + tensor out_237_cast = mul(x = zero_mean_237_cast, y = denom_237_cast)[name = tensor("out_237_cast")]; + tensor var_7939_to_fp16 = const()[name = tensor("op_7939_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2949101504)))]; + tensor var_7940_cast = add(x = out_237_cast, y = var_7939_to_fp16)[name = tensor("op_7940_cast")]; + tensor var_7942_to_fp16 = const()[name = tensor("op_7942_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2949104128)))]; + tensor hidden_states_319_cast = mul(x = var_7940_cast, y = var_7942_to_fp16)[name = tensor("hidden_states_319_cast")]; + tensor var_7949 = const()[name = tensor("op_7949"), val = tensor([1, 1])]; + tensor var_7951 = const()[name = tensor("op_7951"), val = tensor([1, 1])]; + tensor q_159_pad_type_0 = const()[name = tensor("q_159_pad_type_0"), val = tensor("custom")]; + tensor q_159_pad_0 = const()[name = tensor("q_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2949106752)))]; + tensor q_159_cast = conv(dilations = var_7951, groups = var_6865, pad = q_159_pad_0, pad_type = q_159_pad_type_0, strides = var_7949, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_319_cast)[name = tensor("q_159_cast")]; + tensor var_7955 = const()[name = tensor("op_7955"), val = tensor([1, 1])]; + tensor var_7957 = const()[name = tensor("op_7957"), val = tensor([1, 1])]; + tensor k_159_pad_type_0 = const()[name = tensor("k_159_pad_type_0"), val = tensor("custom")]; + tensor k_159_pad_0 = const()[name = tensor("k_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2952383616)))]; + tensor k_159_cast = conv(dilations = var_7957, groups = var_6865, pad = k_159_pad_0, pad_type = k_159_pad_type_0, strides = var_7955, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_159_cast")]; + tensor var_7961 = const()[name = tensor("op_7961"), val = tensor([1, 1])]; + tensor var_7963 = const()[name = tensor("op_7963"), val = tensor([1, 1])]; + tensor v_159_pad_type_0 = const()[name = tensor("v_159_pad_type_0"), val = tensor("custom")]; + tensor v_159_pad_0 = const()[name = tensor("v_159_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2957626560)))]; + tensor v_159_cast = conv(dilations = var_7963, groups = var_6865, pad = v_159_pad_0, pad_type = v_159_pad_type_0, strides = var_7961, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_159_cast")]; + tensor var_7967 = const()[name = tensor("op_7967"), val = tensor([2, 20, 64, -1])]; + tensor var_7968_cast = reshape(shape = var_7967, x = q_159_cast)[name = tensor("op_7968_cast")]; + tensor var_7969 = const()[name = tensor("op_7969"), val = tensor([2, 20, 64, -1])]; + tensor var_7970_cast = reshape(shape = var_7969, x = k_159_cast)[name = tensor("op_7970_cast")]; + tensor var_7971 = const()[name = tensor("op_7971"), val = tensor([2, 20, 64, -1])]; + tensor var_7972_cast = reshape(shape = var_7971, x = v_159_cast)[name = tensor("op_7972_cast")]; + tensor attn_weights_317_transpose_x_0 = const()[name = tensor("attn_weights_317_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_317_transpose_y_0 = const()[name = tensor("attn_weights_317_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_317_cast = matmul(transpose_x = attn_weights_317_transpose_x_0, transpose_y = attn_weights_317_transpose_y_0, x = var_7968_cast, y = var_7970_cast)[name = tensor("attn_weights_317_cast")]; + tensor attn_weights_319_cast = mul(x = attn_weights_317_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_319_cast")]; + tensor var_7976_cast = softmax(axis = var_6849, x = attn_weights_319_cast)[name = tensor("op_7976_cast")]; + tensor attn_159_transpose_x_0 = const()[name = tensor("attn_159_transpose_x_0"), val = tensor(false)]; + tensor attn_159_transpose_y_0 = const()[name = tensor("attn_159_transpose_y_0"), val = tensor(true)]; + tensor attn_159_cast = matmul(transpose_x = attn_159_transpose_x_0, transpose_y = attn_159_transpose_y_0, x = var_7972_cast, y = var_7976_cast)[name = tensor("attn_159_cast")]; + tensor var_7980 = const()[name = tensor("op_7980"), val = tensor([2, 1280, 1, -1])]; + tensor input_481_cast = reshape(shape = var_7980, x = attn_159_cast)[name = tensor("input_481_cast")]; + tensor var_7985 = const()[name = tensor("op_7985"), val = tensor([1, 1])]; + tensor var_7987 = const()[name = tensor("op_7987"), val = tensor([1, 1])]; + tensor var_7989_pad_type_0 = const()[name = tensor("op_7989_pad_type_0"), val = tensor("custom")]; + tensor var_7989_pad_0 = const()[name = tensor("op_7989_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2962869504)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2966146368)))]; + tensor var_7989_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_7987, groups = var_6865, pad = var_7989_pad_0, pad_type = var_7989_pad_type_0, strides = var_7985, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_481_cast)[name = tensor("op_7989_cast")]; + tensor inputs_239_cast = add(x = var_7989_cast, y = inputs_237_cast)[name = tensor("inputs_239_cast")]; + tensor var_7993 = const()[name = tensor("op_7993"), val = tensor([1])]; + tensor channels_mean_239_cast = reduce_mean(axes = var_7993, keep_dims = var_6860, x = inputs_239_cast)[name = tensor("channels_mean_239_cast")]; + tensor zero_mean_239_cast = sub(x = inputs_239_cast, y = channels_mean_239_cast)[name = tensor("zero_mean_239_cast")]; + tensor zero_mean_sq_239_cast = mul(x = zero_mean_239_cast, y = zero_mean_239_cast)[name = tensor("zero_mean_sq_239_cast")]; + tensor var_7997 = const()[name = tensor("op_7997"), val = tensor([1])]; + tensor var_7998_cast = reduce_mean(axes = var_7997, keep_dims = var_6860, x = zero_mean_sq_239_cast)[name = tensor("op_7998_cast")]; + tensor var_7999_to_fp16 = const()[name = tensor("op_7999_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8000_cast = add(x = var_7998_cast, y = var_7999_to_fp16)[name = tensor("op_8000_cast")]; + tensor denom_239_epsilon_0_to_fp16 = const()[name = tensor("denom_239_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_239_cast = rsqrt(epsilon = denom_239_epsilon_0_to_fp16, x = var_8000_cast)[name = tensor("denom_239_cast")]; + tensor out_239_cast = mul(x = zero_mean_239_cast, y = denom_239_cast)[name = tensor("out_239_cast")]; + tensor var_8004_to_fp16 = const()[name = tensor("op_8004_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2966148992)))]; + tensor var_8005_cast = add(x = out_239_cast, y = var_8004_to_fp16)[name = tensor("op_8005_cast")]; + tensor var_8007_to_fp16 = const()[name = tensor("op_8007_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2966151616)))]; + tensor input_483_cast = mul(x = var_8005_cast, y = var_8007_to_fp16)[name = tensor("input_483_cast")]; + tensor var_8015 = const()[name = tensor("op_8015"), val = tensor([1, 1])]; + tensor var_8017 = const()[name = tensor("op_8017"), val = tensor([1, 1])]; + tensor var_8019_pad_type_0 = const()[name = tensor("op_8019_pad_type_0"), val = tensor("custom")]; + tensor var_8019_pad_0 = const()[name = tensor("op_8019_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2966154240)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2992368704)))]; + tensor var_8019_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_8017, groups = var_6865, pad = var_8019_pad_0, pad_type = var_8019_pad_type_0, strides = var_8015, weight = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_483_cast)[name = tensor("op_8019_cast")]; + tensor var_8020_split_sizes_0 = const()[name = tensor("op_8020_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8020_axis_0 = const()[name = tensor("op_8020_axis_0"), val = tensor(1)]; + tensor var_8020_cast_0, tensor var_8020_cast_1 = split(axis = var_8020_axis_0, split_sizes = var_8020_split_sizes_0, x = var_8019_cast)[name = tensor("op_8020_cast")]; + tensor var_8022_mode_0 = const()[name = tensor("op_8022_mode_0"), val = tensor("EXACT")]; + tensor var_8022_cast = gelu(mode = var_8022_mode_0, x = var_8020_cast_1)[name = tensor("op_8022_cast")]; + tensor input_485_cast = mul(x = var_8020_cast_0, y = var_8022_cast)[name = tensor("input_485_cast")]; + tensor var_8026 = const()[name = tensor("op_8026"), val = tensor([1, 1])]; + tensor var_8028 = const()[name = tensor("op_8028"), val = tensor([1, 1])]; + tensor var_8030_pad_type_0 = const()[name = tensor("op_8030_pad_type_0"), val = tensor("custom")]; + tensor var_8030_pad_0 = const()[name = tensor("op_8030_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2992389248)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3005496512)))]; + tensor var_8030_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_8028, groups = var_6865, pad = var_8030_pad_0, pad_type = var_8030_pad_type_0, strides = var_8026, weight = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_485_cast)[name = tensor("op_8030_cast")]; + tensor inputs_241_cast = add(x = var_8030_cast, y = inputs_239_cast)[name = tensor("inputs_241_cast")]; + tensor var_8040 = const()[name = tensor("op_8040"), val = tensor([1])]; + tensor channels_mean_241_cast = reduce_mean(axes = var_8040, keep_dims = var_6860, x = inputs_241_cast)[name = tensor("channels_mean_241_cast")]; + tensor zero_mean_241_cast = sub(x = inputs_241_cast, y = channels_mean_241_cast)[name = tensor("zero_mean_241_cast")]; + tensor zero_mean_sq_241_cast = mul(x = zero_mean_241_cast, y = zero_mean_241_cast)[name = tensor("zero_mean_sq_241_cast")]; + tensor var_8044 = const()[name = tensor("op_8044"), val = tensor([1])]; + tensor var_8045_cast = reduce_mean(axes = var_8044, keep_dims = var_6860, x = zero_mean_sq_241_cast)[name = tensor("op_8045_cast")]; + tensor var_8046_to_fp16 = const()[name = tensor("op_8046_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8047_cast = add(x = var_8045_cast, y = var_8046_to_fp16)[name = tensor("op_8047_cast")]; + tensor denom_241_epsilon_0_to_fp16 = const()[name = tensor("denom_241_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_241_cast = rsqrt(epsilon = denom_241_epsilon_0_to_fp16, x = var_8047_cast)[name = tensor("denom_241_cast")]; + tensor out_241_cast = mul(x = zero_mean_241_cast, y = denom_241_cast)[name = tensor("out_241_cast")]; + tensor var_8051_to_fp16 = const()[name = tensor("op_8051_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3005499136)))]; + tensor var_8052_cast = add(x = out_241_cast, y = var_8051_to_fp16)[name = tensor("op_8052_cast")]; + tensor var_8054_to_fp16 = const()[name = tensor("op_8054_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3005501760)))]; + tensor hidden_states_323_cast = mul(x = var_8052_cast, y = var_8054_to_fp16)[name = tensor("hidden_states_323_cast")]; + tensor var_8061 = const()[name = tensor("op_8061"), val = tensor([1, 1])]; + tensor var_8063 = const()[name = tensor("op_8063"), val = tensor([1, 1])]; + tensor q_161_pad_type_0 = const()[name = tensor("q_161_pad_type_0"), val = tensor("custom")]; + tensor q_161_pad_0 = const()[name = tensor("q_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3005504384)))]; + tensor q_161_cast = conv(dilations = var_8063, groups = var_6865, pad = q_161_pad_0, pad_type = q_161_pad_type_0, strides = var_8061, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_323_cast)[name = tensor("q_161_cast")]; + tensor var_8067 = const()[name = tensor("op_8067"), val = tensor([1, 1])]; + tensor var_8069 = const()[name = tensor("op_8069"), val = tensor([1, 1])]; + tensor k_161_pad_type_0 = const()[name = tensor("k_161_pad_type_0"), val = tensor("custom")]; + tensor k_161_pad_0 = const()[name = tensor("k_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3008781248)))]; + tensor k_161_cast = conv(dilations = var_8069, groups = var_6865, pad = k_161_pad_0, pad_type = k_161_pad_type_0, strides = var_8067, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_323_cast)[name = tensor("k_161_cast")]; + tensor var_8073 = const()[name = tensor("op_8073"), val = tensor([1, 1])]; + tensor var_8075 = const()[name = tensor("op_8075"), val = tensor([1, 1])]; + tensor v_161_pad_type_0 = const()[name = tensor("v_161_pad_type_0"), val = tensor("custom")]; + tensor v_161_pad_0 = const()[name = tensor("v_161_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3012058112)))]; + tensor v_161_cast = conv(dilations = var_8075, groups = var_6865, pad = v_161_pad_0, pad_type = v_161_pad_type_0, strides = var_8073, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_323_cast)[name = tensor("v_161_cast")]; + tensor var_8079 = const()[name = tensor("op_8079"), val = tensor([2, 20, 64, -1])]; + tensor var_8080_cast = reshape(shape = var_8079, x = q_161_cast)[name = tensor("op_8080_cast")]; + tensor var_8081 = const()[name = tensor("op_8081"), val = tensor([2, 20, 64, -1])]; + tensor var_8082_cast = reshape(shape = var_8081, x = k_161_cast)[name = tensor("op_8082_cast")]; + tensor var_8083 = const()[name = tensor("op_8083"), val = tensor([2, 20, 64, -1])]; + tensor var_8084_cast = reshape(shape = var_8083, x = v_161_cast)[name = tensor("op_8084_cast")]; + tensor attn_weights_321_transpose_x_0 = const()[name = tensor("attn_weights_321_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_321_transpose_y_0 = const()[name = tensor("attn_weights_321_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_321_cast = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_8080_cast, y = var_8082_cast)[name = tensor("attn_weights_321_cast")]; + tensor attn_weights_323_cast = mul(x = attn_weights_321_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_323_cast")]; + tensor var_8088_cast = softmax(axis = var_6849, x = attn_weights_323_cast)[name = tensor("op_8088_cast")]; + tensor attn_161_transpose_x_0 = const()[name = tensor("attn_161_transpose_x_0"), val = tensor(false)]; + tensor attn_161_transpose_y_0 = const()[name = tensor("attn_161_transpose_y_0"), val = tensor(true)]; + tensor attn_161_cast = matmul(transpose_x = attn_161_transpose_x_0, transpose_y = attn_161_transpose_y_0, x = var_8084_cast, y = var_8088_cast)[name = tensor("attn_161_cast")]; + tensor var_8092 = const()[name = tensor("op_8092"), val = tensor([2, 1280, 1, -1])]; + tensor input_487_cast = reshape(shape = var_8092, x = attn_161_cast)[name = tensor("input_487_cast")]; + tensor var_8097 = const()[name = tensor("op_8097"), val = tensor([1, 1])]; + tensor var_8099 = const()[name = tensor("op_8099"), val = tensor([1, 1])]; + tensor var_8101_pad_type_0 = const()[name = tensor("op_8101_pad_type_0"), val = tensor("custom")]; + tensor var_8101_pad_0 = const()[name = tensor("op_8101_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3015334976)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3018611840)))]; + tensor var_8101_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_8099, groups = var_6865, pad = var_8101_pad_0, pad_type = var_8101_pad_type_0, strides = var_8097, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_487_cast)[name = tensor("op_8101_cast")]; + tensor inputs_243_cast = add(x = var_8101_cast, y = inputs_241_cast)[name = tensor("inputs_243_cast")]; + tensor var_8105 = const()[name = tensor("op_8105"), val = tensor([1])]; + tensor channels_mean_243_cast = reduce_mean(axes = var_8105, keep_dims = var_6860, x = inputs_243_cast)[name = tensor("channels_mean_243_cast")]; + tensor zero_mean_243_cast = sub(x = inputs_243_cast, y = channels_mean_243_cast)[name = tensor("zero_mean_243_cast")]; + tensor zero_mean_sq_243_cast = mul(x = zero_mean_243_cast, y = zero_mean_243_cast)[name = tensor("zero_mean_sq_243_cast")]; + tensor var_8109 = const()[name = tensor("op_8109"), val = tensor([1])]; + tensor var_8110_cast = reduce_mean(axes = var_8109, keep_dims = var_6860, x = zero_mean_sq_243_cast)[name = tensor("op_8110_cast")]; + tensor var_8111_to_fp16 = const()[name = tensor("op_8111_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8112_cast = add(x = var_8110_cast, y = var_8111_to_fp16)[name = tensor("op_8112_cast")]; + tensor denom_243_epsilon_0_to_fp16 = const()[name = tensor("denom_243_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_243_cast = rsqrt(epsilon = denom_243_epsilon_0_to_fp16, x = var_8112_cast)[name = tensor("denom_243_cast")]; + tensor out_243_cast = mul(x = zero_mean_243_cast, y = denom_243_cast)[name = tensor("out_243_cast")]; + tensor var_8116_to_fp16 = const()[name = tensor("op_8116_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3018614464)))]; + tensor var_8117_cast = add(x = out_243_cast, y = var_8116_to_fp16)[name = tensor("op_8117_cast")]; + tensor var_8119_to_fp16 = const()[name = tensor("op_8119_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3018617088)))]; + tensor hidden_states_325_cast = mul(x = var_8117_cast, y = var_8119_to_fp16)[name = tensor("hidden_states_325_cast")]; + tensor var_8126 = const()[name = tensor("op_8126"), val = tensor([1, 1])]; + tensor var_8128 = const()[name = tensor("op_8128"), val = tensor([1, 1])]; + tensor q_163_pad_type_0 = const()[name = tensor("q_163_pad_type_0"), val = tensor("custom")]; + tensor q_163_pad_0 = const()[name = tensor("q_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3018619712)))]; + tensor q_163_cast = conv(dilations = var_8128, groups = var_6865, pad = q_163_pad_0, pad_type = q_163_pad_type_0, strides = var_8126, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_325_cast)[name = tensor("q_163_cast")]; + tensor var_8132 = const()[name = tensor("op_8132"), val = tensor([1, 1])]; + tensor var_8134 = const()[name = tensor("op_8134"), val = tensor([1, 1])]; + tensor k_163_pad_type_0 = const()[name = tensor("k_163_pad_type_0"), val = tensor("custom")]; + tensor k_163_pad_0 = const()[name = tensor("k_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3021896576)))]; + tensor k_163_cast = conv(dilations = var_8134, groups = var_6865, pad = k_163_pad_0, pad_type = k_163_pad_type_0, strides = var_8132, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_163_cast")]; + tensor var_8138 = const()[name = tensor("op_8138"), val = tensor([1, 1])]; + tensor var_8140 = const()[name = tensor("op_8140"), val = tensor([1, 1])]; + tensor v_163_pad_type_0 = const()[name = tensor("v_163_pad_type_0"), val = tensor("custom")]; + tensor v_163_pad_0 = const()[name = tensor("v_163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3027139520)))]; + tensor v_163_cast = conv(dilations = var_8140, groups = var_6865, pad = v_163_pad_0, pad_type = v_163_pad_type_0, strides = var_8138, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_163_cast")]; + tensor var_8144 = const()[name = tensor("op_8144"), val = tensor([2, 20, 64, -1])]; + tensor var_8145_cast = reshape(shape = var_8144, x = q_163_cast)[name = tensor("op_8145_cast")]; + tensor var_8146 = const()[name = tensor("op_8146"), val = tensor([2, 20, 64, -1])]; + tensor var_8147_cast = reshape(shape = var_8146, x = k_163_cast)[name = tensor("op_8147_cast")]; + tensor var_8148 = const()[name = tensor("op_8148"), val = tensor([2, 20, 64, -1])]; + tensor var_8149_cast = reshape(shape = var_8148, x = v_163_cast)[name = tensor("op_8149_cast")]; + tensor attn_weights_325_transpose_x_0 = const()[name = tensor("attn_weights_325_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_325_transpose_y_0 = const()[name = tensor("attn_weights_325_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_325_cast = matmul(transpose_x = attn_weights_325_transpose_x_0, transpose_y = attn_weights_325_transpose_y_0, x = var_8145_cast, y = var_8147_cast)[name = tensor("attn_weights_325_cast")]; + tensor attn_weights_327_cast = mul(x = attn_weights_325_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_327_cast")]; + tensor var_8153_cast = softmax(axis = var_6849, x = attn_weights_327_cast)[name = tensor("op_8153_cast")]; + tensor attn_163_transpose_x_0 = const()[name = tensor("attn_163_transpose_x_0"), val = tensor(false)]; + tensor attn_163_transpose_y_0 = const()[name = tensor("attn_163_transpose_y_0"), val = tensor(true)]; + tensor attn_163_cast = matmul(transpose_x = attn_163_transpose_x_0, transpose_y = attn_163_transpose_y_0, x = var_8149_cast, y = var_8153_cast)[name = tensor("attn_163_cast")]; + tensor var_8157 = const()[name = tensor("op_8157"), val = tensor([2, 1280, 1, -1])]; + tensor input_489_cast = reshape(shape = var_8157, x = attn_163_cast)[name = tensor("input_489_cast")]; + tensor var_8162 = const()[name = tensor("op_8162"), val = tensor([1, 1])]; + tensor var_8164 = const()[name = tensor("op_8164"), val = tensor([1, 1])]; + tensor var_8166_pad_type_0 = const()[name = tensor("op_8166_pad_type_0"), val = tensor("custom")]; + tensor var_8166_pad_0 = const()[name = tensor("op_8166_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3032382464)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3035659328)))]; + tensor var_8166_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_8164, groups = var_6865, pad = var_8166_pad_0, pad_type = var_8166_pad_type_0, strides = var_8162, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_489_cast)[name = tensor("op_8166_cast")]; + tensor inputs_245_cast = add(x = var_8166_cast, y = inputs_243_cast)[name = tensor("inputs_245_cast")]; + tensor var_8170 = const()[name = tensor("op_8170"), val = tensor([1])]; + tensor channels_mean_245_cast = reduce_mean(axes = var_8170, keep_dims = var_6860, x = inputs_245_cast)[name = tensor("channels_mean_245_cast")]; + tensor zero_mean_245_cast = sub(x = inputs_245_cast, y = channels_mean_245_cast)[name = tensor("zero_mean_245_cast")]; + tensor zero_mean_sq_245_cast = mul(x = zero_mean_245_cast, y = zero_mean_245_cast)[name = tensor("zero_mean_sq_245_cast")]; + tensor var_8174 = const()[name = tensor("op_8174"), val = tensor([1])]; + tensor var_8175_cast = reduce_mean(axes = var_8174, keep_dims = var_6860, x = zero_mean_sq_245_cast)[name = tensor("op_8175_cast")]; + tensor var_8176_to_fp16 = const()[name = tensor("op_8176_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8177_cast = add(x = var_8175_cast, y = var_8176_to_fp16)[name = tensor("op_8177_cast")]; + tensor denom_245_epsilon_0_to_fp16 = const()[name = tensor("denom_245_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_245_cast = rsqrt(epsilon = denom_245_epsilon_0_to_fp16, x = var_8177_cast)[name = tensor("denom_245_cast")]; + tensor out_245_cast = mul(x = zero_mean_245_cast, y = denom_245_cast)[name = tensor("out_245_cast")]; + tensor var_8181_to_fp16 = const()[name = tensor("op_8181_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3035661952)))]; + tensor var_8182_cast = add(x = out_245_cast, y = var_8181_to_fp16)[name = tensor("op_8182_cast")]; + tensor var_8184_to_fp16 = const()[name = tensor("op_8184_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3035664576)))]; + tensor input_491_cast = mul(x = var_8182_cast, y = var_8184_to_fp16)[name = tensor("input_491_cast")]; + tensor var_8192 = const()[name = tensor("op_8192"), val = tensor([1, 1])]; + tensor var_8194 = const()[name = tensor("op_8194"), val = tensor([1, 1])]; + tensor var_8196_pad_type_0 = const()[name = tensor("op_8196_pad_type_0"), val = tensor("custom")]; + tensor var_8196_pad_0 = const()[name = tensor("op_8196_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3035667200)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3061881664)))]; + tensor var_8196_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_8194, groups = var_6865, pad = var_8196_pad_0, pad_type = var_8196_pad_type_0, strides = var_8192, weight = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_491_cast)[name = tensor("op_8196_cast")]; + tensor var_8197_split_sizes_0 = const()[name = tensor("op_8197_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8197_axis_0 = const()[name = tensor("op_8197_axis_0"), val = tensor(1)]; + tensor var_8197_cast_0, tensor var_8197_cast_1 = split(axis = var_8197_axis_0, split_sizes = var_8197_split_sizes_0, x = var_8196_cast)[name = tensor("op_8197_cast")]; + tensor var_8199_mode_0 = const()[name = tensor("op_8199_mode_0"), val = tensor("EXACT")]; + tensor var_8199_cast = gelu(mode = var_8199_mode_0, x = var_8197_cast_1)[name = tensor("op_8199_cast")]; + tensor input_493_cast = mul(x = var_8197_cast_0, y = var_8199_cast)[name = tensor("input_493_cast")]; + tensor var_8203 = const()[name = tensor("op_8203"), val = tensor([1, 1])]; + tensor var_8205 = const()[name = tensor("op_8205"), val = tensor([1, 1])]; + tensor var_8207_pad_type_0 = const()[name = tensor("op_8207_pad_type_0"), val = tensor("custom")]; + tensor var_8207_pad_0 = const()[name = tensor("op_8207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3061902208)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3075009472)))]; + tensor var_8207_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_8205, groups = var_6865, pad = var_8207_pad_0, pad_type = var_8207_pad_type_0, strides = var_8203, weight = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_493_cast)[name = tensor("op_8207_cast")]; + tensor inputs_247_cast = add(x = var_8207_cast, y = inputs_245_cast)[name = tensor("inputs_247_cast")]; + tensor var_8217 = const()[name = tensor("op_8217"), val = tensor([1])]; + tensor channels_mean_247_cast = reduce_mean(axes = var_8217, keep_dims = var_6860, x = inputs_247_cast)[name = tensor("channels_mean_247_cast")]; + tensor zero_mean_247_cast = sub(x = inputs_247_cast, y = channels_mean_247_cast)[name = tensor("zero_mean_247_cast")]; + tensor zero_mean_sq_247_cast = mul(x = zero_mean_247_cast, y = zero_mean_247_cast)[name = tensor("zero_mean_sq_247_cast")]; + tensor var_8221 = const()[name = tensor("op_8221"), val = tensor([1])]; + tensor var_8222_cast = reduce_mean(axes = var_8221, keep_dims = var_6860, x = zero_mean_sq_247_cast)[name = tensor("op_8222_cast")]; + tensor var_8223_to_fp16 = const()[name = tensor("op_8223_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8224_cast = add(x = var_8222_cast, y = var_8223_to_fp16)[name = tensor("op_8224_cast")]; + tensor denom_247_epsilon_0_to_fp16 = const()[name = tensor("denom_247_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_247_cast = rsqrt(epsilon = denom_247_epsilon_0_to_fp16, x = var_8224_cast)[name = tensor("denom_247_cast")]; + tensor out_247_cast = mul(x = zero_mean_247_cast, y = denom_247_cast)[name = tensor("out_247_cast")]; + tensor var_8228_to_fp16 = const()[name = tensor("op_8228_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3075012096)))]; + tensor var_8229_cast = add(x = out_247_cast, y = var_8228_to_fp16)[name = tensor("op_8229_cast")]; + tensor var_8231_to_fp16 = const()[name = tensor("op_8231_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3075014720)))]; + tensor hidden_states_329_cast = mul(x = var_8229_cast, y = var_8231_to_fp16)[name = tensor("hidden_states_329_cast")]; + tensor var_8238 = const()[name = tensor("op_8238"), val = tensor([1, 1])]; + tensor var_8240 = const()[name = tensor("op_8240"), val = tensor([1, 1])]; + tensor q_165_pad_type_0 = const()[name = tensor("q_165_pad_type_0"), val = tensor("custom")]; + tensor q_165_pad_0 = const()[name = tensor("q_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3075017344)))]; + tensor q_165_cast = conv(dilations = var_8240, groups = var_6865, pad = q_165_pad_0, pad_type = q_165_pad_type_0, strides = var_8238, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_329_cast)[name = tensor("q_165_cast")]; + tensor var_8244 = const()[name = tensor("op_8244"), val = tensor([1, 1])]; + tensor var_8246 = const()[name = tensor("op_8246"), val = tensor([1, 1])]; + tensor k_165_pad_type_0 = const()[name = tensor("k_165_pad_type_0"), val = tensor("custom")]; + tensor k_165_pad_0 = const()[name = tensor("k_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3078294208)))]; + tensor k_165_cast = conv(dilations = var_8246, groups = var_6865, pad = k_165_pad_0, pad_type = k_165_pad_type_0, strides = var_8244, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_329_cast)[name = tensor("k_165_cast")]; + tensor var_8250 = const()[name = tensor("op_8250"), val = tensor([1, 1])]; + tensor var_8252 = const()[name = tensor("op_8252"), val = tensor([1, 1])]; + tensor v_165_pad_type_0 = const()[name = tensor("v_165_pad_type_0"), val = tensor("custom")]; + tensor v_165_pad_0 = const()[name = tensor("v_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3081571072)))]; + tensor v_165_cast = conv(dilations = var_8252, groups = var_6865, pad = v_165_pad_0, pad_type = v_165_pad_type_0, strides = var_8250, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_329_cast)[name = tensor("v_165_cast")]; + tensor var_8256 = const()[name = tensor("op_8256"), val = tensor([2, 20, 64, -1])]; + tensor var_8257_cast = reshape(shape = var_8256, x = q_165_cast)[name = tensor("op_8257_cast")]; + tensor var_8258 = const()[name = tensor("op_8258"), val = tensor([2, 20, 64, -1])]; + tensor var_8259_cast = reshape(shape = var_8258, x = k_165_cast)[name = tensor("op_8259_cast")]; + tensor var_8260 = const()[name = tensor("op_8260"), val = tensor([2, 20, 64, -1])]; + tensor var_8261_cast = reshape(shape = var_8260, x = v_165_cast)[name = tensor("op_8261_cast")]; + tensor attn_weights_329_transpose_x_0 = const()[name = tensor("attn_weights_329_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_329_transpose_y_0 = const()[name = tensor("attn_weights_329_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_329_cast = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_8257_cast, y = var_8259_cast)[name = tensor("attn_weights_329_cast")]; + tensor attn_weights_331_cast = mul(x = attn_weights_329_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_331_cast")]; + tensor var_8265_cast = softmax(axis = var_6849, x = attn_weights_331_cast)[name = tensor("op_8265_cast")]; + tensor attn_165_transpose_x_0 = const()[name = tensor("attn_165_transpose_x_0"), val = tensor(false)]; + tensor attn_165_transpose_y_0 = const()[name = tensor("attn_165_transpose_y_0"), val = tensor(true)]; + tensor attn_165_cast = matmul(transpose_x = attn_165_transpose_x_0, transpose_y = attn_165_transpose_y_0, x = var_8261_cast, y = var_8265_cast)[name = tensor("attn_165_cast")]; + tensor var_8269 = const()[name = tensor("op_8269"), val = tensor([2, 1280, 1, -1])]; + tensor input_495_cast = reshape(shape = var_8269, x = attn_165_cast)[name = tensor("input_495_cast")]; + tensor var_8274 = const()[name = tensor("op_8274"), val = tensor([1, 1])]; + tensor var_8276 = const()[name = tensor("op_8276"), val = tensor([1, 1])]; + tensor var_8278_pad_type_0 = const()[name = tensor("op_8278_pad_type_0"), val = tensor("custom")]; + tensor var_8278_pad_0 = const()[name = tensor("op_8278_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3084847936)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3088124800)))]; + tensor var_8278_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_8276, groups = var_6865, pad = var_8278_pad_0, pad_type = var_8278_pad_type_0, strides = var_8274, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_495_cast)[name = tensor("op_8278_cast")]; + tensor inputs_249_cast = add(x = var_8278_cast, y = inputs_247_cast)[name = tensor("inputs_249_cast")]; + tensor var_8282 = const()[name = tensor("op_8282"), val = tensor([1])]; + tensor channels_mean_249_cast = reduce_mean(axes = var_8282, keep_dims = var_6860, x = inputs_249_cast)[name = tensor("channels_mean_249_cast")]; + tensor zero_mean_249_cast = sub(x = inputs_249_cast, y = channels_mean_249_cast)[name = tensor("zero_mean_249_cast")]; + tensor zero_mean_sq_249_cast = mul(x = zero_mean_249_cast, y = zero_mean_249_cast)[name = tensor("zero_mean_sq_249_cast")]; + tensor var_8286 = const()[name = tensor("op_8286"), val = tensor([1])]; + tensor var_8287_cast = reduce_mean(axes = var_8286, keep_dims = var_6860, x = zero_mean_sq_249_cast)[name = tensor("op_8287_cast")]; + tensor var_8288_to_fp16 = const()[name = tensor("op_8288_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8289_cast = add(x = var_8287_cast, y = var_8288_to_fp16)[name = tensor("op_8289_cast")]; + tensor denom_249_epsilon_0_to_fp16 = const()[name = tensor("denom_249_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_249_cast = rsqrt(epsilon = denom_249_epsilon_0_to_fp16, x = var_8289_cast)[name = tensor("denom_249_cast")]; + tensor out_249_cast = mul(x = zero_mean_249_cast, y = denom_249_cast)[name = tensor("out_249_cast")]; + tensor var_8293_to_fp16 = const()[name = tensor("op_8293_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3088127424)))]; + tensor var_8294_cast = add(x = out_249_cast, y = var_8293_to_fp16)[name = tensor("op_8294_cast")]; + tensor var_8296_to_fp16 = const()[name = tensor("op_8296_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3088130048)))]; + tensor hidden_states_331_cast = mul(x = var_8294_cast, y = var_8296_to_fp16)[name = tensor("hidden_states_331_cast")]; + tensor var_8303 = const()[name = tensor("op_8303"), val = tensor([1, 1])]; + tensor var_8305 = const()[name = tensor("op_8305"), val = tensor([1, 1])]; + tensor q_167_pad_type_0 = const()[name = tensor("q_167_pad_type_0"), val = tensor("custom")]; + tensor q_167_pad_0 = const()[name = tensor("q_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3088132672)))]; + tensor q_167_cast = conv(dilations = var_8305, groups = var_6865, pad = q_167_pad_0, pad_type = q_167_pad_type_0, strides = var_8303, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_331_cast)[name = tensor("q_167_cast")]; + tensor var_8309 = const()[name = tensor("op_8309"), val = tensor([1, 1])]; + tensor var_8311 = const()[name = tensor("op_8311"), val = tensor([1, 1])]; + tensor k_167_pad_type_0 = const()[name = tensor("k_167_pad_type_0"), val = tensor("custom")]; + tensor k_167_pad_0 = const()[name = tensor("k_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3091409536)))]; + tensor k_167_cast = conv(dilations = var_8311, groups = var_6865, pad = k_167_pad_0, pad_type = k_167_pad_type_0, strides = var_8309, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_167_cast")]; + tensor var_8315 = const()[name = tensor("op_8315"), val = tensor([1, 1])]; + tensor var_8317 = const()[name = tensor("op_8317"), val = tensor([1, 1])]; + tensor v_167_pad_type_0 = const()[name = tensor("v_167_pad_type_0"), val = tensor("custom")]; + tensor v_167_pad_0 = const()[name = tensor("v_167_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3096652480)))]; + tensor v_167_cast = conv(dilations = var_8317, groups = var_6865, pad = v_167_pad_0, pad_type = v_167_pad_type_0, strides = var_8315, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_167_cast")]; + tensor var_8321 = const()[name = tensor("op_8321"), val = tensor([2, 20, 64, -1])]; + tensor var_8322_cast = reshape(shape = var_8321, x = q_167_cast)[name = tensor("op_8322_cast")]; + tensor var_8323 = const()[name = tensor("op_8323"), val = tensor([2, 20, 64, -1])]; + tensor var_8324_cast = reshape(shape = var_8323, x = k_167_cast)[name = tensor("op_8324_cast")]; + tensor var_8325 = const()[name = tensor("op_8325"), val = tensor([2, 20, 64, -1])]; + tensor var_8326_cast = reshape(shape = var_8325, x = v_167_cast)[name = tensor("op_8326_cast")]; + tensor attn_weights_333_transpose_x_0 = const()[name = tensor("attn_weights_333_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_333_transpose_y_0 = const()[name = tensor("attn_weights_333_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_333_cast = matmul(transpose_x = attn_weights_333_transpose_x_0, transpose_y = attn_weights_333_transpose_y_0, x = var_8322_cast, y = var_8324_cast)[name = tensor("attn_weights_333_cast")]; + tensor attn_weights_335_cast = mul(x = attn_weights_333_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_335_cast")]; + tensor var_8330_cast = softmax(axis = var_6849, x = attn_weights_335_cast)[name = tensor("op_8330_cast")]; + tensor attn_167_transpose_x_0 = const()[name = tensor("attn_167_transpose_x_0"), val = tensor(false)]; + tensor attn_167_transpose_y_0 = const()[name = tensor("attn_167_transpose_y_0"), val = tensor(true)]; + tensor attn_167_cast = matmul(transpose_x = attn_167_transpose_x_0, transpose_y = attn_167_transpose_y_0, x = var_8326_cast, y = var_8330_cast)[name = tensor("attn_167_cast")]; + tensor var_8334 = const()[name = tensor("op_8334"), val = tensor([2, 1280, 1, -1])]; + tensor input_497_cast = reshape(shape = var_8334, x = attn_167_cast)[name = tensor("input_497_cast")]; + tensor var_8339 = const()[name = tensor("op_8339"), val = tensor([1, 1])]; + tensor var_8341 = const()[name = tensor("op_8341"), val = tensor([1, 1])]; + tensor var_8343_pad_type_0 = const()[name = tensor("op_8343_pad_type_0"), val = tensor("custom")]; + tensor var_8343_pad_0 = const()[name = tensor("op_8343_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3101895424)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3105172288)))]; + tensor var_8343_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_8341, groups = var_6865, pad = var_8343_pad_0, pad_type = var_8343_pad_type_0, strides = var_8339, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_497_cast)[name = tensor("op_8343_cast")]; + tensor inputs_251_cast = add(x = var_8343_cast, y = inputs_249_cast)[name = tensor("inputs_251_cast")]; + tensor var_8347 = const()[name = tensor("op_8347"), val = tensor([1])]; + tensor channels_mean_251_cast = reduce_mean(axes = var_8347, keep_dims = var_6860, x = inputs_251_cast)[name = tensor("channels_mean_251_cast")]; + tensor zero_mean_251_cast = sub(x = inputs_251_cast, y = channels_mean_251_cast)[name = tensor("zero_mean_251_cast")]; + tensor zero_mean_sq_251_cast = mul(x = zero_mean_251_cast, y = zero_mean_251_cast)[name = tensor("zero_mean_sq_251_cast")]; + tensor var_8351 = const()[name = tensor("op_8351"), val = tensor([1])]; + tensor var_8352_cast = reduce_mean(axes = var_8351, keep_dims = var_6860, x = zero_mean_sq_251_cast)[name = tensor("op_8352_cast")]; + tensor var_8353_to_fp16 = const()[name = tensor("op_8353_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8354_cast = add(x = var_8352_cast, y = var_8353_to_fp16)[name = tensor("op_8354_cast")]; + tensor denom_251_epsilon_0_to_fp16 = const()[name = tensor("denom_251_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_251_cast = rsqrt(epsilon = denom_251_epsilon_0_to_fp16, x = var_8354_cast)[name = tensor("denom_251_cast")]; + tensor out_251_cast = mul(x = zero_mean_251_cast, y = denom_251_cast)[name = tensor("out_251_cast")]; + tensor var_8358_to_fp16 = const()[name = tensor("op_8358_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3105174912)))]; + tensor var_8359_cast = add(x = out_251_cast, y = var_8358_to_fp16)[name = tensor("op_8359_cast")]; + tensor var_8361_to_fp16 = const()[name = tensor("op_8361_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3105177536)))]; + tensor input_499_cast = mul(x = var_8359_cast, y = var_8361_to_fp16)[name = tensor("input_499_cast")]; + tensor var_8369 = const()[name = tensor("op_8369"), val = tensor([1, 1])]; + tensor var_8371 = const()[name = tensor("op_8371"), val = tensor([1, 1])]; + tensor var_8373_pad_type_0 = const()[name = tensor("op_8373_pad_type_0"), val = tensor("custom")]; + tensor var_8373_pad_0 = const()[name = tensor("op_8373_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3105180160)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3131394624)))]; + tensor var_8373_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_8371, groups = var_6865, pad = var_8373_pad_0, pad_type = var_8373_pad_type_0, strides = var_8369, weight = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_499_cast)[name = tensor("op_8373_cast")]; + tensor var_8374_split_sizes_0 = const()[name = tensor("op_8374_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8374_axis_0 = const()[name = tensor("op_8374_axis_0"), val = tensor(1)]; + tensor var_8374_cast_0, tensor var_8374_cast_1 = split(axis = var_8374_axis_0, split_sizes = var_8374_split_sizes_0, x = var_8373_cast)[name = tensor("op_8374_cast")]; + tensor var_8376_mode_0 = const()[name = tensor("op_8376_mode_0"), val = tensor("EXACT")]; + tensor var_8376_cast = gelu(mode = var_8376_mode_0, x = var_8374_cast_1)[name = tensor("op_8376_cast")]; + tensor input_501_cast = mul(x = var_8374_cast_0, y = var_8376_cast)[name = tensor("input_501_cast")]; + tensor var_8380 = const()[name = tensor("op_8380"), val = tensor([1, 1])]; + tensor var_8382 = const()[name = tensor("op_8382"), val = tensor([1, 1])]; + tensor var_8384_pad_type_0 = const()[name = tensor("op_8384_pad_type_0"), val = tensor("custom")]; + tensor var_8384_pad_0 = const()[name = tensor("op_8384_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3131415168)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3144522432)))]; + tensor var_8384_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_8382, groups = var_6865, pad = var_8384_pad_0, pad_type = var_8384_pad_type_0, strides = var_8380, weight = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_501_cast)[name = tensor("op_8384_cast")]; + tensor inputs_253_cast = add(x = var_8384_cast, y = inputs_251_cast)[name = tensor("inputs_253_cast")]; + tensor var_8394 = const()[name = tensor("op_8394"), val = tensor([1])]; + tensor channels_mean_253_cast = reduce_mean(axes = var_8394, keep_dims = var_6860, x = inputs_253_cast)[name = tensor("channels_mean_253_cast")]; + tensor zero_mean_253_cast = sub(x = inputs_253_cast, y = channels_mean_253_cast)[name = tensor("zero_mean_253_cast")]; + tensor zero_mean_sq_253_cast = mul(x = zero_mean_253_cast, y = zero_mean_253_cast)[name = tensor("zero_mean_sq_253_cast")]; + tensor var_8398 = const()[name = tensor("op_8398"), val = tensor([1])]; + tensor var_8399_cast = reduce_mean(axes = var_8398, keep_dims = var_6860, x = zero_mean_sq_253_cast)[name = tensor("op_8399_cast")]; + tensor var_8400_to_fp16 = const()[name = tensor("op_8400_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8401_cast = add(x = var_8399_cast, y = var_8400_to_fp16)[name = tensor("op_8401_cast")]; + tensor denom_253_epsilon_0_to_fp16 = const()[name = tensor("denom_253_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_253_cast = rsqrt(epsilon = denom_253_epsilon_0_to_fp16, x = var_8401_cast)[name = tensor("denom_253_cast")]; + tensor out_253_cast = mul(x = zero_mean_253_cast, y = denom_253_cast)[name = tensor("out_253_cast")]; + tensor var_8405_to_fp16 = const()[name = tensor("op_8405_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3144525056)))]; + tensor var_8406_cast = add(x = out_253_cast, y = var_8405_to_fp16)[name = tensor("op_8406_cast")]; + tensor var_8408_to_fp16 = const()[name = tensor("op_8408_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3144527680)))]; + tensor hidden_states_335_cast = mul(x = var_8406_cast, y = var_8408_to_fp16)[name = tensor("hidden_states_335_cast")]; + tensor var_8415 = const()[name = tensor("op_8415"), val = tensor([1, 1])]; + tensor var_8417 = const()[name = tensor("op_8417"), val = tensor([1, 1])]; + tensor q_169_pad_type_0 = const()[name = tensor("q_169_pad_type_0"), val = tensor("custom")]; + tensor q_169_pad_0 = const()[name = tensor("q_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3144530304)))]; + tensor q_169_cast = conv(dilations = var_8417, groups = var_6865, pad = q_169_pad_0, pad_type = q_169_pad_type_0, strides = var_8415, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_335_cast)[name = tensor("q_169_cast")]; + tensor var_8421 = const()[name = tensor("op_8421"), val = tensor([1, 1])]; + tensor var_8423 = const()[name = tensor("op_8423"), val = tensor([1, 1])]; + tensor k_169_pad_type_0 = const()[name = tensor("k_169_pad_type_0"), val = tensor("custom")]; + tensor k_169_pad_0 = const()[name = tensor("k_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3147807168)))]; + tensor k_169_cast = conv(dilations = var_8423, groups = var_6865, pad = k_169_pad_0, pad_type = k_169_pad_type_0, strides = var_8421, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_335_cast)[name = tensor("k_169_cast")]; + tensor var_8427 = const()[name = tensor("op_8427"), val = tensor([1, 1])]; + tensor var_8429 = const()[name = tensor("op_8429"), val = tensor([1, 1])]; + tensor v_169_pad_type_0 = const()[name = tensor("v_169_pad_type_0"), val = tensor("custom")]; + tensor v_169_pad_0 = const()[name = tensor("v_169_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3151084032)))]; + tensor v_169_cast = conv(dilations = var_8429, groups = var_6865, pad = v_169_pad_0, pad_type = v_169_pad_type_0, strides = var_8427, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_335_cast)[name = tensor("v_169_cast")]; + tensor var_8433 = const()[name = tensor("op_8433"), val = tensor([2, 20, 64, -1])]; + tensor var_8434_cast = reshape(shape = var_8433, x = q_169_cast)[name = tensor("op_8434_cast")]; + tensor var_8435 = const()[name = tensor("op_8435"), val = tensor([2, 20, 64, -1])]; + tensor var_8436_cast = reshape(shape = var_8435, x = k_169_cast)[name = tensor("op_8436_cast")]; + tensor var_8437 = const()[name = tensor("op_8437"), val = tensor([2, 20, 64, -1])]; + tensor var_8438_cast = reshape(shape = var_8437, x = v_169_cast)[name = tensor("op_8438_cast")]; + tensor attn_weights_337_transpose_x_0 = const()[name = tensor("attn_weights_337_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_337_transpose_y_0 = const()[name = tensor("attn_weights_337_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_337_cast = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_8434_cast, y = var_8436_cast)[name = tensor("attn_weights_337_cast")]; + tensor attn_weights_339_cast = mul(x = attn_weights_337_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_339_cast")]; + tensor var_8442_cast = softmax(axis = var_6849, x = attn_weights_339_cast)[name = tensor("op_8442_cast")]; + tensor attn_169_transpose_x_0 = const()[name = tensor("attn_169_transpose_x_0"), val = tensor(false)]; + tensor attn_169_transpose_y_0 = const()[name = tensor("attn_169_transpose_y_0"), val = tensor(true)]; + tensor attn_169_cast = matmul(transpose_x = attn_169_transpose_x_0, transpose_y = attn_169_transpose_y_0, x = var_8438_cast, y = var_8442_cast)[name = tensor("attn_169_cast")]; + tensor var_8446 = const()[name = tensor("op_8446"), val = tensor([2, 1280, 1, -1])]; + tensor input_503_cast = reshape(shape = var_8446, x = attn_169_cast)[name = tensor("input_503_cast")]; + tensor var_8451 = const()[name = tensor("op_8451"), val = tensor([1, 1])]; + tensor var_8453 = const()[name = tensor("op_8453"), val = tensor([1, 1])]; + tensor var_8455_pad_type_0 = const()[name = tensor("op_8455_pad_type_0"), val = tensor("custom")]; + tensor var_8455_pad_0 = const()[name = tensor("op_8455_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3154360896)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3157637760)))]; + tensor var_8455_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_8453, groups = var_6865, pad = var_8455_pad_0, pad_type = var_8455_pad_type_0, strides = var_8451, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_503_cast)[name = tensor("op_8455_cast")]; + tensor inputs_255_cast = add(x = var_8455_cast, y = inputs_253_cast)[name = tensor("inputs_255_cast")]; + tensor var_8459 = const()[name = tensor("op_8459"), val = tensor([1])]; + tensor channels_mean_255_cast = reduce_mean(axes = var_8459, keep_dims = var_6860, x = inputs_255_cast)[name = tensor("channels_mean_255_cast")]; + tensor zero_mean_255_cast = sub(x = inputs_255_cast, y = channels_mean_255_cast)[name = tensor("zero_mean_255_cast")]; + tensor zero_mean_sq_255_cast = mul(x = zero_mean_255_cast, y = zero_mean_255_cast)[name = tensor("zero_mean_sq_255_cast")]; + tensor var_8463 = const()[name = tensor("op_8463"), val = tensor([1])]; + tensor var_8464_cast = reduce_mean(axes = var_8463, keep_dims = var_6860, x = zero_mean_sq_255_cast)[name = tensor("op_8464_cast")]; + tensor var_8465_to_fp16 = const()[name = tensor("op_8465_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8466_cast = add(x = var_8464_cast, y = var_8465_to_fp16)[name = tensor("op_8466_cast")]; + tensor denom_255_epsilon_0_to_fp16 = const()[name = tensor("denom_255_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_255_cast = rsqrt(epsilon = denom_255_epsilon_0_to_fp16, x = var_8466_cast)[name = tensor("denom_255_cast")]; + tensor out_255_cast = mul(x = zero_mean_255_cast, y = denom_255_cast)[name = tensor("out_255_cast")]; + tensor var_8470_to_fp16 = const()[name = tensor("op_8470_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3157640384)))]; + tensor var_8471_cast = add(x = out_255_cast, y = var_8470_to_fp16)[name = tensor("op_8471_cast")]; + tensor var_8473_to_fp16 = const()[name = tensor("op_8473_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3157643008)))]; + tensor hidden_states_337_cast = mul(x = var_8471_cast, y = var_8473_to_fp16)[name = tensor("hidden_states_337_cast")]; + tensor var_8480 = const()[name = tensor("op_8480"), val = tensor([1, 1])]; + tensor var_8482 = const()[name = tensor("op_8482"), val = tensor([1, 1])]; + tensor q_171_pad_type_0 = const()[name = tensor("q_171_pad_type_0"), val = tensor("custom")]; + tensor q_171_pad_0 = const()[name = tensor("q_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3157645632)))]; + tensor q_171_cast = conv(dilations = var_8482, groups = var_6865, pad = q_171_pad_0, pad_type = q_171_pad_type_0, strides = var_8480, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_337_cast)[name = tensor("q_171_cast")]; + tensor var_8486 = const()[name = tensor("op_8486"), val = tensor([1, 1])]; + tensor var_8488 = const()[name = tensor("op_8488"), val = tensor([1, 1])]; + tensor k_171_pad_type_0 = const()[name = tensor("k_171_pad_type_0"), val = tensor("custom")]; + tensor k_171_pad_0 = const()[name = tensor("k_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3160922496)))]; + tensor k_171_cast = conv(dilations = var_8488, groups = var_6865, pad = k_171_pad_0, pad_type = k_171_pad_type_0, strides = var_8486, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_171_cast")]; + tensor var_8492 = const()[name = tensor("op_8492"), val = tensor([1, 1])]; + tensor var_8494 = const()[name = tensor("op_8494"), val = tensor([1, 1])]; + tensor v_171_pad_type_0 = const()[name = tensor("v_171_pad_type_0"), val = tensor("custom")]; + tensor v_171_pad_0 = const()[name = tensor("v_171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3166165440)))]; + tensor v_171_cast = conv(dilations = var_8494, groups = var_6865, pad = v_171_pad_0, pad_type = v_171_pad_type_0, strides = var_8492, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_171_cast")]; + tensor var_8498 = const()[name = tensor("op_8498"), val = tensor([2, 20, 64, -1])]; + tensor var_8499_cast = reshape(shape = var_8498, x = q_171_cast)[name = tensor("op_8499_cast")]; + tensor var_8500 = const()[name = tensor("op_8500"), val = tensor([2, 20, 64, -1])]; + tensor var_8501_cast = reshape(shape = var_8500, x = k_171_cast)[name = tensor("op_8501_cast")]; + tensor var_8502 = const()[name = tensor("op_8502"), val = tensor([2, 20, 64, -1])]; + tensor var_8503_cast = reshape(shape = var_8502, x = v_171_cast)[name = tensor("op_8503_cast")]; + tensor attn_weights_341_transpose_x_0 = const()[name = tensor("attn_weights_341_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_341_transpose_y_0 = const()[name = tensor("attn_weights_341_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_341_cast = matmul(transpose_x = attn_weights_341_transpose_x_0, transpose_y = attn_weights_341_transpose_y_0, x = var_8499_cast, y = var_8501_cast)[name = tensor("attn_weights_341_cast")]; + tensor attn_weights_343_cast = mul(x = attn_weights_341_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_343_cast")]; + tensor var_8507_cast = softmax(axis = var_6849, x = attn_weights_343_cast)[name = tensor("op_8507_cast")]; + tensor attn_171_transpose_x_0 = const()[name = tensor("attn_171_transpose_x_0"), val = tensor(false)]; + tensor attn_171_transpose_y_0 = const()[name = tensor("attn_171_transpose_y_0"), val = tensor(true)]; + tensor attn_171_cast = matmul(transpose_x = attn_171_transpose_x_0, transpose_y = attn_171_transpose_y_0, x = var_8503_cast, y = var_8507_cast)[name = tensor("attn_171_cast")]; + tensor var_8511 = const()[name = tensor("op_8511"), val = tensor([2, 1280, 1, -1])]; + tensor input_505_cast = reshape(shape = var_8511, x = attn_171_cast)[name = tensor("input_505_cast")]; + tensor var_8516 = const()[name = tensor("op_8516"), val = tensor([1, 1])]; + tensor var_8518 = const()[name = tensor("op_8518"), val = tensor([1, 1])]; + tensor var_8520_pad_type_0 = const()[name = tensor("op_8520_pad_type_0"), val = tensor("custom")]; + tensor var_8520_pad_0 = const()[name = tensor("op_8520_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3171408384)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3174685248)))]; + tensor var_8520_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_8518, groups = var_6865, pad = var_8520_pad_0, pad_type = var_8520_pad_type_0, strides = var_8516, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_505_cast)[name = tensor("op_8520_cast")]; + tensor inputs_257_cast = add(x = var_8520_cast, y = inputs_255_cast)[name = tensor("inputs_257_cast")]; + tensor var_8524 = const()[name = tensor("op_8524"), val = tensor([1])]; + tensor channels_mean_257_cast = reduce_mean(axes = var_8524, keep_dims = var_6860, x = inputs_257_cast)[name = tensor("channels_mean_257_cast")]; + tensor zero_mean_257_cast = sub(x = inputs_257_cast, y = channels_mean_257_cast)[name = tensor("zero_mean_257_cast")]; + tensor zero_mean_sq_257_cast = mul(x = zero_mean_257_cast, y = zero_mean_257_cast)[name = tensor("zero_mean_sq_257_cast")]; + tensor var_8528 = const()[name = tensor("op_8528"), val = tensor([1])]; + tensor var_8529_cast = reduce_mean(axes = var_8528, keep_dims = var_6860, x = zero_mean_sq_257_cast)[name = tensor("op_8529_cast")]; + tensor var_8530_to_fp16 = const()[name = tensor("op_8530_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8531_cast = add(x = var_8529_cast, y = var_8530_to_fp16)[name = tensor("op_8531_cast")]; + tensor denom_257_epsilon_0_to_fp16 = const()[name = tensor("denom_257_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_257_cast = rsqrt(epsilon = denom_257_epsilon_0_to_fp16, x = var_8531_cast)[name = tensor("denom_257_cast")]; + tensor out_257_cast = mul(x = zero_mean_257_cast, y = denom_257_cast)[name = tensor("out_257_cast")]; + tensor var_8535_to_fp16 = const()[name = tensor("op_8535_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3174687872)))]; + tensor var_8536_cast = add(x = out_257_cast, y = var_8535_to_fp16)[name = tensor("op_8536_cast")]; + tensor var_8538_to_fp16 = const()[name = tensor("op_8538_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3174690496)))]; + tensor input_507_cast = mul(x = var_8536_cast, y = var_8538_to_fp16)[name = tensor("input_507_cast")]; + tensor var_8546 = const()[name = tensor("op_8546"), val = tensor([1, 1])]; + tensor var_8548 = const()[name = tensor("op_8548"), val = tensor([1, 1])]; + tensor var_8550_pad_type_0 = const()[name = tensor("op_8550_pad_type_0"), val = tensor("custom")]; + tensor var_8550_pad_0 = const()[name = tensor("op_8550_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3174693120)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3200907584)))]; + tensor var_8550_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_8548, groups = var_6865, pad = var_8550_pad_0, pad_type = var_8550_pad_type_0, strides = var_8546, weight = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_507_cast)[name = tensor("op_8550_cast")]; + tensor var_8551_split_sizes_0 = const()[name = tensor("op_8551_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8551_axis_0 = const()[name = tensor("op_8551_axis_0"), val = tensor(1)]; + tensor var_8551_cast_0, tensor var_8551_cast_1 = split(axis = var_8551_axis_0, split_sizes = var_8551_split_sizes_0, x = var_8550_cast)[name = tensor("op_8551_cast")]; + tensor var_8553_mode_0 = const()[name = tensor("op_8553_mode_0"), val = tensor("EXACT")]; + tensor var_8553_cast = gelu(mode = var_8553_mode_0, x = var_8551_cast_1)[name = tensor("op_8553_cast")]; + tensor input_509_cast = mul(x = var_8551_cast_0, y = var_8553_cast)[name = tensor("input_509_cast")]; + tensor var_8557 = const()[name = tensor("op_8557"), val = tensor([1, 1])]; + tensor var_8559 = const()[name = tensor("op_8559"), val = tensor([1, 1])]; + tensor var_8561_pad_type_0 = const()[name = tensor("op_8561_pad_type_0"), val = tensor("custom")]; + tensor var_8561_pad_0 = const()[name = tensor("op_8561_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3200928128)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3214035392)))]; + tensor var_8561_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_8559, groups = var_6865, pad = var_8561_pad_0, pad_type = var_8561_pad_type_0, strides = var_8557, weight = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_509_cast)[name = tensor("op_8561_cast")]; + tensor inputs_259_cast = add(x = var_8561_cast, y = inputs_257_cast)[name = tensor("inputs_259_cast")]; + tensor var_8571 = const()[name = tensor("op_8571"), val = tensor([1])]; + tensor channels_mean_259_cast = reduce_mean(axes = var_8571, keep_dims = var_6860, x = inputs_259_cast)[name = tensor("channels_mean_259_cast")]; + tensor zero_mean_259_cast = sub(x = inputs_259_cast, y = channels_mean_259_cast)[name = tensor("zero_mean_259_cast")]; + tensor zero_mean_sq_259_cast = mul(x = zero_mean_259_cast, y = zero_mean_259_cast)[name = tensor("zero_mean_sq_259_cast")]; + tensor var_8575 = const()[name = tensor("op_8575"), val = tensor([1])]; + tensor var_8576_cast = reduce_mean(axes = var_8575, keep_dims = var_6860, x = zero_mean_sq_259_cast)[name = tensor("op_8576_cast")]; + tensor var_8577_to_fp16 = const()[name = tensor("op_8577_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8578_cast = add(x = var_8576_cast, y = var_8577_to_fp16)[name = tensor("op_8578_cast")]; + tensor denom_259_epsilon_0_to_fp16 = const()[name = tensor("denom_259_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_259_cast = rsqrt(epsilon = denom_259_epsilon_0_to_fp16, x = var_8578_cast)[name = tensor("denom_259_cast")]; + tensor out_259_cast = mul(x = zero_mean_259_cast, y = denom_259_cast)[name = tensor("out_259_cast")]; + tensor var_8582_to_fp16 = const()[name = tensor("op_8582_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3214038016)))]; + tensor var_8583_cast = add(x = out_259_cast, y = var_8582_to_fp16)[name = tensor("op_8583_cast")]; + tensor var_8585_to_fp16 = const()[name = tensor("op_8585_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3214040640)))]; + tensor hidden_states_341_cast = mul(x = var_8583_cast, y = var_8585_to_fp16)[name = tensor("hidden_states_341_cast")]; + tensor var_8592 = const()[name = tensor("op_8592"), val = tensor([1, 1])]; + tensor var_8594 = const()[name = tensor("op_8594"), val = tensor([1, 1])]; + tensor q_173_pad_type_0 = const()[name = tensor("q_173_pad_type_0"), val = tensor("custom")]; + tensor q_173_pad_0 = const()[name = tensor("q_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3214043264)))]; + tensor q_173_cast = conv(dilations = var_8594, groups = var_6865, pad = q_173_pad_0, pad_type = q_173_pad_type_0, strides = var_8592, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_341_cast)[name = tensor("q_173_cast")]; + tensor var_8598 = const()[name = tensor("op_8598"), val = tensor([1, 1])]; + tensor var_8600 = const()[name = tensor("op_8600"), val = tensor([1, 1])]; + tensor k_173_pad_type_0 = const()[name = tensor("k_173_pad_type_0"), val = tensor("custom")]; + tensor k_173_pad_0 = const()[name = tensor("k_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3217320128)))]; + tensor k_173_cast = conv(dilations = var_8600, groups = var_6865, pad = k_173_pad_0, pad_type = k_173_pad_type_0, strides = var_8598, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_341_cast)[name = tensor("k_173_cast")]; + tensor var_8604 = const()[name = tensor("op_8604"), val = tensor([1, 1])]; + tensor var_8606 = const()[name = tensor("op_8606"), val = tensor([1, 1])]; + tensor v_173_pad_type_0 = const()[name = tensor("v_173_pad_type_0"), val = tensor("custom")]; + tensor v_173_pad_0 = const()[name = tensor("v_173_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3220596992)))]; + tensor v_173_cast = conv(dilations = var_8606, groups = var_6865, pad = v_173_pad_0, pad_type = v_173_pad_type_0, strides = var_8604, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_341_cast)[name = tensor("v_173_cast")]; + tensor var_8610 = const()[name = tensor("op_8610"), val = tensor([2, 20, 64, -1])]; + tensor var_8611_cast = reshape(shape = var_8610, x = q_173_cast)[name = tensor("op_8611_cast")]; + tensor var_8612 = const()[name = tensor("op_8612"), val = tensor([2, 20, 64, -1])]; + tensor var_8613_cast = reshape(shape = var_8612, x = k_173_cast)[name = tensor("op_8613_cast")]; + tensor var_8614 = const()[name = tensor("op_8614"), val = tensor([2, 20, 64, -1])]; + tensor var_8615_cast = reshape(shape = var_8614, x = v_173_cast)[name = tensor("op_8615_cast")]; + tensor attn_weights_345_transpose_x_0 = const()[name = tensor("attn_weights_345_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_345_transpose_y_0 = const()[name = tensor("attn_weights_345_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_345_cast = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_8611_cast, y = var_8613_cast)[name = tensor("attn_weights_345_cast")]; + tensor attn_weights_347_cast = mul(x = attn_weights_345_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_347_cast")]; + tensor var_8619_cast = softmax(axis = var_6849, x = attn_weights_347_cast)[name = tensor("op_8619_cast")]; + tensor attn_173_transpose_x_0 = const()[name = tensor("attn_173_transpose_x_0"), val = tensor(false)]; + tensor attn_173_transpose_y_0 = const()[name = tensor("attn_173_transpose_y_0"), val = tensor(true)]; + tensor attn_173_cast = matmul(transpose_x = attn_173_transpose_x_0, transpose_y = attn_173_transpose_y_0, x = var_8615_cast, y = var_8619_cast)[name = tensor("attn_173_cast")]; + tensor var_8623 = const()[name = tensor("op_8623"), val = tensor([2, 1280, 1, -1])]; + tensor input_511_cast = reshape(shape = var_8623, x = attn_173_cast)[name = tensor("input_511_cast")]; + tensor var_8628 = const()[name = tensor("op_8628"), val = tensor([1, 1])]; + tensor var_8630 = const()[name = tensor("op_8630"), val = tensor([1, 1])]; + tensor var_8632_pad_type_0 = const()[name = tensor("op_8632_pad_type_0"), val = tensor("custom")]; + tensor var_8632_pad_0 = const()[name = tensor("op_8632_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3223873856)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3227150720)))]; + tensor var_8632_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_8630, groups = var_6865, pad = var_8632_pad_0, pad_type = var_8632_pad_type_0, strides = var_8628, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_511_cast)[name = tensor("op_8632_cast")]; + tensor inputs_261_cast = add(x = var_8632_cast, y = inputs_259_cast)[name = tensor("inputs_261_cast")]; + tensor var_8636 = const()[name = tensor("op_8636"), val = tensor([1])]; + tensor channels_mean_261_cast = reduce_mean(axes = var_8636, keep_dims = var_6860, x = inputs_261_cast)[name = tensor("channels_mean_261_cast")]; + tensor zero_mean_261_cast = sub(x = inputs_261_cast, y = channels_mean_261_cast)[name = tensor("zero_mean_261_cast")]; + tensor zero_mean_sq_261_cast = mul(x = zero_mean_261_cast, y = zero_mean_261_cast)[name = tensor("zero_mean_sq_261_cast")]; + tensor var_8640 = const()[name = tensor("op_8640"), val = tensor([1])]; + tensor var_8641_cast = reduce_mean(axes = var_8640, keep_dims = var_6860, x = zero_mean_sq_261_cast)[name = tensor("op_8641_cast")]; + tensor var_8642_to_fp16 = const()[name = tensor("op_8642_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8643_cast = add(x = var_8641_cast, y = var_8642_to_fp16)[name = tensor("op_8643_cast")]; + tensor denom_261_epsilon_0_to_fp16 = const()[name = tensor("denom_261_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_261_cast = rsqrt(epsilon = denom_261_epsilon_0_to_fp16, x = var_8643_cast)[name = tensor("denom_261_cast")]; + tensor out_261_cast = mul(x = zero_mean_261_cast, y = denom_261_cast)[name = tensor("out_261_cast")]; + tensor var_8647_to_fp16 = const()[name = tensor("op_8647_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3227153344)))]; + tensor var_8648_cast = add(x = out_261_cast, y = var_8647_to_fp16)[name = tensor("op_8648_cast")]; + tensor var_8650_to_fp16 = const()[name = tensor("op_8650_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3227155968)))]; + tensor hidden_states_343_cast = mul(x = var_8648_cast, y = var_8650_to_fp16)[name = tensor("hidden_states_343_cast")]; + tensor var_8657 = const()[name = tensor("op_8657"), val = tensor([1, 1])]; + tensor var_8659 = const()[name = tensor("op_8659"), val = tensor([1, 1])]; + tensor q_175_pad_type_0 = const()[name = tensor("q_175_pad_type_0"), val = tensor("custom")]; + tensor q_175_pad_0 = const()[name = tensor("q_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3227158592)))]; + tensor q_175_cast = conv(dilations = var_8659, groups = var_6865, pad = q_175_pad_0, pad_type = q_175_pad_type_0, strides = var_8657, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_343_cast)[name = tensor("q_175_cast")]; + tensor var_8663 = const()[name = tensor("op_8663"), val = tensor([1, 1])]; + tensor var_8665 = const()[name = tensor("op_8665"), val = tensor([1, 1])]; + tensor k_175_pad_type_0 = const()[name = tensor("k_175_pad_type_0"), val = tensor("custom")]; + tensor k_175_pad_0 = const()[name = tensor("k_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3230435456)))]; + tensor k_175_cast = conv(dilations = var_8665, groups = var_6865, pad = k_175_pad_0, pad_type = k_175_pad_type_0, strides = var_8663, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_175_cast")]; + tensor var_8669 = const()[name = tensor("op_8669"), val = tensor([1, 1])]; + tensor var_8671 = const()[name = tensor("op_8671"), val = tensor([1, 1])]; + tensor v_175_pad_type_0 = const()[name = tensor("v_175_pad_type_0"), val = tensor("custom")]; + tensor v_175_pad_0 = const()[name = tensor("v_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3235678400)))]; + tensor v_175_cast = conv(dilations = var_8671, groups = var_6865, pad = v_175_pad_0, pad_type = v_175_pad_type_0, strides = var_8669, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_175_cast")]; + tensor var_8675 = const()[name = tensor("op_8675"), val = tensor([2, 20, 64, -1])]; + tensor var_8676_cast = reshape(shape = var_8675, x = q_175_cast)[name = tensor("op_8676_cast")]; + tensor var_8677 = const()[name = tensor("op_8677"), val = tensor([2, 20, 64, -1])]; + tensor var_8678_cast = reshape(shape = var_8677, x = k_175_cast)[name = tensor("op_8678_cast")]; + tensor var_8679 = const()[name = tensor("op_8679"), val = tensor([2, 20, 64, -1])]; + tensor var_8680_cast = reshape(shape = var_8679, x = v_175_cast)[name = tensor("op_8680_cast")]; + tensor attn_weights_349_transpose_x_0 = const()[name = tensor("attn_weights_349_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_349_transpose_y_0 = const()[name = tensor("attn_weights_349_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_349_cast = matmul(transpose_x = attn_weights_349_transpose_x_0, transpose_y = attn_weights_349_transpose_y_0, x = var_8676_cast, y = var_8678_cast)[name = tensor("attn_weights_349_cast")]; + tensor attn_weights_351_cast = mul(x = attn_weights_349_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_351_cast")]; + tensor var_8684_cast = softmax(axis = var_6849, x = attn_weights_351_cast)[name = tensor("op_8684_cast")]; + tensor attn_175_transpose_x_0 = const()[name = tensor("attn_175_transpose_x_0"), val = tensor(false)]; + tensor attn_175_transpose_y_0 = const()[name = tensor("attn_175_transpose_y_0"), val = tensor(true)]; + tensor attn_175_cast = matmul(transpose_x = attn_175_transpose_x_0, transpose_y = attn_175_transpose_y_0, x = var_8680_cast, y = var_8684_cast)[name = tensor("attn_175_cast")]; + tensor var_8688 = const()[name = tensor("op_8688"), val = tensor([2, 1280, 1, -1])]; + tensor input_513_cast = reshape(shape = var_8688, x = attn_175_cast)[name = tensor("input_513_cast")]; + tensor var_8693 = const()[name = tensor("op_8693"), val = tensor([1, 1])]; + tensor var_8695 = const()[name = tensor("op_8695"), val = tensor([1, 1])]; + tensor var_8697_pad_type_0 = const()[name = tensor("op_8697_pad_type_0"), val = tensor("custom")]; + tensor var_8697_pad_0 = const()[name = tensor("op_8697_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3240921344)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3244198208)))]; + tensor var_8697_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_8695, groups = var_6865, pad = var_8697_pad_0, pad_type = var_8697_pad_type_0, strides = var_8693, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_513_cast)[name = tensor("op_8697_cast")]; + tensor inputs_263_cast = add(x = var_8697_cast, y = inputs_261_cast)[name = tensor("inputs_263_cast")]; + tensor var_8701 = const()[name = tensor("op_8701"), val = tensor([1])]; + tensor channels_mean_263_cast = reduce_mean(axes = var_8701, keep_dims = var_6860, x = inputs_263_cast)[name = tensor("channels_mean_263_cast")]; + tensor zero_mean_263_cast = sub(x = inputs_263_cast, y = channels_mean_263_cast)[name = tensor("zero_mean_263_cast")]; + tensor zero_mean_sq_263_cast = mul(x = zero_mean_263_cast, y = zero_mean_263_cast)[name = tensor("zero_mean_sq_263_cast")]; + tensor var_8705 = const()[name = tensor("op_8705"), val = tensor([1])]; + tensor var_8706_cast = reduce_mean(axes = var_8705, keep_dims = var_6860, x = zero_mean_sq_263_cast)[name = tensor("op_8706_cast")]; + tensor var_8707_to_fp16 = const()[name = tensor("op_8707_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8708_cast = add(x = var_8706_cast, y = var_8707_to_fp16)[name = tensor("op_8708_cast")]; + tensor denom_263_epsilon_0_to_fp16 = const()[name = tensor("denom_263_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_263_cast = rsqrt(epsilon = denom_263_epsilon_0_to_fp16, x = var_8708_cast)[name = tensor("denom_263_cast")]; + tensor out_263_cast = mul(x = zero_mean_263_cast, y = denom_263_cast)[name = tensor("out_263_cast")]; + tensor var_8712_to_fp16 = const()[name = tensor("op_8712_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3244200832)))]; + tensor var_8713_cast = add(x = out_263_cast, y = var_8712_to_fp16)[name = tensor("op_8713_cast")]; + tensor var_8715_to_fp16 = const()[name = tensor("op_8715_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3244203456)))]; + tensor input_515_cast = mul(x = var_8713_cast, y = var_8715_to_fp16)[name = tensor("input_515_cast")]; + tensor var_8723 = const()[name = tensor("op_8723"), val = tensor([1, 1])]; + tensor var_8725 = const()[name = tensor("op_8725"), val = tensor([1, 1])]; + tensor var_8727_pad_type_0 = const()[name = tensor("op_8727_pad_type_0"), val = tensor("custom")]; + tensor var_8727_pad_0 = const()[name = tensor("op_8727_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3244206080)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3270420544)))]; + tensor var_8727_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_8725, groups = var_6865, pad = var_8727_pad_0, pad_type = var_8727_pad_type_0, strides = var_8723, weight = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_515_cast)[name = tensor("op_8727_cast")]; + tensor var_8728_split_sizes_0 = const()[name = tensor("op_8728_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8728_axis_0 = const()[name = tensor("op_8728_axis_0"), val = tensor(1)]; + tensor var_8728_cast_0, tensor var_8728_cast_1 = split(axis = var_8728_axis_0, split_sizes = var_8728_split_sizes_0, x = var_8727_cast)[name = tensor("op_8728_cast")]; + tensor var_8730_mode_0 = const()[name = tensor("op_8730_mode_0"), val = tensor("EXACT")]; + tensor var_8730_cast = gelu(mode = var_8730_mode_0, x = var_8728_cast_1)[name = tensor("op_8730_cast")]; + tensor input_517_cast = mul(x = var_8728_cast_0, y = var_8730_cast)[name = tensor("input_517_cast")]; + tensor var_8734 = const()[name = tensor("op_8734"), val = tensor([1, 1])]; + tensor var_8736 = const()[name = tensor("op_8736"), val = tensor([1, 1])]; + tensor var_8738_pad_type_0 = const()[name = tensor("op_8738_pad_type_0"), val = tensor("custom")]; + tensor var_8738_pad_0 = const()[name = tensor("op_8738_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3270441088)))]; + tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3283548352)))]; + tensor var_8738_cast = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_8736, groups = var_6865, pad = var_8738_pad_0, pad_type = var_8738_pad_type_0, strides = var_8734, weight = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_517_cast)[name = tensor("op_8738_cast")]; + tensor hidden_states_347_cast = add(x = var_8738_cast, y = inputs_263_cast)[name = tensor("hidden_states_347_cast")]; + tensor var_8740 = const()[name = tensor("op_8740"), val = tensor([2, 1280, 32, 32])]; + tensor input_519_cast = reshape(shape = var_8740, x = hidden_states_347_cast)[name = tensor("input_519_cast")]; + tensor var_8744 = const()[name = tensor("op_8744"), val = tensor([1, 1])]; + tensor var_8746 = const()[name = tensor("op_8746"), val = tensor([1, 1])]; + tensor hidden_states_349_pad_type_0 = const()[name = tensor("hidden_states_349_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_349_pad_0 = const()[name = tensor("hidden_states_349_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3283550976)))]; + tensor up_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3286827840)))]; + tensor hidden_states_349_cast = conv(bias = up_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_8746, groups = var_6865, pad = hidden_states_349_pad_0, pad_type = hidden_states_349_pad_type_0, strides = var_8744, weight = up_blocks_0_attentions_0_proj_out_weight_to_fp16, x = input_519_cast)[name = tensor("hidden_states_349_cast")]; + tensor hidden_states_351_cast = add(x = hidden_states_349_cast, y = hidden_states_283_cast)[name = tensor("hidden_states_351_cast")]; + tensor input_521_interleave_0 = const()[name = tensor("input_521_interleave_0"), val = tensor(false)]; + tensor input_521_cast = concat(axis = var_6865, interleave = input_521_interleave_0, values = (hidden_states_351_cast, input_213_cast))[name = tensor("input_521_cast")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([2, 32, 80, 32, 32])]; + tensor reshape_96_cast = reshape(shape = reshape_96_shape_0, x = input_521_cast)[name = tensor("reshape_96_cast")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72_cast = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast)[name = tensor("reduce_mean_72_cast")]; + tensor sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast)[name = tensor("sub_48_cast")]; + tensor square_24_cast = square(x = sub_48_cast)[name = tensor("square_24_cast")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74_cast = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast)[name = tensor("reduce_mean_74_cast")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_48_cast = add(x = reduce_mean_74_cast, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast")]; + tensor sqrt_24_cast = sqrt(x = add_48_cast)[name = tensor("sqrt_24_cast")]; + tensor real_div_24_cast = real_div(x = sub_48_cast, y = sqrt_24_cast)[name = tensor("real_div_24_cast")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([2, 2560, 32, 32])]; + tensor reshape_97_cast = reshape(shape = reshape_97_shape_0, x = real_div_24_cast)[name = tensor("reshape_97_cast")]; + tensor add_49_gamma_0_to_fp16 = const()[name = tensor("add_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3286830464)))]; + tensor add_49_beta_0_to_fp16 = const()[name = tensor("add_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3286835648)))]; + tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_49_cast = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_43_mean_0_to_fp16, variance = add_43_variance_0_to_fp16, x = reshape_97_cast)[name = tensor("add_49_cast")]; + tensor input_525_cast = silu(x = add_49_cast)[name = tensor("input_525_cast")]; + tensor var_8764 = const()[name = tensor("op_8764"), val = tensor([1, 1])]; + tensor var_8766 = const()[name = tensor("op_8766"), val = tensor([1, 1])]; + tensor hidden_states_353_pad_type_0 = const()[name = tensor("hidden_states_353_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_353_pad_0 = const()[name = tensor("hidden_states_353_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3286840832)))]; + tensor up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3345823296)))]; + tensor hidden_states_353_cast = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_8766, groups = var_6865, pad = hidden_states_353_pad_0, pad_type = hidden_states_353_pad_type_0, strides = var_8764, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_525_cast)[name = tensor("hidden_states_353_cast")]; + tensor var_8772 = const()[name = tensor("op_8772"), val = tensor([1, 1])]; + tensor var_8774 = const()[name = tensor("op_8774"), val = tensor([1, 1])]; + tensor temb_19_pad_type_0 = const()[name = tensor("temb_19_pad_type_0"), val = tensor("custom")]; + tensor temb_19_pad_0 = const()[name = tensor("temb_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3345825920)))]; + tensor up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3349102784)))]; + tensor temb_19_cast = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_8774, groups = var_6865, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_8772, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_19_cast")]; + tensor input_529_cast = add(x = hidden_states_353_cast, y = temb_19_cast)[name = tensor("input_529_cast")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_100_cast = reshape(shape = reshape_100_shape_0, x = input_529_cast)[name = tensor("reshape_100_cast")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75_cast = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast)[name = tensor("reduce_mean_75_cast")]; + tensor sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast)[name = tensor("sub_50_cast")]; + tensor square_25_cast = square(x = sub_50_cast)[name = tensor("square_25_cast")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77_cast = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast)[name = tensor("reduce_mean_77_cast")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_50_cast = add(x = reduce_mean_77_cast, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast")]; + tensor sqrt_25_cast = sqrt(x = add_50_cast)[name = tensor("sqrt_25_cast")]; + tensor real_div_25_cast = real_div(x = sub_50_cast, y = sqrt_25_cast)[name = tensor("real_div_25_cast")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_101_cast = reshape(shape = reshape_101_shape_0, x = real_div_25_cast)[name = tensor("reshape_101_cast")]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3349105408)))]; + tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3349108032)))]; + tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_51_cast = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_101_cast)[name = tensor("add_51_cast")]; + tensor input_533_cast = silu(x = add_51_cast)[name = tensor("input_533_cast")]; + tensor var_8784 = const()[name = tensor("op_8784"), val = tensor([1, 1])]; + tensor var_8786 = const()[name = tensor("op_8786"), val = tensor([1, 1])]; + tensor hidden_states_355_pad_type_0 = const()[name = tensor("hidden_states_355_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_355_pad_0 = const()[name = tensor("hidden_states_355_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3349110656)))]; + tensor up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3378601920)))]; + tensor hidden_states_355_cast = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_8786, groups = var_6865, pad = hidden_states_355_pad_0, pad_type = hidden_states_355_pad_type_0, strides = var_8784, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_533_cast)[name = tensor("hidden_states_355_cast")]; + tensor var_8791 = const()[name = tensor("op_8791"), val = tensor([1, 1])]; + tensor var_8793 = const()[name = tensor("op_8793"), val = tensor([1, 1])]; + tensor x_7_pad_type_0 = const()[name = tensor("x_7_pad_type_0"), val = tensor("custom")]; + tensor x_7_pad_0 = const()[name = tensor("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3378604544)))]; + tensor up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3385158208)))]; + tensor x_7_cast = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_8793, groups = var_6865, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_8791, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16, x = input_521_cast)[name = tensor("x_7_cast")]; + tensor hidden_states_357_cast = add(x = x_7_cast, y = hidden_states_355_cast)[name = tensor("hidden_states_357_cast")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_104_cast = reshape(shape = reshape_104_shape_0, x = hidden_states_357_cast)[name = tensor("reshape_104_cast")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78_cast = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast)[name = tensor("reduce_mean_78_cast")]; + tensor sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast)[name = tensor("sub_52_cast")]; + tensor square_26_cast = square(x = sub_52_cast)[name = tensor("square_26_cast")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80_cast = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast)[name = tensor("reduce_mean_80_cast")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_52_cast = add(x = reduce_mean_80_cast, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast")]; + tensor sqrt_26_cast = sqrt(x = add_52_cast)[name = tensor("sqrt_26_cast")]; + tensor real_div_26_cast = real_div(x = sub_52_cast, y = sqrt_26_cast)[name = tensor("real_div_26_cast")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_105_cast = reshape(shape = reshape_105_shape_0, x = real_div_26_cast)[name = tensor("reshape_105_cast")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3385160832)))]; + tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3385163456)))]; + tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_53_cast = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_105_cast)[name = tensor("add_53_cast")]; + tensor var_8831 = const()[name = tensor("op_8831"), val = tensor([1, 1])]; + tensor var_8833 = const()[name = tensor("op_8833"), val = tensor([1, 1])]; + tensor hidden_states_359_pad_type_0 = const()[name = tensor("hidden_states_359_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_359_pad_0 = const()[name = tensor("hidden_states_359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3385166080)))]; + tensor up_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3388442944)))]; + tensor hidden_states_359_cast = conv(bias = up_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_8833, groups = var_6865, pad = hidden_states_359_pad_0, pad_type = hidden_states_359_pad_type_0, strides = var_8831, weight = up_blocks_0_attentions_1_proj_in_weight_to_fp16, x = add_53_cast)[name = tensor("hidden_states_359_cast")]; + tensor var_8838 = const()[name = tensor("op_8838"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_265_cast = reshape(shape = var_8838, x = hidden_states_359_cast)[name = tensor("inputs_265_cast")]; + tensor var_8848 = const()[name = tensor("op_8848"), val = tensor([1])]; + tensor channels_mean_265_cast = reduce_mean(axes = var_8848, keep_dims = var_6860, x = inputs_265_cast)[name = tensor("channels_mean_265_cast")]; + tensor zero_mean_265_cast = sub(x = inputs_265_cast, y = channels_mean_265_cast)[name = tensor("zero_mean_265_cast")]; + tensor zero_mean_sq_265_cast = mul(x = zero_mean_265_cast, y = zero_mean_265_cast)[name = tensor("zero_mean_sq_265_cast")]; + tensor var_8852 = const()[name = tensor("op_8852"), val = tensor([1])]; + tensor var_8853_cast = reduce_mean(axes = var_8852, keep_dims = var_6860, x = zero_mean_sq_265_cast)[name = tensor("op_8853_cast")]; + tensor var_8854_to_fp16 = const()[name = tensor("op_8854_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8855_cast = add(x = var_8853_cast, y = var_8854_to_fp16)[name = tensor("op_8855_cast")]; + tensor denom_265_epsilon_0_to_fp16 = const()[name = tensor("denom_265_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_265_cast = rsqrt(epsilon = denom_265_epsilon_0_to_fp16, x = var_8855_cast)[name = tensor("denom_265_cast")]; + tensor out_265_cast = mul(x = zero_mean_265_cast, y = denom_265_cast)[name = tensor("out_265_cast")]; + tensor var_8859_to_fp16 = const()[name = tensor("op_8859_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3388445568)))]; + tensor var_8860_cast = add(x = out_265_cast, y = var_8859_to_fp16)[name = tensor("op_8860_cast")]; + tensor var_8862_to_fp16 = const()[name = tensor("op_8862_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3388448192)))]; + tensor hidden_states_361_cast = mul(x = var_8860_cast, y = var_8862_to_fp16)[name = tensor("hidden_states_361_cast")]; + tensor var_8869 = const()[name = tensor("op_8869"), val = tensor([1, 1])]; + tensor var_8871 = const()[name = tensor("op_8871"), val = tensor([1, 1])]; + tensor q_177_pad_type_0 = const()[name = tensor("q_177_pad_type_0"), val = tensor("custom")]; + tensor q_177_pad_0 = const()[name = tensor("q_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3388450816)))]; + tensor q_177_cast = conv(dilations = var_8871, groups = var_6865, pad = q_177_pad_0, pad_type = q_177_pad_type_0, strides = var_8869, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_361_cast)[name = tensor("q_177_cast")]; + tensor var_8875 = const()[name = tensor("op_8875"), val = tensor([1, 1])]; + tensor var_8877 = const()[name = tensor("op_8877"), val = tensor([1, 1])]; + tensor k_177_pad_type_0 = const()[name = tensor("k_177_pad_type_0"), val = tensor("custom")]; + tensor k_177_pad_0 = const()[name = tensor("k_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3391727680)))]; + tensor k_177_cast = conv(dilations = var_8877, groups = var_6865, pad = k_177_pad_0, pad_type = k_177_pad_type_0, strides = var_8875, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_361_cast)[name = tensor("k_177_cast")]; + tensor var_8881 = const()[name = tensor("op_8881"), val = tensor([1, 1])]; + tensor var_8883 = const()[name = tensor("op_8883"), val = tensor([1, 1])]; + tensor v_177_pad_type_0 = const()[name = tensor("v_177_pad_type_0"), val = tensor("custom")]; + tensor v_177_pad_0 = const()[name = tensor("v_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3395004544)))]; + tensor v_177_cast = conv(dilations = var_8883, groups = var_6865, pad = v_177_pad_0, pad_type = v_177_pad_type_0, strides = var_8881, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_361_cast)[name = tensor("v_177_cast")]; + tensor var_8887 = const()[name = tensor("op_8887"), val = tensor([2, 20, 64, -1])]; + tensor var_8888_cast = reshape(shape = var_8887, x = q_177_cast)[name = tensor("op_8888_cast")]; + tensor var_8889 = const()[name = tensor("op_8889"), val = tensor([2, 20, 64, -1])]; + tensor var_8890_cast = reshape(shape = var_8889, x = k_177_cast)[name = tensor("op_8890_cast")]; + tensor var_8891 = const()[name = tensor("op_8891"), val = tensor([2, 20, 64, -1])]; + tensor var_8892_cast = reshape(shape = var_8891, x = v_177_cast)[name = tensor("op_8892_cast")]; + tensor attn_weights_353_transpose_x_0 = const()[name = tensor("attn_weights_353_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_353_transpose_y_0 = const()[name = tensor("attn_weights_353_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_353_cast = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8888_cast, y = var_8890_cast)[name = tensor("attn_weights_353_cast")]; + tensor attn_weights_355_cast = mul(x = attn_weights_353_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_355_cast")]; + tensor var_8896_cast = softmax(axis = var_6849, x = attn_weights_355_cast)[name = tensor("op_8896_cast")]; + tensor attn_177_transpose_x_0 = const()[name = tensor("attn_177_transpose_x_0"), val = tensor(false)]; + tensor attn_177_transpose_y_0 = const()[name = tensor("attn_177_transpose_y_0"), val = tensor(true)]; + tensor attn_177_cast = matmul(transpose_x = attn_177_transpose_x_0, transpose_y = attn_177_transpose_y_0, x = var_8892_cast, y = var_8896_cast)[name = tensor("attn_177_cast")]; + tensor var_8900 = const()[name = tensor("op_8900"), val = tensor([2, 1280, 1, -1])]; + tensor input_537_cast = reshape(shape = var_8900, x = attn_177_cast)[name = tensor("input_537_cast")]; + tensor var_8905 = const()[name = tensor("op_8905"), val = tensor([1, 1])]; + tensor var_8907 = const()[name = tensor("op_8907"), val = tensor([1, 1])]; + tensor var_8909_pad_type_0 = const()[name = tensor("op_8909_pad_type_0"), val = tensor("custom")]; + tensor var_8909_pad_0 = const()[name = tensor("op_8909_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3398281408)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3401558272)))]; + tensor var_8909_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_8907, groups = var_6865, pad = var_8909_pad_0, pad_type = var_8909_pad_type_0, strides = var_8905, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_537_cast)[name = tensor("op_8909_cast")]; + tensor inputs_267_cast = add(x = var_8909_cast, y = inputs_265_cast)[name = tensor("inputs_267_cast")]; + tensor var_8913 = const()[name = tensor("op_8913"), val = tensor([1])]; + tensor channels_mean_267_cast = reduce_mean(axes = var_8913, keep_dims = var_6860, x = inputs_267_cast)[name = tensor("channels_mean_267_cast")]; + tensor zero_mean_267_cast = sub(x = inputs_267_cast, y = channels_mean_267_cast)[name = tensor("zero_mean_267_cast")]; + tensor zero_mean_sq_267_cast = mul(x = zero_mean_267_cast, y = zero_mean_267_cast)[name = tensor("zero_mean_sq_267_cast")]; + tensor var_8917 = const()[name = tensor("op_8917"), val = tensor([1])]; + tensor var_8918_cast = reduce_mean(axes = var_8917, keep_dims = var_6860, x = zero_mean_sq_267_cast)[name = tensor("op_8918_cast")]; + tensor var_8919_to_fp16 = const()[name = tensor("op_8919_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8920_cast = add(x = var_8918_cast, y = var_8919_to_fp16)[name = tensor("op_8920_cast")]; + tensor denom_267_epsilon_0_to_fp16 = const()[name = tensor("denom_267_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_267_cast = rsqrt(epsilon = denom_267_epsilon_0_to_fp16, x = var_8920_cast)[name = tensor("denom_267_cast")]; + tensor out_267_cast = mul(x = zero_mean_267_cast, y = denom_267_cast)[name = tensor("out_267_cast")]; + tensor var_8924_to_fp16 = const()[name = tensor("op_8924_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3401560896)))]; + tensor var_8925_cast = add(x = out_267_cast, y = var_8924_to_fp16)[name = tensor("op_8925_cast")]; + tensor var_8927_to_fp16 = const()[name = tensor("op_8927_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3401563520)))]; + tensor hidden_states_363_cast = mul(x = var_8925_cast, y = var_8927_to_fp16)[name = tensor("hidden_states_363_cast")]; + tensor var_8934 = const()[name = tensor("op_8934"), val = tensor([1, 1])]; + tensor var_8936 = const()[name = tensor("op_8936"), val = tensor([1, 1])]; + tensor q_179_pad_type_0 = const()[name = tensor("q_179_pad_type_0"), val = tensor("custom")]; + tensor q_179_pad_0 = const()[name = tensor("q_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3401566144)))]; + tensor q_179_cast = conv(dilations = var_8936, groups = var_6865, pad = q_179_pad_0, pad_type = q_179_pad_type_0, strides = var_8934, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_363_cast)[name = tensor("q_179_cast")]; + tensor var_8940 = const()[name = tensor("op_8940"), val = tensor([1, 1])]; + tensor var_8942 = const()[name = tensor("op_8942"), val = tensor([1, 1])]; + tensor k_179_pad_type_0 = const()[name = tensor("k_179_pad_type_0"), val = tensor("custom")]; + tensor k_179_pad_0 = const()[name = tensor("k_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3404843008)))]; + tensor k_179_cast = conv(dilations = var_8942, groups = var_6865, pad = k_179_pad_0, pad_type = k_179_pad_type_0, strides = var_8940, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_179_cast")]; + tensor var_8946 = const()[name = tensor("op_8946"), val = tensor([1, 1])]; + tensor var_8948 = const()[name = tensor("op_8948"), val = tensor([1, 1])]; + tensor v_179_pad_type_0 = const()[name = tensor("v_179_pad_type_0"), val = tensor("custom")]; + tensor v_179_pad_0 = const()[name = tensor("v_179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3410085952)))]; + tensor v_179_cast = conv(dilations = var_8948, groups = var_6865, pad = v_179_pad_0, pad_type = v_179_pad_type_0, strides = var_8946, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_179_cast")]; + tensor var_8952 = const()[name = tensor("op_8952"), val = tensor([2, 20, 64, -1])]; + tensor var_8953_cast = reshape(shape = var_8952, x = q_179_cast)[name = tensor("op_8953_cast")]; + tensor var_8954 = const()[name = tensor("op_8954"), val = tensor([2, 20, 64, -1])]; + tensor var_8955_cast = reshape(shape = var_8954, x = k_179_cast)[name = tensor("op_8955_cast")]; + tensor var_8956 = const()[name = tensor("op_8956"), val = tensor([2, 20, 64, -1])]; + tensor var_8957_cast = reshape(shape = var_8956, x = v_179_cast)[name = tensor("op_8957_cast")]; + tensor attn_weights_357_transpose_x_0 = const()[name = tensor("attn_weights_357_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_357_transpose_y_0 = const()[name = tensor("attn_weights_357_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_357_cast = matmul(transpose_x = attn_weights_357_transpose_x_0, transpose_y = attn_weights_357_transpose_y_0, x = var_8953_cast, y = var_8955_cast)[name = tensor("attn_weights_357_cast")]; + tensor attn_weights_359_cast = mul(x = attn_weights_357_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_359_cast")]; + tensor var_8961_cast = softmax(axis = var_6849, x = attn_weights_359_cast)[name = tensor("op_8961_cast")]; + tensor attn_179_transpose_x_0 = const()[name = tensor("attn_179_transpose_x_0"), val = tensor(false)]; + tensor attn_179_transpose_y_0 = const()[name = tensor("attn_179_transpose_y_0"), val = tensor(true)]; + tensor attn_179_cast = matmul(transpose_x = attn_179_transpose_x_0, transpose_y = attn_179_transpose_y_0, x = var_8957_cast, y = var_8961_cast)[name = tensor("attn_179_cast")]; + tensor var_8965 = const()[name = tensor("op_8965"), val = tensor([2, 1280, 1, -1])]; + tensor input_539_cast = reshape(shape = var_8965, x = attn_179_cast)[name = tensor("input_539_cast")]; + tensor var_8970 = const()[name = tensor("op_8970"), val = tensor([1, 1])]; + tensor var_8972 = const()[name = tensor("op_8972"), val = tensor([1, 1])]; + tensor var_8974_pad_type_0 = const()[name = tensor("op_8974_pad_type_0"), val = tensor("custom")]; + tensor var_8974_pad_0 = const()[name = tensor("op_8974_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3415328896)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3418605760)))]; + tensor var_8974_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_8972, groups = var_6865, pad = var_8974_pad_0, pad_type = var_8974_pad_type_0, strides = var_8970, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_539_cast)[name = tensor("op_8974_cast")]; + tensor inputs_269_cast = add(x = var_8974_cast, y = inputs_267_cast)[name = tensor("inputs_269_cast")]; + tensor var_8978 = const()[name = tensor("op_8978"), val = tensor([1])]; + tensor channels_mean_269_cast = reduce_mean(axes = var_8978, keep_dims = var_6860, x = inputs_269_cast)[name = tensor("channels_mean_269_cast")]; + tensor zero_mean_269_cast = sub(x = inputs_269_cast, y = channels_mean_269_cast)[name = tensor("zero_mean_269_cast")]; + tensor zero_mean_sq_269_cast = mul(x = zero_mean_269_cast, y = zero_mean_269_cast)[name = tensor("zero_mean_sq_269_cast")]; + tensor var_8982 = const()[name = tensor("op_8982"), val = tensor([1])]; + tensor var_8983_cast = reduce_mean(axes = var_8982, keep_dims = var_6860, x = zero_mean_sq_269_cast)[name = tensor("op_8983_cast")]; + tensor var_8984_to_fp16 = const()[name = tensor("op_8984_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8985_cast = add(x = var_8983_cast, y = var_8984_to_fp16)[name = tensor("op_8985_cast")]; + tensor denom_269_epsilon_0_to_fp16 = const()[name = tensor("denom_269_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_269_cast = rsqrt(epsilon = denom_269_epsilon_0_to_fp16, x = var_8985_cast)[name = tensor("denom_269_cast")]; + tensor out_269_cast = mul(x = zero_mean_269_cast, y = denom_269_cast)[name = tensor("out_269_cast")]; + tensor var_8989_to_fp16 = const()[name = tensor("op_8989_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3418608384)))]; + tensor var_8990_cast = add(x = out_269_cast, y = var_8989_to_fp16)[name = tensor("op_8990_cast")]; + tensor var_8992_to_fp16 = const()[name = tensor("op_8992_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3418611008)))]; + tensor input_541_cast = mul(x = var_8990_cast, y = var_8992_to_fp16)[name = tensor("input_541_cast")]; + tensor var_9000 = const()[name = tensor("op_9000"), val = tensor([1, 1])]; + tensor var_9002 = const()[name = tensor("op_9002"), val = tensor([1, 1])]; + tensor var_9004_pad_type_0 = const()[name = tensor("op_9004_pad_type_0"), val = tensor("custom")]; + tensor var_9004_pad_0 = const()[name = tensor("op_9004_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3418613632)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3444828096)))]; + tensor var_9004_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_9002, groups = var_6865, pad = var_9004_pad_0, pad_type = var_9004_pad_type_0, strides = var_9000, weight = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_541_cast)[name = tensor("op_9004_cast")]; + tensor var_9005_split_sizes_0 = const()[name = tensor("op_9005_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9005_axis_0 = const()[name = tensor("op_9005_axis_0"), val = tensor(1)]; + tensor var_9005_cast_0, tensor var_9005_cast_1 = split(axis = var_9005_axis_0, split_sizes = var_9005_split_sizes_0, x = var_9004_cast)[name = tensor("op_9005_cast")]; + tensor var_9007_mode_0 = const()[name = tensor("op_9007_mode_0"), val = tensor("EXACT")]; + tensor var_9007_cast = gelu(mode = var_9007_mode_0, x = var_9005_cast_1)[name = tensor("op_9007_cast")]; + tensor input_543_cast = mul(x = var_9005_cast_0, y = var_9007_cast)[name = tensor("input_543_cast")]; + tensor var_9011 = const()[name = tensor("op_9011"), val = tensor([1, 1])]; + tensor var_9013 = const()[name = tensor("op_9013"), val = tensor([1, 1])]; + tensor var_9015_pad_type_0 = const()[name = tensor("op_9015_pad_type_0"), val = tensor("custom")]; + tensor var_9015_pad_0 = const()[name = tensor("op_9015_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3444848640)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3457955904)))]; + tensor var_9015_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_9013, groups = var_6865, pad = var_9015_pad_0, pad_type = var_9015_pad_type_0, strides = var_9011, weight = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_543_cast)[name = tensor("op_9015_cast")]; + tensor inputs_271_cast = add(x = var_9015_cast, y = inputs_269_cast)[name = tensor("inputs_271_cast")]; + tensor var_9025 = const()[name = tensor("op_9025"), val = tensor([1])]; + tensor channels_mean_271_cast = reduce_mean(axes = var_9025, keep_dims = var_6860, x = inputs_271_cast)[name = tensor("channels_mean_271_cast")]; + tensor zero_mean_271_cast = sub(x = inputs_271_cast, y = channels_mean_271_cast)[name = tensor("zero_mean_271_cast")]; + tensor zero_mean_sq_271_cast = mul(x = zero_mean_271_cast, y = zero_mean_271_cast)[name = tensor("zero_mean_sq_271_cast")]; + tensor var_9029 = const()[name = tensor("op_9029"), val = tensor([1])]; + tensor var_9030_cast = reduce_mean(axes = var_9029, keep_dims = var_6860, x = zero_mean_sq_271_cast)[name = tensor("op_9030_cast")]; + tensor var_9031_to_fp16 = const()[name = tensor("op_9031_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9032_cast = add(x = var_9030_cast, y = var_9031_to_fp16)[name = tensor("op_9032_cast")]; + tensor denom_271_epsilon_0_to_fp16 = const()[name = tensor("denom_271_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_271_cast = rsqrt(epsilon = denom_271_epsilon_0_to_fp16, x = var_9032_cast)[name = tensor("denom_271_cast")]; + tensor out_271_cast = mul(x = zero_mean_271_cast, y = denom_271_cast)[name = tensor("out_271_cast")]; + tensor var_9036_to_fp16 = const()[name = tensor("op_9036_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3457958528)))]; + tensor var_9037_cast = add(x = out_271_cast, y = var_9036_to_fp16)[name = tensor("op_9037_cast")]; + tensor var_9039_to_fp16 = const()[name = tensor("op_9039_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3457961152)))]; + tensor hidden_states_367_cast = mul(x = var_9037_cast, y = var_9039_to_fp16)[name = tensor("hidden_states_367_cast")]; + tensor var_9046 = const()[name = tensor("op_9046"), val = tensor([1, 1])]; + tensor var_9048 = const()[name = tensor("op_9048"), val = tensor([1, 1])]; + tensor q_181_pad_type_0 = const()[name = tensor("q_181_pad_type_0"), val = tensor("custom")]; + tensor q_181_pad_0 = const()[name = tensor("q_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3457963776)))]; + tensor q_181_cast = conv(dilations = var_9048, groups = var_6865, pad = q_181_pad_0, pad_type = q_181_pad_type_0, strides = var_9046, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_367_cast)[name = tensor("q_181_cast")]; + tensor var_9052 = const()[name = tensor("op_9052"), val = tensor([1, 1])]; + tensor var_9054 = const()[name = tensor("op_9054"), val = tensor([1, 1])]; + tensor k_181_pad_type_0 = const()[name = tensor("k_181_pad_type_0"), val = tensor("custom")]; + tensor k_181_pad_0 = const()[name = tensor("k_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3461240640)))]; + tensor k_181_cast = conv(dilations = var_9054, groups = var_6865, pad = k_181_pad_0, pad_type = k_181_pad_type_0, strides = var_9052, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_367_cast)[name = tensor("k_181_cast")]; + tensor var_9058 = const()[name = tensor("op_9058"), val = tensor([1, 1])]; + tensor var_9060 = const()[name = tensor("op_9060"), val = tensor([1, 1])]; + tensor v_181_pad_type_0 = const()[name = tensor("v_181_pad_type_0"), val = tensor("custom")]; + tensor v_181_pad_0 = const()[name = tensor("v_181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3464517504)))]; + tensor v_181_cast = conv(dilations = var_9060, groups = var_6865, pad = v_181_pad_0, pad_type = v_181_pad_type_0, strides = var_9058, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_367_cast)[name = tensor("v_181_cast")]; + tensor var_9064 = const()[name = tensor("op_9064"), val = tensor([2, 20, 64, -1])]; + tensor var_9065_cast = reshape(shape = var_9064, x = q_181_cast)[name = tensor("op_9065_cast")]; + tensor var_9066 = const()[name = tensor("op_9066"), val = tensor([2, 20, 64, -1])]; + tensor var_9067_cast = reshape(shape = var_9066, x = k_181_cast)[name = tensor("op_9067_cast")]; + tensor var_9068 = const()[name = tensor("op_9068"), val = tensor([2, 20, 64, -1])]; + tensor var_9069_cast = reshape(shape = var_9068, x = v_181_cast)[name = tensor("op_9069_cast")]; + tensor attn_weights_361_transpose_x_0 = const()[name = tensor("attn_weights_361_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_361_transpose_y_0 = const()[name = tensor("attn_weights_361_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_361_cast = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_9065_cast, y = var_9067_cast)[name = tensor("attn_weights_361_cast")]; + tensor attn_weights_363_cast = mul(x = attn_weights_361_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_363_cast")]; + tensor var_9073_cast = softmax(axis = var_6849, x = attn_weights_363_cast)[name = tensor("op_9073_cast")]; + tensor attn_181_transpose_x_0 = const()[name = tensor("attn_181_transpose_x_0"), val = tensor(false)]; + tensor attn_181_transpose_y_0 = const()[name = tensor("attn_181_transpose_y_0"), val = tensor(true)]; + tensor attn_181_cast = matmul(transpose_x = attn_181_transpose_x_0, transpose_y = attn_181_transpose_y_0, x = var_9069_cast, y = var_9073_cast)[name = tensor("attn_181_cast")]; + tensor var_9077 = const()[name = tensor("op_9077"), val = tensor([2, 1280, 1, -1])]; + tensor input_545_cast = reshape(shape = var_9077, x = attn_181_cast)[name = tensor("input_545_cast")]; + tensor var_9082 = const()[name = tensor("op_9082"), val = tensor([1, 1])]; + tensor var_9084 = const()[name = tensor("op_9084"), val = tensor([1, 1])]; + tensor var_9086_pad_type_0 = const()[name = tensor("op_9086_pad_type_0"), val = tensor("custom")]; + tensor var_9086_pad_0 = const()[name = tensor("op_9086_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3467794368)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3471071232)))]; + tensor var_9086_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_9084, groups = var_6865, pad = var_9086_pad_0, pad_type = var_9086_pad_type_0, strides = var_9082, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_545_cast)[name = tensor("op_9086_cast")]; + tensor inputs_273_cast = add(x = var_9086_cast, y = inputs_271_cast)[name = tensor("inputs_273_cast")]; + tensor var_9090 = const()[name = tensor("op_9090"), val = tensor([1])]; + tensor channels_mean_273_cast = reduce_mean(axes = var_9090, keep_dims = var_6860, x = inputs_273_cast)[name = tensor("channels_mean_273_cast")]; + tensor zero_mean_273_cast = sub(x = inputs_273_cast, y = channels_mean_273_cast)[name = tensor("zero_mean_273_cast")]; + tensor zero_mean_sq_273_cast = mul(x = zero_mean_273_cast, y = zero_mean_273_cast)[name = tensor("zero_mean_sq_273_cast")]; + tensor var_9094 = const()[name = tensor("op_9094"), val = tensor([1])]; + tensor var_9095_cast = reduce_mean(axes = var_9094, keep_dims = var_6860, x = zero_mean_sq_273_cast)[name = tensor("op_9095_cast")]; + tensor var_9096_to_fp16 = const()[name = tensor("op_9096_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9097_cast = add(x = var_9095_cast, y = var_9096_to_fp16)[name = tensor("op_9097_cast")]; + tensor denom_273_epsilon_0_to_fp16 = const()[name = tensor("denom_273_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_273_cast = rsqrt(epsilon = denom_273_epsilon_0_to_fp16, x = var_9097_cast)[name = tensor("denom_273_cast")]; + tensor out_273_cast = mul(x = zero_mean_273_cast, y = denom_273_cast)[name = tensor("out_273_cast")]; + tensor var_9101_to_fp16 = const()[name = tensor("op_9101_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3471073856)))]; + tensor var_9102_cast = add(x = out_273_cast, y = var_9101_to_fp16)[name = tensor("op_9102_cast")]; + tensor var_9104_to_fp16 = const()[name = tensor("op_9104_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3471076480)))]; + tensor hidden_states_369_cast = mul(x = var_9102_cast, y = var_9104_to_fp16)[name = tensor("hidden_states_369_cast")]; + tensor var_9111 = const()[name = tensor("op_9111"), val = tensor([1, 1])]; + tensor var_9113 = const()[name = tensor("op_9113"), val = tensor([1, 1])]; + tensor q_183_pad_type_0 = const()[name = tensor("q_183_pad_type_0"), val = tensor("custom")]; + tensor q_183_pad_0 = const()[name = tensor("q_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3471079104)))]; + tensor q_183_cast = conv(dilations = var_9113, groups = var_6865, pad = q_183_pad_0, pad_type = q_183_pad_type_0, strides = var_9111, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_369_cast)[name = tensor("q_183_cast")]; + tensor var_9117 = const()[name = tensor("op_9117"), val = tensor([1, 1])]; + tensor var_9119 = const()[name = tensor("op_9119"), val = tensor([1, 1])]; + tensor k_183_pad_type_0 = const()[name = tensor("k_183_pad_type_0"), val = tensor("custom")]; + tensor k_183_pad_0 = const()[name = tensor("k_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3474355968)))]; + tensor k_183_cast = conv(dilations = var_9119, groups = var_6865, pad = k_183_pad_0, pad_type = k_183_pad_type_0, strides = var_9117, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_183_cast")]; + tensor var_9123 = const()[name = tensor("op_9123"), val = tensor([1, 1])]; + tensor var_9125 = const()[name = tensor("op_9125"), val = tensor([1, 1])]; + tensor v_183_pad_type_0 = const()[name = tensor("v_183_pad_type_0"), val = tensor("custom")]; + tensor v_183_pad_0 = const()[name = tensor("v_183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3479598912)))]; + tensor v_183_cast = conv(dilations = var_9125, groups = var_6865, pad = v_183_pad_0, pad_type = v_183_pad_type_0, strides = var_9123, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_183_cast")]; + tensor var_9129 = const()[name = tensor("op_9129"), val = tensor([2, 20, 64, -1])]; + tensor var_9130_cast = reshape(shape = var_9129, x = q_183_cast)[name = tensor("op_9130_cast")]; + tensor var_9131 = const()[name = tensor("op_9131"), val = tensor([2, 20, 64, -1])]; + tensor var_9132_cast = reshape(shape = var_9131, x = k_183_cast)[name = tensor("op_9132_cast")]; + tensor var_9133 = const()[name = tensor("op_9133"), val = tensor([2, 20, 64, -1])]; + tensor var_9134_cast = reshape(shape = var_9133, x = v_183_cast)[name = tensor("op_9134_cast")]; + tensor attn_weights_365_transpose_x_0 = const()[name = tensor("attn_weights_365_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_365_transpose_y_0 = const()[name = tensor("attn_weights_365_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_365_cast = matmul(transpose_x = attn_weights_365_transpose_x_0, transpose_y = attn_weights_365_transpose_y_0, x = var_9130_cast, y = var_9132_cast)[name = tensor("attn_weights_365_cast")]; + tensor attn_weights_367_cast = mul(x = attn_weights_365_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_367_cast")]; + tensor var_9138_cast = softmax(axis = var_6849, x = attn_weights_367_cast)[name = tensor("op_9138_cast")]; + tensor attn_183_transpose_x_0 = const()[name = tensor("attn_183_transpose_x_0"), val = tensor(false)]; + tensor attn_183_transpose_y_0 = const()[name = tensor("attn_183_transpose_y_0"), val = tensor(true)]; + tensor attn_183_cast = matmul(transpose_x = attn_183_transpose_x_0, transpose_y = attn_183_transpose_y_0, x = var_9134_cast, y = var_9138_cast)[name = tensor("attn_183_cast")]; + tensor var_9142 = const()[name = tensor("op_9142"), val = tensor([2, 1280, 1, -1])]; + tensor input_547_cast = reshape(shape = var_9142, x = attn_183_cast)[name = tensor("input_547_cast")]; + tensor var_9147 = const()[name = tensor("op_9147"), val = tensor([1, 1])]; + tensor var_9149 = const()[name = tensor("op_9149"), val = tensor([1, 1])]; + tensor var_9151_pad_type_0 = const()[name = tensor("op_9151_pad_type_0"), val = tensor("custom")]; + tensor var_9151_pad_0 = const()[name = tensor("op_9151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3484841856)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3488118720)))]; + tensor var_9151_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_9149, groups = var_6865, pad = var_9151_pad_0, pad_type = var_9151_pad_type_0, strides = var_9147, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_547_cast)[name = tensor("op_9151_cast")]; + tensor inputs_275_cast = add(x = var_9151_cast, y = inputs_273_cast)[name = tensor("inputs_275_cast")]; + tensor var_9155 = const()[name = tensor("op_9155"), val = tensor([1])]; + tensor channels_mean_275_cast = reduce_mean(axes = var_9155, keep_dims = var_6860, x = inputs_275_cast)[name = tensor("channels_mean_275_cast")]; + tensor zero_mean_275_cast = sub(x = inputs_275_cast, y = channels_mean_275_cast)[name = tensor("zero_mean_275_cast")]; + tensor zero_mean_sq_275_cast = mul(x = zero_mean_275_cast, y = zero_mean_275_cast)[name = tensor("zero_mean_sq_275_cast")]; + tensor var_9159 = const()[name = tensor("op_9159"), val = tensor([1])]; + tensor var_9160_cast = reduce_mean(axes = var_9159, keep_dims = var_6860, x = zero_mean_sq_275_cast)[name = tensor("op_9160_cast")]; + tensor var_9161_to_fp16 = const()[name = tensor("op_9161_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9162_cast = add(x = var_9160_cast, y = var_9161_to_fp16)[name = tensor("op_9162_cast")]; + tensor denom_275_epsilon_0_to_fp16 = const()[name = tensor("denom_275_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_275_cast = rsqrt(epsilon = denom_275_epsilon_0_to_fp16, x = var_9162_cast)[name = tensor("denom_275_cast")]; + tensor out_275_cast = mul(x = zero_mean_275_cast, y = denom_275_cast)[name = tensor("out_275_cast")]; + tensor var_9166_to_fp16 = const()[name = tensor("op_9166_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3488121344)))]; + tensor var_9167_cast = add(x = out_275_cast, y = var_9166_to_fp16)[name = tensor("op_9167_cast")]; + tensor var_9169_to_fp16 = const()[name = tensor("op_9169_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3488123968)))]; + tensor input_549_cast = mul(x = var_9167_cast, y = var_9169_to_fp16)[name = tensor("input_549_cast")]; + tensor var_9177 = const()[name = tensor("op_9177"), val = tensor([1, 1])]; + tensor var_9179 = const()[name = tensor("op_9179"), val = tensor([1, 1])]; + tensor var_9181_pad_type_0 = const()[name = tensor("op_9181_pad_type_0"), val = tensor("custom")]; + tensor var_9181_pad_0 = const()[name = tensor("op_9181_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3488126592)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3514341056)))]; + tensor var_9181_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_9179, groups = var_6865, pad = var_9181_pad_0, pad_type = var_9181_pad_type_0, strides = var_9177, weight = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_549_cast)[name = tensor("op_9181_cast")]; + tensor var_9182_split_sizes_0 = const()[name = tensor("op_9182_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9182_axis_0 = const()[name = tensor("op_9182_axis_0"), val = tensor(1)]; + tensor var_9182_cast_0, tensor var_9182_cast_1 = split(axis = var_9182_axis_0, split_sizes = var_9182_split_sizes_0, x = var_9181_cast)[name = tensor("op_9182_cast")]; + tensor var_9184_mode_0 = const()[name = tensor("op_9184_mode_0"), val = tensor("EXACT")]; + tensor var_9184_cast = gelu(mode = var_9184_mode_0, x = var_9182_cast_1)[name = tensor("op_9184_cast")]; + tensor input_551_cast = mul(x = var_9182_cast_0, y = var_9184_cast)[name = tensor("input_551_cast")]; + tensor var_9188 = const()[name = tensor("op_9188"), val = tensor([1, 1])]; + tensor var_9190 = const()[name = tensor("op_9190"), val = tensor([1, 1])]; + tensor var_9192_pad_type_0 = const()[name = tensor("op_9192_pad_type_0"), val = tensor("custom")]; + tensor var_9192_pad_0 = const()[name = tensor("op_9192_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3514361600)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3527468864)))]; + tensor var_9192_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_9190, groups = var_6865, pad = var_9192_pad_0, pad_type = var_9192_pad_type_0, strides = var_9188, weight = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_551_cast)[name = tensor("op_9192_cast")]; + tensor inputs_277_cast = add(x = var_9192_cast, y = inputs_275_cast)[name = tensor("inputs_277_cast")]; + tensor var_9202 = const()[name = tensor("op_9202"), val = tensor([1])]; + tensor channels_mean_277_cast = reduce_mean(axes = var_9202, keep_dims = var_6860, x = inputs_277_cast)[name = tensor("channels_mean_277_cast")]; + tensor zero_mean_277_cast = sub(x = inputs_277_cast, y = channels_mean_277_cast)[name = tensor("zero_mean_277_cast")]; + tensor zero_mean_sq_277_cast = mul(x = zero_mean_277_cast, y = zero_mean_277_cast)[name = tensor("zero_mean_sq_277_cast")]; + tensor var_9206 = const()[name = tensor("op_9206"), val = tensor([1])]; + tensor var_9207_cast = reduce_mean(axes = var_9206, keep_dims = var_6860, x = zero_mean_sq_277_cast)[name = tensor("op_9207_cast")]; + tensor var_9208_to_fp16 = const()[name = tensor("op_9208_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9209_cast = add(x = var_9207_cast, y = var_9208_to_fp16)[name = tensor("op_9209_cast")]; + tensor denom_277_epsilon_0_to_fp16 = const()[name = tensor("denom_277_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_277_cast = rsqrt(epsilon = denom_277_epsilon_0_to_fp16, x = var_9209_cast)[name = tensor("denom_277_cast")]; + tensor out_277_cast = mul(x = zero_mean_277_cast, y = denom_277_cast)[name = tensor("out_277_cast")]; + tensor var_9213_to_fp16 = const()[name = tensor("op_9213_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3527471488)))]; + tensor var_9214_cast = add(x = out_277_cast, y = var_9213_to_fp16)[name = tensor("op_9214_cast")]; + tensor var_9216_to_fp16 = const()[name = tensor("op_9216_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3527474112)))]; + tensor hidden_states_373_cast = mul(x = var_9214_cast, y = var_9216_to_fp16)[name = tensor("hidden_states_373_cast")]; + tensor var_9223 = const()[name = tensor("op_9223"), val = tensor([1, 1])]; + tensor var_9225 = const()[name = tensor("op_9225"), val = tensor([1, 1])]; + tensor q_185_pad_type_0 = const()[name = tensor("q_185_pad_type_0"), val = tensor("custom")]; + tensor q_185_pad_0 = const()[name = tensor("q_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3527476736)))]; + tensor q_185_cast = conv(dilations = var_9225, groups = var_6865, pad = q_185_pad_0, pad_type = q_185_pad_type_0, strides = var_9223, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_373_cast)[name = tensor("q_185_cast")]; + tensor var_9229 = const()[name = tensor("op_9229"), val = tensor([1, 1])]; + tensor var_9231 = const()[name = tensor("op_9231"), val = tensor([1, 1])]; + tensor k_185_pad_type_0 = const()[name = tensor("k_185_pad_type_0"), val = tensor("custom")]; + tensor k_185_pad_0 = const()[name = tensor("k_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3530753600)))]; + tensor k_185_cast = conv(dilations = var_9231, groups = var_6865, pad = k_185_pad_0, pad_type = k_185_pad_type_0, strides = var_9229, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_373_cast)[name = tensor("k_185_cast")]; + tensor var_9235 = const()[name = tensor("op_9235"), val = tensor([1, 1])]; + tensor var_9237 = const()[name = tensor("op_9237"), val = tensor([1, 1])]; + tensor v_185_pad_type_0 = const()[name = tensor("v_185_pad_type_0"), val = tensor("custom")]; + tensor v_185_pad_0 = const()[name = tensor("v_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3534030464)))]; + tensor v_185_cast = conv(dilations = var_9237, groups = var_6865, pad = v_185_pad_0, pad_type = v_185_pad_type_0, strides = var_9235, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_373_cast)[name = tensor("v_185_cast")]; + tensor var_9241 = const()[name = tensor("op_9241"), val = tensor([2, 20, 64, -1])]; + tensor var_9242_cast = reshape(shape = var_9241, x = q_185_cast)[name = tensor("op_9242_cast")]; + tensor var_9243 = const()[name = tensor("op_9243"), val = tensor([2, 20, 64, -1])]; + tensor var_9244_cast = reshape(shape = var_9243, x = k_185_cast)[name = tensor("op_9244_cast")]; + tensor var_9245 = const()[name = tensor("op_9245"), val = tensor([2, 20, 64, -1])]; + tensor var_9246_cast = reshape(shape = var_9245, x = v_185_cast)[name = tensor("op_9246_cast")]; + tensor attn_weights_369_transpose_x_0 = const()[name = tensor("attn_weights_369_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_369_transpose_y_0 = const()[name = tensor("attn_weights_369_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_369_cast = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_9242_cast, y = var_9244_cast)[name = tensor("attn_weights_369_cast")]; + tensor attn_weights_371_cast = mul(x = attn_weights_369_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_371_cast")]; + tensor var_9250_cast = softmax(axis = var_6849, x = attn_weights_371_cast)[name = tensor("op_9250_cast")]; + tensor attn_185_transpose_x_0 = const()[name = tensor("attn_185_transpose_x_0"), val = tensor(false)]; + tensor attn_185_transpose_y_0 = const()[name = tensor("attn_185_transpose_y_0"), val = tensor(true)]; + tensor attn_185_cast = matmul(transpose_x = attn_185_transpose_x_0, transpose_y = attn_185_transpose_y_0, x = var_9246_cast, y = var_9250_cast)[name = tensor("attn_185_cast")]; + tensor var_9254 = const()[name = tensor("op_9254"), val = tensor([2, 1280, 1, -1])]; + tensor input_553_cast = reshape(shape = var_9254, x = attn_185_cast)[name = tensor("input_553_cast")]; + tensor var_9259 = const()[name = tensor("op_9259"), val = tensor([1, 1])]; + tensor var_9261 = const()[name = tensor("op_9261"), val = tensor([1, 1])]; + tensor var_9263_pad_type_0 = const()[name = tensor("op_9263_pad_type_0"), val = tensor("custom")]; + tensor var_9263_pad_0 = const()[name = tensor("op_9263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3537307328)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3540584192)))]; + tensor var_9263_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_9261, groups = var_6865, pad = var_9263_pad_0, pad_type = var_9263_pad_type_0, strides = var_9259, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_553_cast)[name = tensor("op_9263_cast")]; + tensor inputs_279_cast = add(x = var_9263_cast, y = inputs_277_cast)[name = tensor("inputs_279_cast")]; + tensor var_9267 = const()[name = tensor("op_9267"), val = tensor([1])]; + tensor channels_mean_279_cast = reduce_mean(axes = var_9267, keep_dims = var_6860, x = inputs_279_cast)[name = tensor("channels_mean_279_cast")]; + tensor zero_mean_279_cast = sub(x = inputs_279_cast, y = channels_mean_279_cast)[name = tensor("zero_mean_279_cast")]; + tensor zero_mean_sq_279_cast = mul(x = zero_mean_279_cast, y = zero_mean_279_cast)[name = tensor("zero_mean_sq_279_cast")]; + tensor var_9271 = const()[name = tensor("op_9271"), val = tensor([1])]; + tensor var_9272_cast = reduce_mean(axes = var_9271, keep_dims = var_6860, x = zero_mean_sq_279_cast)[name = tensor("op_9272_cast")]; + tensor var_9273_to_fp16 = const()[name = tensor("op_9273_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9274_cast = add(x = var_9272_cast, y = var_9273_to_fp16)[name = tensor("op_9274_cast")]; + tensor denom_279_epsilon_0_to_fp16 = const()[name = tensor("denom_279_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_279_cast = rsqrt(epsilon = denom_279_epsilon_0_to_fp16, x = var_9274_cast)[name = tensor("denom_279_cast")]; + tensor out_279_cast = mul(x = zero_mean_279_cast, y = denom_279_cast)[name = tensor("out_279_cast")]; + tensor var_9278_to_fp16 = const()[name = tensor("op_9278_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3540586816)))]; + tensor var_9279_cast = add(x = out_279_cast, y = var_9278_to_fp16)[name = tensor("op_9279_cast")]; + tensor var_9281_to_fp16 = const()[name = tensor("op_9281_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3540589440)))]; + tensor hidden_states_375_cast = mul(x = var_9279_cast, y = var_9281_to_fp16)[name = tensor("hidden_states_375_cast")]; + tensor var_9288 = const()[name = tensor("op_9288"), val = tensor([1, 1])]; + tensor var_9290 = const()[name = tensor("op_9290"), val = tensor([1, 1])]; + tensor q_187_pad_type_0 = const()[name = tensor("q_187_pad_type_0"), val = tensor("custom")]; + tensor q_187_pad_0 = const()[name = tensor("q_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3540592064)))]; + tensor q_187_cast = conv(dilations = var_9290, groups = var_6865, pad = q_187_pad_0, pad_type = q_187_pad_type_0, strides = var_9288, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_375_cast)[name = tensor("q_187_cast")]; + tensor var_9294 = const()[name = tensor("op_9294"), val = tensor([1, 1])]; + tensor var_9296 = const()[name = tensor("op_9296"), val = tensor([1, 1])]; + tensor k_187_pad_type_0 = const()[name = tensor("k_187_pad_type_0"), val = tensor("custom")]; + tensor k_187_pad_0 = const()[name = tensor("k_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3543868928)))]; + tensor k_187_cast = conv(dilations = var_9296, groups = var_6865, pad = k_187_pad_0, pad_type = k_187_pad_type_0, strides = var_9294, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_187_cast")]; + tensor var_9300 = const()[name = tensor("op_9300"), val = tensor([1, 1])]; + tensor var_9302 = const()[name = tensor("op_9302"), val = tensor([1, 1])]; + tensor v_187_pad_type_0 = const()[name = tensor("v_187_pad_type_0"), val = tensor("custom")]; + tensor v_187_pad_0 = const()[name = tensor("v_187_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3549111872)))]; + tensor v_187_cast = conv(dilations = var_9302, groups = var_6865, pad = v_187_pad_0, pad_type = v_187_pad_type_0, strides = var_9300, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_187_cast")]; + tensor var_9306 = const()[name = tensor("op_9306"), val = tensor([2, 20, 64, -1])]; + tensor var_9307_cast = reshape(shape = var_9306, x = q_187_cast)[name = tensor("op_9307_cast")]; + tensor var_9308 = const()[name = tensor("op_9308"), val = tensor([2, 20, 64, -1])]; + tensor var_9309_cast = reshape(shape = var_9308, x = k_187_cast)[name = tensor("op_9309_cast")]; + tensor var_9310 = const()[name = tensor("op_9310"), val = tensor([2, 20, 64, -1])]; + tensor var_9311_cast = reshape(shape = var_9310, x = v_187_cast)[name = tensor("op_9311_cast")]; + tensor attn_weights_373_transpose_x_0 = const()[name = tensor("attn_weights_373_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_373_transpose_y_0 = const()[name = tensor("attn_weights_373_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_373_cast = matmul(transpose_x = attn_weights_373_transpose_x_0, transpose_y = attn_weights_373_transpose_y_0, x = var_9307_cast, y = var_9309_cast)[name = tensor("attn_weights_373_cast")]; + tensor attn_weights_375_cast = mul(x = attn_weights_373_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_375_cast")]; + tensor var_9315_cast = softmax(axis = var_6849, x = attn_weights_375_cast)[name = tensor("op_9315_cast")]; + tensor attn_187_transpose_x_0 = const()[name = tensor("attn_187_transpose_x_0"), val = tensor(false)]; + tensor attn_187_transpose_y_0 = const()[name = tensor("attn_187_transpose_y_0"), val = tensor(true)]; + tensor attn_187_cast = matmul(transpose_x = attn_187_transpose_x_0, transpose_y = attn_187_transpose_y_0, x = var_9311_cast, y = var_9315_cast)[name = tensor("attn_187_cast")]; + tensor var_9319 = const()[name = tensor("op_9319"), val = tensor([2, 1280, 1, -1])]; + tensor input_555_cast = reshape(shape = var_9319, x = attn_187_cast)[name = tensor("input_555_cast")]; + tensor var_9324 = const()[name = tensor("op_9324"), val = tensor([1, 1])]; + tensor var_9326 = const()[name = tensor("op_9326"), val = tensor([1, 1])]; + tensor var_9328_pad_type_0 = const()[name = tensor("op_9328_pad_type_0"), val = tensor("custom")]; + tensor var_9328_pad_0 = const()[name = tensor("op_9328_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3554354816)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3557631680)))]; + tensor var_9328_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_9326, groups = var_6865, pad = var_9328_pad_0, pad_type = var_9328_pad_type_0, strides = var_9324, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_555_cast)[name = tensor("op_9328_cast")]; + tensor inputs_281_cast = add(x = var_9328_cast, y = inputs_279_cast)[name = tensor("inputs_281_cast")]; + tensor var_9332 = const()[name = tensor("op_9332"), val = tensor([1])]; + tensor channels_mean_281_cast = reduce_mean(axes = var_9332, keep_dims = var_6860, x = inputs_281_cast)[name = tensor("channels_mean_281_cast")]; + tensor zero_mean_281_cast = sub(x = inputs_281_cast, y = channels_mean_281_cast)[name = tensor("zero_mean_281_cast")]; + tensor zero_mean_sq_281_cast = mul(x = zero_mean_281_cast, y = zero_mean_281_cast)[name = tensor("zero_mean_sq_281_cast")]; + tensor var_9336 = const()[name = tensor("op_9336"), val = tensor([1])]; + tensor var_9337_cast = reduce_mean(axes = var_9336, keep_dims = var_6860, x = zero_mean_sq_281_cast)[name = tensor("op_9337_cast")]; + tensor var_9338_to_fp16 = const()[name = tensor("op_9338_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9339_cast = add(x = var_9337_cast, y = var_9338_to_fp16)[name = tensor("op_9339_cast")]; + tensor denom_281_epsilon_0_to_fp16 = const()[name = tensor("denom_281_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_281_cast = rsqrt(epsilon = denom_281_epsilon_0_to_fp16, x = var_9339_cast)[name = tensor("denom_281_cast")]; + tensor out_281_cast = mul(x = zero_mean_281_cast, y = denom_281_cast)[name = tensor("out_281_cast")]; + tensor var_9343_to_fp16 = const()[name = tensor("op_9343_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3557634304)))]; + tensor var_9344_cast = add(x = out_281_cast, y = var_9343_to_fp16)[name = tensor("op_9344_cast")]; + tensor var_9346_to_fp16 = const()[name = tensor("op_9346_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3557636928)))]; + tensor input_557_cast = mul(x = var_9344_cast, y = var_9346_to_fp16)[name = tensor("input_557_cast")]; + tensor var_9354 = const()[name = tensor("op_9354"), val = tensor([1, 1])]; + tensor var_9356 = const()[name = tensor("op_9356"), val = tensor([1, 1])]; + tensor var_9358_pad_type_0 = const()[name = tensor("op_9358_pad_type_0"), val = tensor("custom")]; + tensor var_9358_pad_0 = const()[name = tensor("op_9358_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3557639552)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3583854016)))]; + tensor var_9358_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_9356, groups = var_6865, pad = var_9358_pad_0, pad_type = var_9358_pad_type_0, strides = var_9354, weight = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_557_cast)[name = tensor("op_9358_cast")]; + tensor var_9359_split_sizes_0 = const()[name = tensor("op_9359_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9359_axis_0 = const()[name = tensor("op_9359_axis_0"), val = tensor(1)]; + tensor var_9359_cast_0, tensor var_9359_cast_1 = split(axis = var_9359_axis_0, split_sizes = var_9359_split_sizes_0, x = var_9358_cast)[name = tensor("op_9359_cast")]; + tensor var_9361_mode_0 = const()[name = tensor("op_9361_mode_0"), val = tensor("EXACT")]; + tensor var_9361_cast = gelu(mode = var_9361_mode_0, x = var_9359_cast_1)[name = tensor("op_9361_cast")]; + tensor input_559_cast = mul(x = var_9359_cast_0, y = var_9361_cast)[name = tensor("input_559_cast")]; + tensor var_9365 = const()[name = tensor("op_9365"), val = tensor([1, 1])]; + tensor var_9367 = const()[name = tensor("op_9367"), val = tensor([1, 1])]; + tensor var_9369_pad_type_0 = const()[name = tensor("op_9369_pad_type_0"), val = tensor("custom")]; + tensor var_9369_pad_0 = const()[name = tensor("op_9369_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3583874560)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3596981824)))]; + tensor var_9369_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_9367, groups = var_6865, pad = var_9369_pad_0, pad_type = var_9369_pad_type_0, strides = var_9365, weight = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_559_cast)[name = tensor("op_9369_cast")]; + tensor inputs_283_cast = add(x = var_9369_cast, y = inputs_281_cast)[name = tensor("inputs_283_cast")]; + tensor var_9379 = const()[name = tensor("op_9379"), val = tensor([1])]; + tensor channels_mean_283_cast = reduce_mean(axes = var_9379, keep_dims = var_6860, x = inputs_283_cast)[name = tensor("channels_mean_283_cast")]; + tensor zero_mean_283_cast = sub(x = inputs_283_cast, y = channels_mean_283_cast)[name = tensor("zero_mean_283_cast")]; + tensor zero_mean_sq_283_cast = mul(x = zero_mean_283_cast, y = zero_mean_283_cast)[name = tensor("zero_mean_sq_283_cast")]; + tensor var_9383 = const()[name = tensor("op_9383"), val = tensor([1])]; + tensor var_9384_cast = reduce_mean(axes = var_9383, keep_dims = var_6860, x = zero_mean_sq_283_cast)[name = tensor("op_9384_cast")]; + tensor var_9385_to_fp16 = const()[name = tensor("op_9385_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9386_cast = add(x = var_9384_cast, y = var_9385_to_fp16)[name = tensor("op_9386_cast")]; + tensor denom_283_epsilon_0_to_fp16 = const()[name = tensor("denom_283_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_283_cast = rsqrt(epsilon = denom_283_epsilon_0_to_fp16, x = var_9386_cast)[name = tensor("denom_283_cast")]; + tensor out_283_cast = mul(x = zero_mean_283_cast, y = denom_283_cast)[name = tensor("out_283_cast")]; + tensor var_9390_to_fp16 = const()[name = tensor("op_9390_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3596984448)))]; + tensor var_9391_cast = add(x = out_283_cast, y = var_9390_to_fp16)[name = tensor("op_9391_cast")]; + tensor var_9393_to_fp16 = const()[name = tensor("op_9393_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3596987072)))]; + tensor hidden_states_379_cast = mul(x = var_9391_cast, y = var_9393_to_fp16)[name = tensor("hidden_states_379_cast")]; + tensor var_9400 = const()[name = tensor("op_9400"), val = tensor([1, 1])]; + tensor var_9402 = const()[name = tensor("op_9402"), val = tensor([1, 1])]; + tensor q_189_pad_type_0 = const()[name = tensor("q_189_pad_type_0"), val = tensor("custom")]; + tensor q_189_pad_0 = const()[name = tensor("q_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3596989696)))]; + tensor q_189_cast = conv(dilations = var_9402, groups = var_6865, pad = q_189_pad_0, pad_type = q_189_pad_type_0, strides = var_9400, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_379_cast)[name = tensor("q_189_cast")]; + tensor var_9406 = const()[name = tensor("op_9406"), val = tensor([1, 1])]; + tensor var_9408 = const()[name = tensor("op_9408"), val = tensor([1, 1])]; + tensor k_189_pad_type_0 = const()[name = tensor("k_189_pad_type_0"), val = tensor("custom")]; + tensor k_189_pad_0 = const()[name = tensor("k_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3600266560)))]; + tensor k_189_cast = conv(dilations = var_9408, groups = var_6865, pad = k_189_pad_0, pad_type = k_189_pad_type_0, strides = var_9406, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_379_cast)[name = tensor("k_189_cast")]; + tensor var_9412 = const()[name = tensor("op_9412"), val = tensor([1, 1])]; + tensor var_9414 = const()[name = tensor("op_9414"), val = tensor([1, 1])]; + tensor v_189_pad_type_0 = const()[name = tensor("v_189_pad_type_0"), val = tensor("custom")]; + tensor v_189_pad_0 = const()[name = tensor("v_189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3603543424)))]; + tensor v_189_cast = conv(dilations = var_9414, groups = var_6865, pad = v_189_pad_0, pad_type = v_189_pad_type_0, strides = var_9412, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_379_cast)[name = tensor("v_189_cast")]; + tensor var_9418 = const()[name = tensor("op_9418"), val = tensor([2, 20, 64, -1])]; + tensor var_9419_cast = reshape(shape = var_9418, x = q_189_cast)[name = tensor("op_9419_cast")]; + tensor var_9420 = const()[name = tensor("op_9420"), val = tensor([2, 20, 64, -1])]; + tensor var_9421_cast = reshape(shape = var_9420, x = k_189_cast)[name = tensor("op_9421_cast")]; + tensor var_9422 = const()[name = tensor("op_9422"), val = tensor([2, 20, 64, -1])]; + tensor var_9423_cast = reshape(shape = var_9422, x = v_189_cast)[name = tensor("op_9423_cast")]; + tensor attn_weights_377_transpose_x_0 = const()[name = tensor("attn_weights_377_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_377_transpose_y_0 = const()[name = tensor("attn_weights_377_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_377_cast = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_9419_cast, y = var_9421_cast)[name = tensor("attn_weights_377_cast")]; + tensor attn_weights_379_cast = mul(x = attn_weights_377_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_379_cast")]; + tensor var_9427_cast = softmax(axis = var_6849, x = attn_weights_379_cast)[name = tensor("op_9427_cast")]; + tensor attn_189_transpose_x_0 = const()[name = tensor("attn_189_transpose_x_0"), val = tensor(false)]; + tensor attn_189_transpose_y_0 = const()[name = tensor("attn_189_transpose_y_0"), val = tensor(true)]; + tensor attn_189_cast = matmul(transpose_x = attn_189_transpose_x_0, transpose_y = attn_189_transpose_y_0, x = var_9423_cast, y = var_9427_cast)[name = tensor("attn_189_cast")]; + tensor var_9431 = const()[name = tensor("op_9431"), val = tensor([2, 1280, 1, -1])]; + tensor input_561_cast = reshape(shape = var_9431, x = attn_189_cast)[name = tensor("input_561_cast")]; + tensor var_9436 = const()[name = tensor("op_9436"), val = tensor([1, 1])]; + tensor var_9438 = const()[name = tensor("op_9438"), val = tensor([1, 1])]; + tensor var_9440_pad_type_0 = const()[name = tensor("op_9440_pad_type_0"), val = tensor("custom")]; + tensor var_9440_pad_0 = const()[name = tensor("op_9440_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3606820288)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3610097152)))]; + tensor var_9440_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_9438, groups = var_6865, pad = var_9440_pad_0, pad_type = var_9440_pad_type_0, strides = var_9436, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_561_cast)[name = tensor("op_9440_cast")]; + tensor inputs_285_cast = add(x = var_9440_cast, y = inputs_283_cast)[name = tensor("inputs_285_cast")]; + tensor var_9444 = const()[name = tensor("op_9444"), val = tensor([1])]; + tensor channels_mean_285_cast = reduce_mean(axes = var_9444, keep_dims = var_6860, x = inputs_285_cast)[name = tensor("channels_mean_285_cast")]; + tensor zero_mean_285_cast = sub(x = inputs_285_cast, y = channels_mean_285_cast)[name = tensor("zero_mean_285_cast")]; + tensor zero_mean_sq_285_cast = mul(x = zero_mean_285_cast, y = zero_mean_285_cast)[name = tensor("zero_mean_sq_285_cast")]; + tensor var_9448 = const()[name = tensor("op_9448"), val = tensor([1])]; + tensor var_9449_cast = reduce_mean(axes = var_9448, keep_dims = var_6860, x = zero_mean_sq_285_cast)[name = tensor("op_9449_cast")]; + tensor var_9450_to_fp16 = const()[name = tensor("op_9450_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9451_cast = add(x = var_9449_cast, y = var_9450_to_fp16)[name = tensor("op_9451_cast")]; + tensor denom_285_epsilon_0_to_fp16 = const()[name = tensor("denom_285_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_285_cast = rsqrt(epsilon = denom_285_epsilon_0_to_fp16, x = var_9451_cast)[name = tensor("denom_285_cast")]; + tensor out_285_cast = mul(x = zero_mean_285_cast, y = denom_285_cast)[name = tensor("out_285_cast")]; + tensor var_9455_to_fp16 = const()[name = tensor("op_9455_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3610099776)))]; + tensor var_9456_cast = add(x = out_285_cast, y = var_9455_to_fp16)[name = tensor("op_9456_cast")]; + tensor var_9458_to_fp16 = const()[name = tensor("op_9458_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3610102400)))]; + tensor hidden_states_381_cast = mul(x = var_9456_cast, y = var_9458_to_fp16)[name = tensor("hidden_states_381_cast")]; + tensor var_9465 = const()[name = tensor("op_9465"), val = tensor([1, 1])]; + tensor var_9467 = const()[name = tensor("op_9467"), val = tensor([1, 1])]; + tensor q_191_pad_type_0 = const()[name = tensor("q_191_pad_type_0"), val = tensor("custom")]; + tensor q_191_pad_0 = const()[name = tensor("q_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3610105024)))]; + tensor q_191_cast = conv(dilations = var_9467, groups = var_6865, pad = q_191_pad_0, pad_type = q_191_pad_type_0, strides = var_9465, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_381_cast)[name = tensor("q_191_cast")]; + tensor var_9471 = const()[name = tensor("op_9471"), val = tensor([1, 1])]; + tensor var_9473 = const()[name = tensor("op_9473"), val = tensor([1, 1])]; + tensor k_191_pad_type_0 = const()[name = tensor("k_191_pad_type_0"), val = tensor("custom")]; + tensor k_191_pad_0 = const()[name = tensor("k_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3613381888)))]; + tensor k_191_cast = conv(dilations = var_9473, groups = var_6865, pad = k_191_pad_0, pad_type = k_191_pad_type_0, strides = var_9471, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_191_cast")]; + tensor var_9477 = const()[name = tensor("op_9477"), val = tensor([1, 1])]; + tensor var_9479 = const()[name = tensor("op_9479"), val = tensor([1, 1])]; + tensor v_191_pad_type_0 = const()[name = tensor("v_191_pad_type_0"), val = tensor("custom")]; + tensor v_191_pad_0 = const()[name = tensor("v_191_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3618624832)))]; + tensor v_191_cast = conv(dilations = var_9479, groups = var_6865, pad = v_191_pad_0, pad_type = v_191_pad_type_0, strides = var_9477, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_191_cast")]; + tensor var_9483 = const()[name = tensor("op_9483"), val = tensor([2, 20, 64, -1])]; + tensor var_9484_cast = reshape(shape = var_9483, x = q_191_cast)[name = tensor("op_9484_cast")]; + tensor var_9485 = const()[name = tensor("op_9485"), val = tensor([2, 20, 64, -1])]; + tensor var_9486_cast = reshape(shape = var_9485, x = k_191_cast)[name = tensor("op_9486_cast")]; + tensor var_9487 = const()[name = tensor("op_9487"), val = tensor([2, 20, 64, -1])]; + tensor var_9488_cast = reshape(shape = var_9487, x = v_191_cast)[name = tensor("op_9488_cast")]; + tensor attn_weights_381_transpose_x_0 = const()[name = tensor("attn_weights_381_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_381_transpose_y_0 = const()[name = tensor("attn_weights_381_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_381_cast = matmul(transpose_x = attn_weights_381_transpose_x_0, transpose_y = attn_weights_381_transpose_y_0, x = var_9484_cast, y = var_9486_cast)[name = tensor("attn_weights_381_cast")]; + tensor attn_weights_383_cast = mul(x = attn_weights_381_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_383_cast")]; + tensor var_9492_cast = softmax(axis = var_6849, x = attn_weights_383_cast)[name = tensor("op_9492_cast")]; + tensor attn_191_transpose_x_0 = const()[name = tensor("attn_191_transpose_x_0"), val = tensor(false)]; + tensor attn_191_transpose_y_0 = const()[name = tensor("attn_191_transpose_y_0"), val = tensor(true)]; + tensor attn_191_cast = matmul(transpose_x = attn_191_transpose_x_0, transpose_y = attn_191_transpose_y_0, x = var_9488_cast, y = var_9492_cast)[name = tensor("attn_191_cast")]; + tensor var_9496 = const()[name = tensor("op_9496"), val = tensor([2, 1280, 1, -1])]; + tensor input_563_cast = reshape(shape = var_9496, x = attn_191_cast)[name = tensor("input_563_cast")]; + tensor var_9501 = const()[name = tensor("op_9501"), val = tensor([1, 1])]; + tensor var_9503 = const()[name = tensor("op_9503"), val = tensor([1, 1])]; + tensor var_9505_pad_type_0 = const()[name = tensor("op_9505_pad_type_0"), val = tensor("custom")]; + tensor var_9505_pad_0 = const()[name = tensor("op_9505_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3623867776)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627144640)))]; + tensor var_9505_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_9503, groups = var_6865, pad = var_9505_pad_0, pad_type = var_9505_pad_type_0, strides = var_9501, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_563_cast)[name = tensor("op_9505_cast")]; + tensor inputs_287_cast = add(x = var_9505_cast, y = inputs_285_cast)[name = tensor("inputs_287_cast")]; + tensor var_9509 = const()[name = tensor("op_9509"), val = tensor([1])]; + tensor channels_mean_287_cast = reduce_mean(axes = var_9509, keep_dims = var_6860, x = inputs_287_cast)[name = tensor("channels_mean_287_cast")]; + tensor zero_mean_287_cast = sub(x = inputs_287_cast, y = channels_mean_287_cast)[name = tensor("zero_mean_287_cast")]; + tensor zero_mean_sq_287_cast = mul(x = zero_mean_287_cast, y = zero_mean_287_cast)[name = tensor("zero_mean_sq_287_cast")]; + tensor var_9513 = const()[name = tensor("op_9513"), val = tensor([1])]; + tensor var_9514_cast = reduce_mean(axes = var_9513, keep_dims = var_6860, x = zero_mean_sq_287_cast)[name = tensor("op_9514_cast")]; + tensor var_9515_to_fp16 = const()[name = tensor("op_9515_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9516_cast = add(x = var_9514_cast, y = var_9515_to_fp16)[name = tensor("op_9516_cast")]; + tensor denom_287_epsilon_0_to_fp16 = const()[name = tensor("denom_287_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_287_cast = rsqrt(epsilon = denom_287_epsilon_0_to_fp16, x = var_9516_cast)[name = tensor("denom_287_cast")]; + tensor out_287_cast = mul(x = zero_mean_287_cast, y = denom_287_cast)[name = tensor("out_287_cast")]; + tensor var_9520_to_fp16 = const()[name = tensor("op_9520_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627147264)))]; + tensor var_9521_cast = add(x = out_287_cast, y = var_9520_to_fp16)[name = tensor("op_9521_cast")]; + tensor var_9523_to_fp16 = const()[name = tensor("op_9523_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627149888)))]; + tensor input_565_cast = mul(x = var_9521_cast, y = var_9523_to_fp16)[name = tensor("input_565_cast")]; + tensor var_9531 = const()[name = tensor("op_9531"), val = tensor([1, 1])]; + tensor var_9533 = const()[name = tensor("op_9533"), val = tensor([1, 1])]; + tensor var_9535_pad_type_0 = const()[name = tensor("op_9535_pad_type_0"), val = tensor("custom")]; + tensor var_9535_pad_0 = const()[name = tensor("op_9535_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627152512)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3653366976)))]; + tensor var_9535_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_9533, groups = var_6865, pad = var_9535_pad_0, pad_type = var_9535_pad_type_0, strides = var_9531, weight = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_565_cast)[name = tensor("op_9535_cast")]; + tensor var_9536_split_sizes_0 = const()[name = tensor("op_9536_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9536_axis_0 = const()[name = tensor("op_9536_axis_0"), val = tensor(1)]; + tensor var_9536_cast_0, tensor var_9536_cast_1 = split(axis = var_9536_axis_0, split_sizes = var_9536_split_sizes_0, x = var_9535_cast)[name = tensor("op_9536_cast")]; + tensor var_9538_mode_0 = const()[name = tensor("op_9538_mode_0"), val = tensor("EXACT")]; + tensor var_9538_cast = gelu(mode = var_9538_mode_0, x = var_9536_cast_1)[name = tensor("op_9538_cast")]; + tensor input_567_cast = mul(x = var_9536_cast_0, y = var_9538_cast)[name = tensor("input_567_cast")]; + tensor var_9542 = const()[name = tensor("op_9542"), val = tensor([1, 1])]; + tensor var_9544 = const()[name = tensor("op_9544"), val = tensor([1, 1])]; + tensor var_9546_pad_type_0 = const()[name = tensor("op_9546_pad_type_0"), val = tensor("custom")]; + tensor var_9546_pad_0 = const()[name = tensor("op_9546_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3653387520)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3666494784)))]; + tensor var_9546_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_9544, groups = var_6865, pad = var_9546_pad_0, pad_type = var_9546_pad_type_0, strides = var_9542, weight = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_567_cast)[name = tensor("op_9546_cast")]; + tensor inputs_289_cast = add(x = var_9546_cast, y = inputs_287_cast)[name = tensor("inputs_289_cast")]; + tensor var_9556 = const()[name = tensor("op_9556"), val = tensor([1])]; + tensor channels_mean_289_cast = reduce_mean(axes = var_9556, keep_dims = var_6860, x = inputs_289_cast)[name = tensor("channels_mean_289_cast")]; + tensor zero_mean_289_cast = sub(x = inputs_289_cast, y = channels_mean_289_cast)[name = tensor("zero_mean_289_cast")]; + tensor zero_mean_sq_289_cast = mul(x = zero_mean_289_cast, y = zero_mean_289_cast)[name = tensor("zero_mean_sq_289_cast")]; + tensor var_9560 = const()[name = tensor("op_9560"), val = tensor([1])]; + tensor var_9561_cast = reduce_mean(axes = var_9560, keep_dims = var_6860, x = zero_mean_sq_289_cast)[name = tensor("op_9561_cast")]; + tensor var_9562_to_fp16 = const()[name = tensor("op_9562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9563_cast = add(x = var_9561_cast, y = var_9562_to_fp16)[name = tensor("op_9563_cast")]; + tensor denom_289_epsilon_0_to_fp16 = const()[name = tensor("denom_289_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_289_cast = rsqrt(epsilon = denom_289_epsilon_0_to_fp16, x = var_9563_cast)[name = tensor("denom_289_cast")]; + tensor out_289_cast = mul(x = zero_mean_289_cast, y = denom_289_cast)[name = tensor("out_289_cast")]; + tensor var_9567_to_fp16 = const()[name = tensor("op_9567_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3666497408)))]; + tensor var_9568_cast = add(x = out_289_cast, y = var_9567_to_fp16)[name = tensor("op_9568_cast")]; + tensor var_9570_to_fp16 = const()[name = tensor("op_9570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3666500032)))]; + tensor hidden_states_385_cast = mul(x = var_9568_cast, y = var_9570_to_fp16)[name = tensor("hidden_states_385_cast")]; + tensor var_9577 = const()[name = tensor("op_9577"), val = tensor([1, 1])]; + tensor var_9579 = const()[name = tensor("op_9579"), val = tensor([1, 1])]; + tensor q_193_pad_type_0 = const()[name = tensor("q_193_pad_type_0"), val = tensor("custom")]; + tensor q_193_pad_0 = const()[name = tensor("q_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3666502656)))]; + tensor q_193_cast = conv(dilations = var_9579, groups = var_6865, pad = q_193_pad_0, pad_type = q_193_pad_type_0, strides = var_9577, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_385_cast)[name = tensor("q_193_cast")]; + tensor var_9583 = const()[name = tensor("op_9583"), val = tensor([1, 1])]; + tensor var_9585 = const()[name = tensor("op_9585"), val = tensor([1, 1])]; + tensor k_193_pad_type_0 = const()[name = tensor("k_193_pad_type_0"), val = tensor("custom")]; + tensor k_193_pad_0 = const()[name = tensor("k_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3669779520)))]; + tensor k_193_cast = conv(dilations = var_9585, groups = var_6865, pad = k_193_pad_0, pad_type = k_193_pad_type_0, strides = var_9583, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_385_cast)[name = tensor("k_193_cast")]; + tensor var_9589 = const()[name = tensor("op_9589"), val = tensor([1, 1])]; + tensor var_9591 = const()[name = tensor("op_9591"), val = tensor([1, 1])]; + tensor v_193_pad_type_0 = const()[name = tensor("v_193_pad_type_0"), val = tensor("custom")]; + tensor v_193_pad_0 = const()[name = tensor("v_193_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3673056384)))]; + tensor v_193_cast = conv(dilations = var_9591, groups = var_6865, pad = v_193_pad_0, pad_type = v_193_pad_type_0, strides = var_9589, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_385_cast)[name = tensor("v_193_cast")]; + tensor var_9595 = const()[name = tensor("op_9595"), val = tensor([2, 20, 64, -1])]; + tensor var_9596_cast = reshape(shape = var_9595, x = q_193_cast)[name = tensor("op_9596_cast")]; + tensor var_9597 = const()[name = tensor("op_9597"), val = tensor([2, 20, 64, -1])]; + tensor var_9598_cast = reshape(shape = var_9597, x = k_193_cast)[name = tensor("op_9598_cast")]; + tensor var_9599 = const()[name = tensor("op_9599"), val = tensor([2, 20, 64, -1])]; + tensor var_9600_cast = reshape(shape = var_9599, x = v_193_cast)[name = tensor("op_9600_cast")]; + tensor attn_weights_385_transpose_x_0 = const()[name = tensor("attn_weights_385_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_385_transpose_y_0 = const()[name = tensor("attn_weights_385_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_385_cast = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_9596_cast, y = var_9598_cast)[name = tensor("attn_weights_385_cast")]; + tensor attn_weights_387_cast = mul(x = attn_weights_385_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_387_cast")]; + tensor var_9604_cast = softmax(axis = var_6849, x = attn_weights_387_cast)[name = tensor("op_9604_cast")]; + tensor attn_193_transpose_x_0 = const()[name = tensor("attn_193_transpose_x_0"), val = tensor(false)]; + tensor attn_193_transpose_y_0 = const()[name = tensor("attn_193_transpose_y_0"), val = tensor(true)]; + tensor attn_193_cast = matmul(transpose_x = attn_193_transpose_x_0, transpose_y = attn_193_transpose_y_0, x = var_9600_cast, y = var_9604_cast)[name = tensor("attn_193_cast")]; + tensor var_9608 = const()[name = tensor("op_9608"), val = tensor([2, 1280, 1, -1])]; + tensor input_569_cast = reshape(shape = var_9608, x = attn_193_cast)[name = tensor("input_569_cast")]; + tensor var_9613 = const()[name = tensor("op_9613"), val = tensor([1, 1])]; + tensor var_9615 = const()[name = tensor("op_9615"), val = tensor([1, 1])]; + tensor var_9617_pad_type_0 = const()[name = tensor("op_9617_pad_type_0"), val = tensor("custom")]; + tensor var_9617_pad_0 = const()[name = tensor("op_9617_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3676333248)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3679610112)))]; + tensor var_9617_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_9615, groups = var_6865, pad = var_9617_pad_0, pad_type = var_9617_pad_type_0, strides = var_9613, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_569_cast)[name = tensor("op_9617_cast")]; + tensor inputs_291_cast = add(x = var_9617_cast, y = inputs_289_cast)[name = tensor("inputs_291_cast")]; + tensor var_9621 = const()[name = tensor("op_9621"), val = tensor([1])]; + tensor channels_mean_291_cast = reduce_mean(axes = var_9621, keep_dims = var_6860, x = inputs_291_cast)[name = tensor("channels_mean_291_cast")]; + tensor zero_mean_291_cast = sub(x = inputs_291_cast, y = channels_mean_291_cast)[name = tensor("zero_mean_291_cast")]; + tensor zero_mean_sq_291_cast = mul(x = zero_mean_291_cast, y = zero_mean_291_cast)[name = tensor("zero_mean_sq_291_cast")]; + tensor var_9625 = const()[name = tensor("op_9625"), val = tensor([1])]; + tensor var_9626_cast = reduce_mean(axes = var_9625, keep_dims = var_6860, x = zero_mean_sq_291_cast)[name = tensor("op_9626_cast")]; + tensor var_9627_to_fp16 = const()[name = tensor("op_9627_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9628_cast = add(x = var_9626_cast, y = var_9627_to_fp16)[name = tensor("op_9628_cast")]; + tensor denom_291_epsilon_0_to_fp16 = const()[name = tensor("denom_291_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_291_cast = rsqrt(epsilon = denom_291_epsilon_0_to_fp16, x = var_9628_cast)[name = tensor("denom_291_cast")]; + tensor out_291_cast = mul(x = zero_mean_291_cast, y = denom_291_cast)[name = tensor("out_291_cast")]; + tensor var_9632_to_fp16 = const()[name = tensor("op_9632_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3679612736)))]; + tensor var_9633_cast = add(x = out_291_cast, y = var_9632_to_fp16)[name = tensor("op_9633_cast")]; + tensor var_9635_to_fp16 = const()[name = tensor("op_9635_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3679615360)))]; + tensor hidden_states_387_cast = mul(x = var_9633_cast, y = var_9635_to_fp16)[name = tensor("hidden_states_387_cast")]; + tensor var_9642 = const()[name = tensor("op_9642"), val = tensor([1, 1])]; + tensor var_9644 = const()[name = tensor("op_9644"), val = tensor([1, 1])]; + tensor q_195_pad_type_0 = const()[name = tensor("q_195_pad_type_0"), val = tensor("custom")]; + tensor q_195_pad_0 = const()[name = tensor("q_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3679617984)))]; + tensor q_195_cast = conv(dilations = var_9644, groups = var_6865, pad = q_195_pad_0, pad_type = q_195_pad_type_0, strides = var_9642, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_387_cast)[name = tensor("q_195_cast")]; + tensor var_9648 = const()[name = tensor("op_9648"), val = tensor([1, 1])]; + tensor var_9650 = const()[name = tensor("op_9650"), val = tensor([1, 1])]; + tensor k_195_pad_type_0 = const()[name = tensor("k_195_pad_type_0"), val = tensor("custom")]; + tensor k_195_pad_0 = const()[name = tensor("k_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3682894848)))]; + tensor k_195_cast = conv(dilations = var_9650, groups = var_6865, pad = k_195_pad_0, pad_type = k_195_pad_type_0, strides = var_9648, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_195_cast")]; + tensor var_9654 = const()[name = tensor("op_9654"), val = tensor([1, 1])]; + tensor var_9656 = const()[name = tensor("op_9656"), val = tensor([1, 1])]; + tensor v_195_pad_type_0 = const()[name = tensor("v_195_pad_type_0"), val = tensor("custom")]; + tensor v_195_pad_0 = const()[name = tensor("v_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3688137792)))]; + tensor v_195_cast = conv(dilations = var_9656, groups = var_6865, pad = v_195_pad_0, pad_type = v_195_pad_type_0, strides = var_9654, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_195_cast")]; + tensor var_9660 = const()[name = tensor("op_9660"), val = tensor([2, 20, 64, -1])]; + tensor var_9661_cast = reshape(shape = var_9660, x = q_195_cast)[name = tensor("op_9661_cast")]; + tensor var_9662 = const()[name = tensor("op_9662"), val = tensor([2, 20, 64, -1])]; + tensor var_9663_cast = reshape(shape = var_9662, x = k_195_cast)[name = tensor("op_9663_cast")]; + tensor var_9664 = const()[name = tensor("op_9664"), val = tensor([2, 20, 64, -1])]; + tensor var_9665_cast = reshape(shape = var_9664, x = v_195_cast)[name = tensor("op_9665_cast")]; + tensor attn_weights_389_transpose_x_0 = const()[name = tensor("attn_weights_389_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_389_transpose_y_0 = const()[name = tensor("attn_weights_389_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_389_cast = matmul(transpose_x = attn_weights_389_transpose_x_0, transpose_y = attn_weights_389_transpose_y_0, x = var_9661_cast, y = var_9663_cast)[name = tensor("attn_weights_389_cast")]; + tensor attn_weights_391_cast = mul(x = attn_weights_389_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_391_cast")]; + tensor var_9669_cast = softmax(axis = var_6849, x = attn_weights_391_cast)[name = tensor("op_9669_cast")]; + tensor attn_195_transpose_x_0 = const()[name = tensor("attn_195_transpose_x_0"), val = tensor(false)]; + tensor attn_195_transpose_y_0 = const()[name = tensor("attn_195_transpose_y_0"), val = tensor(true)]; + tensor attn_195_cast = matmul(transpose_x = attn_195_transpose_x_0, transpose_y = attn_195_transpose_y_0, x = var_9665_cast, y = var_9669_cast)[name = tensor("attn_195_cast")]; + tensor var_9673 = const()[name = tensor("op_9673"), val = tensor([2, 1280, 1, -1])]; + tensor input_571_cast = reshape(shape = var_9673, x = attn_195_cast)[name = tensor("input_571_cast")]; + tensor var_9678 = const()[name = tensor("op_9678"), val = tensor([1, 1])]; + tensor var_9680 = const()[name = tensor("op_9680"), val = tensor([1, 1])]; + tensor var_9682_pad_type_0 = const()[name = tensor("op_9682_pad_type_0"), val = tensor("custom")]; + tensor var_9682_pad_0 = const()[name = tensor("op_9682_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3693380736)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3696657600)))]; + tensor var_9682_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_9680, groups = var_6865, pad = var_9682_pad_0, pad_type = var_9682_pad_type_0, strides = var_9678, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_571_cast)[name = tensor("op_9682_cast")]; + tensor inputs_293_cast = add(x = var_9682_cast, y = inputs_291_cast)[name = tensor("inputs_293_cast")]; + tensor var_9686 = const()[name = tensor("op_9686"), val = tensor([1])]; + tensor channels_mean_293_cast = reduce_mean(axes = var_9686, keep_dims = var_6860, x = inputs_293_cast)[name = tensor("channels_mean_293_cast")]; + tensor zero_mean_293_cast = sub(x = inputs_293_cast, y = channels_mean_293_cast)[name = tensor("zero_mean_293_cast")]; + tensor zero_mean_sq_293_cast = mul(x = zero_mean_293_cast, y = zero_mean_293_cast)[name = tensor("zero_mean_sq_293_cast")]; + tensor var_9690 = const()[name = tensor("op_9690"), val = tensor([1])]; + tensor var_9691_cast = reduce_mean(axes = var_9690, keep_dims = var_6860, x = zero_mean_sq_293_cast)[name = tensor("op_9691_cast")]; + tensor var_9692_to_fp16 = const()[name = tensor("op_9692_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9693_cast = add(x = var_9691_cast, y = var_9692_to_fp16)[name = tensor("op_9693_cast")]; + tensor denom_293_epsilon_0_to_fp16 = const()[name = tensor("denom_293_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_293_cast = rsqrt(epsilon = denom_293_epsilon_0_to_fp16, x = var_9693_cast)[name = tensor("denom_293_cast")]; + tensor out_293_cast = mul(x = zero_mean_293_cast, y = denom_293_cast)[name = tensor("out_293_cast")]; + tensor var_9697_to_fp16 = const()[name = tensor("op_9697_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3696660224)))]; + tensor var_9698_cast = add(x = out_293_cast, y = var_9697_to_fp16)[name = tensor("op_9698_cast")]; + tensor var_9700_to_fp16 = const()[name = tensor("op_9700_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3696662848)))]; + tensor input_573_cast = mul(x = var_9698_cast, y = var_9700_to_fp16)[name = tensor("input_573_cast")]; + tensor var_9708 = const()[name = tensor("op_9708"), val = tensor([1, 1])]; + tensor var_9710 = const()[name = tensor("op_9710"), val = tensor([1, 1])]; + tensor var_9712_pad_type_0 = const()[name = tensor("op_9712_pad_type_0"), val = tensor("custom")]; + tensor var_9712_pad_0 = const()[name = tensor("op_9712_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3696665472)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3722879936)))]; + tensor var_9712_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_9710, groups = var_6865, pad = var_9712_pad_0, pad_type = var_9712_pad_type_0, strides = var_9708, weight = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_573_cast)[name = tensor("op_9712_cast")]; + tensor var_9713_split_sizes_0 = const()[name = tensor("op_9713_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9713_axis_0 = const()[name = tensor("op_9713_axis_0"), val = tensor(1)]; + tensor var_9713_cast_0, tensor var_9713_cast_1 = split(axis = var_9713_axis_0, split_sizes = var_9713_split_sizes_0, x = var_9712_cast)[name = tensor("op_9713_cast")]; + tensor var_9715_mode_0 = const()[name = tensor("op_9715_mode_0"), val = tensor("EXACT")]; + tensor var_9715_cast = gelu(mode = var_9715_mode_0, x = var_9713_cast_1)[name = tensor("op_9715_cast")]; + tensor input_575_cast = mul(x = var_9713_cast_0, y = var_9715_cast)[name = tensor("input_575_cast")]; + tensor var_9719 = const()[name = tensor("op_9719"), val = tensor([1, 1])]; + tensor var_9721 = const()[name = tensor("op_9721"), val = tensor([1, 1])]; + tensor var_9723_pad_type_0 = const()[name = tensor("op_9723_pad_type_0"), val = tensor("custom")]; + tensor var_9723_pad_0 = const()[name = tensor("op_9723_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3722900480)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3736007744)))]; + tensor var_9723_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_9721, groups = var_6865, pad = var_9723_pad_0, pad_type = var_9723_pad_type_0, strides = var_9719, weight = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_575_cast)[name = tensor("op_9723_cast")]; + tensor inputs_295_cast = add(x = var_9723_cast, y = inputs_293_cast)[name = tensor("inputs_295_cast")]; + tensor var_9733 = const()[name = tensor("op_9733"), val = tensor([1])]; + tensor channels_mean_295_cast = reduce_mean(axes = var_9733, keep_dims = var_6860, x = inputs_295_cast)[name = tensor("channels_mean_295_cast")]; + tensor zero_mean_295_cast = sub(x = inputs_295_cast, y = channels_mean_295_cast)[name = tensor("zero_mean_295_cast")]; + tensor zero_mean_sq_295_cast = mul(x = zero_mean_295_cast, y = zero_mean_295_cast)[name = tensor("zero_mean_sq_295_cast")]; + tensor var_9737 = const()[name = tensor("op_9737"), val = tensor([1])]; + tensor var_9738_cast = reduce_mean(axes = var_9737, keep_dims = var_6860, x = zero_mean_sq_295_cast)[name = tensor("op_9738_cast")]; + tensor var_9739_to_fp16 = const()[name = tensor("op_9739_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9740_cast = add(x = var_9738_cast, y = var_9739_to_fp16)[name = tensor("op_9740_cast")]; + tensor denom_295_epsilon_0_to_fp16 = const()[name = tensor("denom_295_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_295_cast = rsqrt(epsilon = denom_295_epsilon_0_to_fp16, x = var_9740_cast)[name = tensor("denom_295_cast")]; + tensor out_295_cast = mul(x = zero_mean_295_cast, y = denom_295_cast)[name = tensor("out_295_cast")]; + tensor var_9744_to_fp16 = const()[name = tensor("op_9744_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3736010368)))]; + tensor var_9745_cast = add(x = out_295_cast, y = var_9744_to_fp16)[name = tensor("op_9745_cast")]; + tensor var_9747_to_fp16 = const()[name = tensor("op_9747_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3736012992)))]; + tensor hidden_states_391_cast = mul(x = var_9745_cast, y = var_9747_to_fp16)[name = tensor("hidden_states_391_cast")]; + tensor var_9754 = const()[name = tensor("op_9754"), val = tensor([1, 1])]; + tensor var_9756 = const()[name = tensor("op_9756"), val = tensor([1, 1])]; + tensor q_197_pad_type_0 = const()[name = tensor("q_197_pad_type_0"), val = tensor("custom")]; + tensor q_197_pad_0 = const()[name = tensor("q_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3736015616)))]; + tensor q_197_cast = conv(dilations = var_9756, groups = var_6865, pad = q_197_pad_0, pad_type = q_197_pad_type_0, strides = var_9754, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_391_cast)[name = tensor("q_197_cast")]; + tensor var_9760 = const()[name = tensor("op_9760"), val = tensor([1, 1])]; + tensor var_9762 = const()[name = tensor("op_9762"), val = tensor([1, 1])]; + tensor k_197_pad_type_0 = const()[name = tensor("k_197_pad_type_0"), val = tensor("custom")]; + tensor k_197_pad_0 = const()[name = tensor("k_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3739292480)))]; + tensor k_197_cast = conv(dilations = var_9762, groups = var_6865, pad = k_197_pad_0, pad_type = k_197_pad_type_0, strides = var_9760, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_391_cast)[name = tensor("k_197_cast")]; + tensor var_9766 = const()[name = tensor("op_9766"), val = tensor([1, 1])]; + tensor var_9768 = const()[name = tensor("op_9768"), val = tensor([1, 1])]; + tensor v_197_pad_type_0 = const()[name = tensor("v_197_pad_type_0"), val = tensor("custom")]; + tensor v_197_pad_0 = const()[name = tensor("v_197_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3742569344)))]; + tensor v_197_cast = conv(dilations = var_9768, groups = var_6865, pad = v_197_pad_0, pad_type = v_197_pad_type_0, strides = var_9766, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_391_cast)[name = tensor("v_197_cast")]; + tensor var_9772 = const()[name = tensor("op_9772"), val = tensor([2, 20, 64, -1])]; + tensor var_9773_cast = reshape(shape = var_9772, x = q_197_cast)[name = tensor("op_9773_cast")]; + tensor var_9774 = const()[name = tensor("op_9774"), val = tensor([2, 20, 64, -1])]; + tensor var_9775_cast = reshape(shape = var_9774, x = k_197_cast)[name = tensor("op_9775_cast")]; + tensor var_9776 = const()[name = tensor("op_9776"), val = tensor([2, 20, 64, -1])]; + tensor var_9777_cast = reshape(shape = var_9776, x = v_197_cast)[name = tensor("op_9777_cast")]; + tensor attn_weights_393_transpose_x_0 = const()[name = tensor("attn_weights_393_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_393_transpose_y_0 = const()[name = tensor("attn_weights_393_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_393_cast = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_9773_cast, y = var_9775_cast)[name = tensor("attn_weights_393_cast")]; + tensor attn_weights_395_cast = mul(x = attn_weights_393_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_395_cast")]; + tensor var_9781_cast = softmax(axis = var_6849, x = attn_weights_395_cast)[name = tensor("op_9781_cast")]; + tensor attn_197_transpose_x_0 = const()[name = tensor("attn_197_transpose_x_0"), val = tensor(false)]; + tensor attn_197_transpose_y_0 = const()[name = tensor("attn_197_transpose_y_0"), val = tensor(true)]; + tensor attn_197_cast = matmul(transpose_x = attn_197_transpose_x_0, transpose_y = attn_197_transpose_y_0, x = var_9777_cast, y = var_9781_cast)[name = tensor("attn_197_cast")]; + tensor var_9785 = const()[name = tensor("op_9785"), val = tensor([2, 1280, 1, -1])]; + tensor input_577_cast = reshape(shape = var_9785, x = attn_197_cast)[name = tensor("input_577_cast")]; + tensor var_9790 = const()[name = tensor("op_9790"), val = tensor([1, 1])]; + tensor var_9792 = const()[name = tensor("op_9792"), val = tensor([1, 1])]; + tensor var_9794_pad_type_0 = const()[name = tensor("op_9794_pad_type_0"), val = tensor("custom")]; + tensor var_9794_pad_0 = const()[name = tensor("op_9794_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3745846208)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3749123072)))]; + tensor var_9794_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_9792, groups = var_6865, pad = var_9794_pad_0, pad_type = var_9794_pad_type_0, strides = var_9790, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_577_cast)[name = tensor("op_9794_cast")]; + tensor inputs_297_cast = add(x = var_9794_cast, y = inputs_295_cast)[name = tensor("inputs_297_cast")]; + tensor var_9798 = const()[name = tensor("op_9798"), val = tensor([1])]; + tensor channels_mean_297_cast = reduce_mean(axes = var_9798, keep_dims = var_6860, x = inputs_297_cast)[name = tensor("channels_mean_297_cast")]; + tensor zero_mean_297_cast = sub(x = inputs_297_cast, y = channels_mean_297_cast)[name = tensor("zero_mean_297_cast")]; + tensor zero_mean_sq_297_cast = mul(x = zero_mean_297_cast, y = zero_mean_297_cast)[name = tensor("zero_mean_sq_297_cast")]; + tensor var_9802 = const()[name = tensor("op_9802"), val = tensor([1])]; + tensor var_9803_cast = reduce_mean(axes = var_9802, keep_dims = var_6860, x = zero_mean_sq_297_cast)[name = tensor("op_9803_cast")]; + tensor var_9804_to_fp16 = const()[name = tensor("op_9804_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9805_cast = add(x = var_9803_cast, y = var_9804_to_fp16)[name = tensor("op_9805_cast")]; + tensor denom_297_epsilon_0_to_fp16 = const()[name = tensor("denom_297_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_297_cast = rsqrt(epsilon = denom_297_epsilon_0_to_fp16, x = var_9805_cast)[name = tensor("denom_297_cast")]; + tensor out_297_cast = mul(x = zero_mean_297_cast, y = denom_297_cast)[name = tensor("out_297_cast")]; + tensor var_9809_to_fp16 = const()[name = tensor("op_9809_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3749125696)))]; + tensor var_9810_cast = add(x = out_297_cast, y = var_9809_to_fp16)[name = tensor("op_9810_cast")]; + tensor var_9812_to_fp16 = const()[name = tensor("op_9812_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3749128320)))]; + tensor hidden_states_393_cast = mul(x = var_9810_cast, y = var_9812_to_fp16)[name = tensor("hidden_states_393_cast")]; + tensor var_9819 = const()[name = tensor("op_9819"), val = tensor([1, 1])]; + tensor var_9821 = const()[name = tensor("op_9821"), val = tensor([1, 1])]; + tensor q_199_pad_type_0 = const()[name = tensor("q_199_pad_type_0"), val = tensor("custom")]; + tensor q_199_pad_0 = const()[name = tensor("q_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3749130944)))]; + tensor q_199_cast = conv(dilations = var_9821, groups = var_6865, pad = q_199_pad_0, pad_type = q_199_pad_type_0, strides = var_9819, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_393_cast)[name = tensor("q_199_cast")]; + tensor var_9825 = const()[name = tensor("op_9825"), val = tensor([1, 1])]; + tensor var_9827 = const()[name = tensor("op_9827"), val = tensor([1, 1])]; + tensor k_199_pad_type_0 = const()[name = tensor("k_199_pad_type_0"), val = tensor("custom")]; + tensor k_199_pad_0 = const()[name = tensor("k_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3752407808)))]; + tensor k_199_cast = conv(dilations = var_9827, groups = var_6865, pad = k_199_pad_0, pad_type = k_199_pad_type_0, strides = var_9825, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_199_cast")]; + tensor var_9831 = const()[name = tensor("op_9831"), val = tensor([1, 1])]; + tensor var_9833 = const()[name = tensor("op_9833"), val = tensor([1, 1])]; + tensor v_199_pad_type_0 = const()[name = tensor("v_199_pad_type_0"), val = tensor("custom")]; + tensor v_199_pad_0 = const()[name = tensor("v_199_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3757650752)))]; + tensor v_199_cast = conv(dilations = var_9833, groups = var_6865, pad = v_199_pad_0, pad_type = v_199_pad_type_0, strides = var_9831, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_199_cast")]; + tensor var_9837 = const()[name = tensor("op_9837"), val = tensor([2, 20, 64, -1])]; + tensor var_9838_cast = reshape(shape = var_9837, x = q_199_cast)[name = tensor("op_9838_cast")]; + tensor var_9839 = const()[name = tensor("op_9839"), val = tensor([2, 20, 64, -1])]; + tensor var_9840_cast = reshape(shape = var_9839, x = k_199_cast)[name = tensor("op_9840_cast")]; + tensor var_9841 = const()[name = tensor("op_9841"), val = tensor([2, 20, 64, -1])]; + tensor var_9842_cast = reshape(shape = var_9841, x = v_199_cast)[name = tensor("op_9842_cast")]; + tensor attn_weights_397_transpose_x_0 = const()[name = tensor("attn_weights_397_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_397_transpose_y_0 = const()[name = tensor("attn_weights_397_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_397_cast = matmul(transpose_x = attn_weights_397_transpose_x_0, transpose_y = attn_weights_397_transpose_y_0, x = var_9838_cast, y = var_9840_cast)[name = tensor("attn_weights_397_cast")]; + tensor attn_weights_399_cast = mul(x = attn_weights_397_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_399_cast")]; + tensor var_9846_cast = softmax(axis = var_6849, x = attn_weights_399_cast)[name = tensor("op_9846_cast")]; + tensor attn_199_transpose_x_0 = const()[name = tensor("attn_199_transpose_x_0"), val = tensor(false)]; + tensor attn_199_transpose_y_0 = const()[name = tensor("attn_199_transpose_y_0"), val = tensor(true)]; + tensor attn_199_cast = matmul(transpose_x = attn_199_transpose_x_0, transpose_y = attn_199_transpose_y_0, x = var_9842_cast, y = var_9846_cast)[name = tensor("attn_199_cast")]; + tensor var_9850 = const()[name = tensor("op_9850"), val = tensor([2, 1280, 1, -1])]; + tensor input_579_cast = reshape(shape = var_9850, x = attn_199_cast)[name = tensor("input_579_cast")]; + tensor var_9855 = const()[name = tensor("op_9855"), val = tensor([1, 1])]; + tensor var_9857 = const()[name = tensor("op_9857"), val = tensor([1, 1])]; + tensor var_9859_pad_type_0 = const()[name = tensor("op_9859_pad_type_0"), val = tensor("custom")]; + tensor var_9859_pad_0 = const()[name = tensor("op_9859_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3762893696)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3766170560)))]; + tensor var_9859_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_9857, groups = var_6865, pad = var_9859_pad_0, pad_type = var_9859_pad_type_0, strides = var_9855, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_579_cast)[name = tensor("op_9859_cast")]; + tensor inputs_299_cast = add(x = var_9859_cast, y = inputs_297_cast)[name = tensor("inputs_299_cast")]; + tensor var_9863 = const()[name = tensor("op_9863"), val = tensor([1])]; + tensor channels_mean_299_cast = reduce_mean(axes = var_9863, keep_dims = var_6860, x = inputs_299_cast)[name = tensor("channels_mean_299_cast")]; + tensor zero_mean_299_cast = sub(x = inputs_299_cast, y = channels_mean_299_cast)[name = tensor("zero_mean_299_cast")]; + tensor zero_mean_sq_299_cast = mul(x = zero_mean_299_cast, y = zero_mean_299_cast)[name = tensor("zero_mean_sq_299_cast")]; + tensor var_9867 = const()[name = tensor("op_9867"), val = tensor([1])]; + tensor var_9868_cast = reduce_mean(axes = var_9867, keep_dims = var_6860, x = zero_mean_sq_299_cast)[name = tensor("op_9868_cast")]; + tensor var_9869_to_fp16 = const()[name = tensor("op_9869_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9870_cast = add(x = var_9868_cast, y = var_9869_to_fp16)[name = tensor("op_9870_cast")]; + tensor denom_299_epsilon_0_to_fp16 = const()[name = tensor("denom_299_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_299_cast = rsqrt(epsilon = denom_299_epsilon_0_to_fp16, x = var_9870_cast)[name = tensor("denom_299_cast")]; + tensor out_299_cast = mul(x = zero_mean_299_cast, y = denom_299_cast)[name = tensor("out_299_cast")]; + tensor var_9874_to_fp16 = const()[name = tensor("op_9874_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3766173184)))]; + tensor var_9875_cast = add(x = out_299_cast, y = var_9874_to_fp16)[name = tensor("op_9875_cast")]; + tensor var_9877_to_fp16 = const()[name = tensor("op_9877_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3766175808)))]; + tensor input_581_cast = mul(x = var_9875_cast, y = var_9877_to_fp16)[name = tensor("input_581_cast")]; + tensor var_9885 = const()[name = tensor("op_9885"), val = tensor([1, 1])]; + tensor var_9887 = const()[name = tensor("op_9887"), val = tensor([1, 1])]; + tensor var_9889_pad_type_0 = const()[name = tensor("op_9889_pad_type_0"), val = tensor("custom")]; + tensor var_9889_pad_0 = const()[name = tensor("op_9889_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3766178432)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3792392896)))]; + tensor var_9889_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_9887, groups = var_6865, pad = var_9889_pad_0, pad_type = var_9889_pad_type_0, strides = var_9885, weight = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_581_cast)[name = tensor("op_9889_cast")]; + tensor var_9890_split_sizes_0 = const()[name = tensor("op_9890_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9890_axis_0 = const()[name = tensor("op_9890_axis_0"), val = tensor(1)]; + tensor var_9890_cast_0, tensor var_9890_cast_1 = split(axis = var_9890_axis_0, split_sizes = var_9890_split_sizes_0, x = var_9889_cast)[name = tensor("op_9890_cast")]; + tensor var_9892_mode_0 = const()[name = tensor("op_9892_mode_0"), val = tensor("EXACT")]; + tensor var_9892_cast = gelu(mode = var_9892_mode_0, x = var_9890_cast_1)[name = tensor("op_9892_cast")]; + tensor input_583_cast = mul(x = var_9890_cast_0, y = var_9892_cast)[name = tensor("input_583_cast")]; + tensor var_9896 = const()[name = tensor("op_9896"), val = tensor([1, 1])]; + tensor var_9898 = const()[name = tensor("op_9898"), val = tensor([1, 1])]; + tensor var_9900_pad_type_0 = const()[name = tensor("op_9900_pad_type_0"), val = tensor("custom")]; + tensor var_9900_pad_0 = const()[name = tensor("op_9900_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3792413440)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3805520704)))]; + tensor var_9900_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_9898, groups = var_6865, pad = var_9900_pad_0, pad_type = var_9900_pad_type_0, strides = var_9896, weight = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_583_cast)[name = tensor("op_9900_cast")]; + tensor inputs_301_cast = add(x = var_9900_cast, y = inputs_299_cast)[name = tensor("inputs_301_cast")]; + tensor var_9910 = const()[name = tensor("op_9910"), val = tensor([1])]; + tensor channels_mean_301_cast = reduce_mean(axes = var_9910, keep_dims = var_6860, x = inputs_301_cast)[name = tensor("channels_mean_301_cast")]; + tensor zero_mean_301_cast = sub(x = inputs_301_cast, y = channels_mean_301_cast)[name = tensor("zero_mean_301_cast")]; + tensor zero_mean_sq_301_cast = mul(x = zero_mean_301_cast, y = zero_mean_301_cast)[name = tensor("zero_mean_sq_301_cast")]; + tensor var_9914 = const()[name = tensor("op_9914"), val = tensor([1])]; + tensor var_9915_cast = reduce_mean(axes = var_9914, keep_dims = var_6860, x = zero_mean_sq_301_cast)[name = tensor("op_9915_cast")]; + tensor var_9916_to_fp16 = const()[name = tensor("op_9916_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9917_cast = add(x = var_9915_cast, y = var_9916_to_fp16)[name = tensor("op_9917_cast")]; + tensor denom_301_epsilon_0_to_fp16 = const()[name = tensor("denom_301_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_301_cast = rsqrt(epsilon = denom_301_epsilon_0_to_fp16, x = var_9917_cast)[name = tensor("denom_301_cast")]; + tensor out_301_cast = mul(x = zero_mean_301_cast, y = denom_301_cast)[name = tensor("out_301_cast")]; + tensor var_9921_to_fp16 = const()[name = tensor("op_9921_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3805523328)))]; + tensor var_9922_cast = add(x = out_301_cast, y = var_9921_to_fp16)[name = tensor("op_9922_cast")]; + tensor var_9924_to_fp16 = const()[name = tensor("op_9924_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3805525952)))]; + tensor hidden_states_397_cast = mul(x = var_9922_cast, y = var_9924_to_fp16)[name = tensor("hidden_states_397_cast")]; + tensor var_9931 = const()[name = tensor("op_9931"), val = tensor([1, 1])]; + tensor var_9933 = const()[name = tensor("op_9933"), val = tensor([1, 1])]; + tensor q_201_pad_type_0 = const()[name = tensor("q_201_pad_type_0"), val = tensor("custom")]; + tensor q_201_pad_0 = const()[name = tensor("q_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3805528576)))]; + tensor q_201_cast = conv(dilations = var_9933, groups = var_6865, pad = q_201_pad_0, pad_type = q_201_pad_type_0, strides = var_9931, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_397_cast)[name = tensor("q_201_cast")]; + tensor var_9937 = const()[name = tensor("op_9937"), val = tensor([1, 1])]; + tensor var_9939 = const()[name = tensor("op_9939"), val = tensor([1, 1])]; + tensor k_201_pad_type_0 = const()[name = tensor("k_201_pad_type_0"), val = tensor("custom")]; + tensor k_201_pad_0 = const()[name = tensor("k_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3808805440)))]; + tensor k_201_cast = conv(dilations = var_9939, groups = var_6865, pad = k_201_pad_0, pad_type = k_201_pad_type_0, strides = var_9937, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_397_cast)[name = tensor("k_201_cast")]; + tensor var_9943 = const()[name = tensor("op_9943"), val = tensor([1, 1])]; + tensor var_9945 = const()[name = tensor("op_9945"), val = tensor([1, 1])]; + tensor v_201_pad_type_0 = const()[name = tensor("v_201_pad_type_0"), val = tensor("custom")]; + tensor v_201_pad_0 = const()[name = tensor("v_201_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3812082304)))]; + tensor v_201_cast = conv(dilations = var_9945, groups = var_6865, pad = v_201_pad_0, pad_type = v_201_pad_type_0, strides = var_9943, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_397_cast)[name = tensor("v_201_cast")]; + tensor var_9949 = const()[name = tensor("op_9949"), val = tensor([2, 20, 64, -1])]; + tensor var_9950_cast = reshape(shape = var_9949, x = q_201_cast)[name = tensor("op_9950_cast")]; + tensor var_9951 = const()[name = tensor("op_9951"), val = tensor([2, 20, 64, -1])]; + tensor var_9952_cast = reshape(shape = var_9951, x = k_201_cast)[name = tensor("op_9952_cast")]; + tensor var_9953 = const()[name = tensor("op_9953"), val = tensor([2, 20, 64, -1])]; + tensor var_9954_cast = reshape(shape = var_9953, x = v_201_cast)[name = tensor("op_9954_cast")]; + tensor attn_weights_401_transpose_x_0 = const()[name = tensor("attn_weights_401_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_401_transpose_y_0 = const()[name = tensor("attn_weights_401_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_401_cast = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9950_cast, y = var_9952_cast)[name = tensor("attn_weights_401_cast")]; + tensor attn_weights_403_cast = mul(x = attn_weights_401_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_403_cast")]; + tensor var_9958_cast = softmax(axis = var_6849, x = attn_weights_403_cast)[name = tensor("op_9958_cast")]; + tensor attn_201_transpose_x_0 = const()[name = tensor("attn_201_transpose_x_0"), val = tensor(false)]; + tensor attn_201_transpose_y_0 = const()[name = tensor("attn_201_transpose_y_0"), val = tensor(true)]; + tensor attn_201_cast = matmul(transpose_x = attn_201_transpose_x_0, transpose_y = attn_201_transpose_y_0, x = var_9954_cast, y = var_9958_cast)[name = tensor("attn_201_cast")]; + tensor var_9962 = const()[name = tensor("op_9962"), val = tensor([2, 1280, 1, -1])]; + tensor input_585_cast = reshape(shape = var_9962, x = attn_201_cast)[name = tensor("input_585_cast")]; + tensor var_9967 = const()[name = tensor("op_9967"), val = tensor([1, 1])]; + tensor var_9969 = const()[name = tensor("op_9969"), val = tensor([1, 1])]; + tensor var_9971_pad_type_0 = const()[name = tensor("op_9971_pad_type_0"), val = tensor("custom")]; + tensor var_9971_pad_0 = const()[name = tensor("op_9971_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3815359168)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818636032)))]; + tensor var_9971_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_9969, groups = var_6865, pad = var_9971_pad_0, pad_type = var_9971_pad_type_0, strides = var_9967, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_585_cast)[name = tensor("op_9971_cast")]; + tensor inputs_303_cast = add(x = var_9971_cast, y = inputs_301_cast)[name = tensor("inputs_303_cast")]; + tensor var_9975 = const()[name = tensor("op_9975"), val = tensor([1])]; + tensor channels_mean_303_cast = reduce_mean(axes = var_9975, keep_dims = var_6860, x = inputs_303_cast)[name = tensor("channels_mean_303_cast")]; + tensor zero_mean_303_cast = sub(x = inputs_303_cast, y = channels_mean_303_cast)[name = tensor("zero_mean_303_cast")]; + tensor zero_mean_sq_303_cast = mul(x = zero_mean_303_cast, y = zero_mean_303_cast)[name = tensor("zero_mean_sq_303_cast")]; + tensor var_9979 = const()[name = tensor("op_9979"), val = tensor([1])]; + tensor var_9980_cast = reduce_mean(axes = var_9979, keep_dims = var_6860, x = zero_mean_sq_303_cast)[name = tensor("op_9980_cast")]; + tensor var_9981_to_fp16 = const()[name = tensor("op_9981_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9982_cast = add(x = var_9980_cast, y = var_9981_to_fp16)[name = tensor("op_9982_cast")]; + tensor denom_303_epsilon_0_to_fp16 = const()[name = tensor("denom_303_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_303_cast = rsqrt(epsilon = denom_303_epsilon_0_to_fp16, x = var_9982_cast)[name = tensor("denom_303_cast")]; + tensor out_303_cast = mul(x = zero_mean_303_cast, y = denom_303_cast)[name = tensor("out_303_cast")]; + tensor var_9986_to_fp16 = const()[name = tensor("op_9986_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818638656)))]; + tensor var_9987_cast = add(x = out_303_cast, y = var_9986_to_fp16)[name = tensor("op_9987_cast")]; + tensor var_9989_to_fp16 = const()[name = tensor("op_9989_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818641280)))]; + tensor hidden_states_399_cast = mul(x = var_9987_cast, y = var_9989_to_fp16)[name = tensor("hidden_states_399_cast")]; + tensor var_9996 = const()[name = tensor("op_9996"), val = tensor([1, 1])]; + tensor var_9998 = const()[name = tensor("op_9998"), val = tensor([1, 1])]; + tensor q_203_pad_type_0 = const()[name = tensor("q_203_pad_type_0"), val = tensor("custom")]; + tensor q_203_pad_0 = const()[name = tensor("q_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818643904)))]; + tensor q_203_cast = conv(dilations = var_9998, groups = var_6865, pad = q_203_pad_0, pad_type = q_203_pad_type_0, strides = var_9996, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_399_cast)[name = tensor("q_203_cast")]; + tensor var_10002 = const()[name = tensor("op_10002"), val = tensor([1, 1])]; + tensor var_10004 = const()[name = tensor("op_10004"), val = tensor([1, 1])]; + tensor k_203_pad_type_0 = const()[name = tensor("k_203_pad_type_0"), val = tensor("custom")]; + tensor k_203_pad_0 = const()[name = tensor("k_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3821920768)))]; + tensor k_203_cast = conv(dilations = var_10004, groups = var_6865, pad = k_203_pad_0, pad_type = k_203_pad_type_0, strides = var_10002, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_203_cast")]; + tensor var_10008 = const()[name = tensor("op_10008"), val = tensor([1, 1])]; + tensor var_10010 = const()[name = tensor("op_10010"), val = tensor([1, 1])]; + tensor v_203_pad_type_0 = const()[name = tensor("v_203_pad_type_0"), val = tensor("custom")]; + tensor v_203_pad_0 = const()[name = tensor("v_203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3827163712)))]; + tensor v_203_cast = conv(dilations = var_10010, groups = var_6865, pad = v_203_pad_0, pad_type = v_203_pad_type_0, strides = var_10008, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_203_cast")]; + tensor var_10014 = const()[name = tensor("op_10014"), val = tensor([2, 20, 64, -1])]; + tensor var_10015_cast = reshape(shape = var_10014, x = q_203_cast)[name = tensor("op_10015_cast")]; + tensor var_10016 = const()[name = tensor("op_10016"), val = tensor([2, 20, 64, -1])]; + tensor var_10017_cast = reshape(shape = var_10016, x = k_203_cast)[name = tensor("op_10017_cast")]; + tensor var_10018 = const()[name = tensor("op_10018"), val = tensor([2, 20, 64, -1])]; + tensor var_10019_cast = reshape(shape = var_10018, x = v_203_cast)[name = tensor("op_10019_cast")]; + tensor attn_weights_405_transpose_x_0 = const()[name = tensor("attn_weights_405_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_405_transpose_y_0 = const()[name = tensor("attn_weights_405_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_405_cast = matmul(transpose_x = attn_weights_405_transpose_x_0, transpose_y = attn_weights_405_transpose_y_0, x = var_10015_cast, y = var_10017_cast)[name = tensor("attn_weights_405_cast")]; + tensor attn_weights_407_cast = mul(x = attn_weights_405_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_407_cast")]; + tensor var_10023_cast = softmax(axis = var_6849, x = attn_weights_407_cast)[name = tensor("op_10023_cast")]; + tensor attn_203_transpose_x_0 = const()[name = tensor("attn_203_transpose_x_0"), val = tensor(false)]; + tensor attn_203_transpose_y_0 = const()[name = tensor("attn_203_transpose_y_0"), val = tensor(true)]; + tensor attn_203_cast = matmul(transpose_x = attn_203_transpose_x_0, transpose_y = attn_203_transpose_y_0, x = var_10019_cast, y = var_10023_cast)[name = tensor("attn_203_cast")]; + tensor var_10027 = const()[name = tensor("op_10027"), val = tensor([2, 1280, 1, -1])]; + tensor input_587_cast = reshape(shape = var_10027, x = attn_203_cast)[name = tensor("input_587_cast")]; + tensor var_10032 = const()[name = tensor("op_10032"), val = tensor([1, 1])]; + tensor var_10034 = const()[name = tensor("op_10034"), val = tensor([1, 1])]; + tensor var_10036_pad_type_0 = const()[name = tensor("op_10036_pad_type_0"), val = tensor("custom")]; + tensor var_10036_pad_0 = const()[name = tensor("op_10036_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3832406656)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3835683520)))]; + tensor var_10036_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_10034, groups = var_6865, pad = var_10036_pad_0, pad_type = var_10036_pad_type_0, strides = var_10032, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_587_cast)[name = tensor("op_10036_cast")]; + tensor inputs_305_cast = add(x = var_10036_cast, y = inputs_303_cast)[name = tensor("inputs_305_cast")]; + tensor var_10040 = const()[name = tensor("op_10040"), val = tensor([1])]; + tensor channels_mean_305_cast = reduce_mean(axes = var_10040, keep_dims = var_6860, x = inputs_305_cast)[name = tensor("channels_mean_305_cast")]; + tensor zero_mean_305_cast = sub(x = inputs_305_cast, y = channels_mean_305_cast)[name = tensor("zero_mean_305_cast")]; + tensor zero_mean_sq_305_cast = mul(x = zero_mean_305_cast, y = zero_mean_305_cast)[name = tensor("zero_mean_sq_305_cast")]; + tensor var_10044 = const()[name = tensor("op_10044"), val = tensor([1])]; + tensor var_10045_cast = reduce_mean(axes = var_10044, keep_dims = var_6860, x = zero_mean_sq_305_cast)[name = tensor("op_10045_cast")]; + tensor var_10046_to_fp16 = const()[name = tensor("op_10046_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10047_cast = add(x = var_10045_cast, y = var_10046_to_fp16)[name = tensor("op_10047_cast")]; + tensor denom_305_epsilon_0_to_fp16 = const()[name = tensor("denom_305_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_305_cast = rsqrt(epsilon = denom_305_epsilon_0_to_fp16, x = var_10047_cast)[name = tensor("denom_305_cast")]; + tensor out_305_cast = mul(x = zero_mean_305_cast, y = denom_305_cast)[name = tensor("out_305_cast")]; + tensor var_10051_to_fp16 = const()[name = tensor("op_10051_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3835686144)))]; + tensor var_10052_cast = add(x = out_305_cast, y = var_10051_to_fp16)[name = tensor("op_10052_cast")]; + tensor var_10054_to_fp16 = const()[name = tensor("op_10054_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3835688768)))]; + tensor input_589_cast = mul(x = var_10052_cast, y = var_10054_to_fp16)[name = tensor("input_589_cast")]; + tensor var_10062 = const()[name = tensor("op_10062"), val = tensor([1, 1])]; + tensor var_10064 = const()[name = tensor("op_10064"), val = tensor([1, 1])]; + tensor var_10066_pad_type_0 = const()[name = tensor("op_10066_pad_type_0"), val = tensor("custom")]; + tensor var_10066_pad_0 = const()[name = tensor("op_10066_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3835691392)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3861905856)))]; + tensor var_10066_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_10064, groups = var_6865, pad = var_10066_pad_0, pad_type = var_10066_pad_type_0, strides = var_10062, weight = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_589_cast)[name = tensor("op_10066_cast")]; + tensor var_10067_split_sizes_0 = const()[name = tensor("op_10067_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10067_axis_0 = const()[name = tensor("op_10067_axis_0"), val = tensor(1)]; + tensor var_10067_cast_0, tensor var_10067_cast_1 = split(axis = var_10067_axis_0, split_sizes = var_10067_split_sizes_0, x = var_10066_cast)[name = tensor("op_10067_cast")]; + tensor var_10069_mode_0 = const()[name = tensor("op_10069_mode_0"), val = tensor("EXACT")]; + tensor var_10069_cast = gelu(mode = var_10069_mode_0, x = var_10067_cast_1)[name = tensor("op_10069_cast")]; + tensor input_591_cast = mul(x = var_10067_cast_0, y = var_10069_cast)[name = tensor("input_591_cast")]; + tensor var_10073 = const()[name = tensor("op_10073"), val = tensor([1, 1])]; + tensor var_10075 = const()[name = tensor("op_10075"), val = tensor([1, 1])]; + tensor var_10077_pad_type_0 = const()[name = tensor("op_10077_pad_type_0"), val = tensor("custom")]; + tensor var_10077_pad_0 = const()[name = tensor("op_10077_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3861926400)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3875033664)))]; + tensor var_10077_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_10075, groups = var_6865, pad = var_10077_pad_0, pad_type = var_10077_pad_type_0, strides = var_10073, weight = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_591_cast)[name = tensor("op_10077_cast")]; + tensor inputs_307_cast = add(x = var_10077_cast, y = inputs_305_cast)[name = tensor("inputs_307_cast")]; + tensor var_10087 = const()[name = tensor("op_10087"), val = tensor([1])]; + tensor channels_mean_307_cast = reduce_mean(axes = var_10087, keep_dims = var_6860, x = inputs_307_cast)[name = tensor("channels_mean_307_cast")]; + tensor zero_mean_307_cast = sub(x = inputs_307_cast, y = channels_mean_307_cast)[name = tensor("zero_mean_307_cast")]; + tensor zero_mean_sq_307_cast = mul(x = zero_mean_307_cast, y = zero_mean_307_cast)[name = tensor("zero_mean_sq_307_cast")]; + tensor var_10091 = const()[name = tensor("op_10091"), val = tensor([1])]; + tensor var_10092_cast = reduce_mean(axes = var_10091, keep_dims = var_6860, x = zero_mean_sq_307_cast)[name = tensor("op_10092_cast")]; + tensor var_10093_to_fp16 = const()[name = tensor("op_10093_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10094_cast = add(x = var_10092_cast, y = var_10093_to_fp16)[name = tensor("op_10094_cast")]; + tensor denom_307_epsilon_0_to_fp16 = const()[name = tensor("denom_307_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_307_cast = rsqrt(epsilon = denom_307_epsilon_0_to_fp16, x = var_10094_cast)[name = tensor("denom_307_cast")]; + tensor out_307_cast = mul(x = zero_mean_307_cast, y = denom_307_cast)[name = tensor("out_307_cast")]; + tensor var_10098_to_fp16 = const()[name = tensor("op_10098_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3875036288)))]; + tensor var_10099_cast = add(x = out_307_cast, y = var_10098_to_fp16)[name = tensor("op_10099_cast")]; + tensor var_10101_to_fp16 = const()[name = tensor("op_10101_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3875038912)))]; + tensor hidden_states_403_cast = mul(x = var_10099_cast, y = var_10101_to_fp16)[name = tensor("hidden_states_403_cast")]; + tensor var_10108 = const()[name = tensor("op_10108"), val = tensor([1, 1])]; + tensor var_10110 = const()[name = tensor("op_10110"), val = tensor([1, 1])]; + tensor q_205_pad_type_0 = const()[name = tensor("q_205_pad_type_0"), val = tensor("custom")]; + tensor q_205_pad_0 = const()[name = tensor("q_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3875041536)))]; + tensor q_205_cast = conv(dilations = var_10110, groups = var_6865, pad = q_205_pad_0, pad_type = q_205_pad_type_0, strides = var_10108, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_403_cast)[name = tensor("q_205_cast")]; + tensor var_10114 = const()[name = tensor("op_10114"), val = tensor([1, 1])]; + tensor var_10116 = const()[name = tensor("op_10116"), val = tensor([1, 1])]; + tensor k_205_pad_type_0 = const()[name = tensor("k_205_pad_type_0"), val = tensor("custom")]; + tensor k_205_pad_0 = const()[name = tensor("k_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3878318400)))]; + tensor k_205_cast = conv(dilations = var_10116, groups = var_6865, pad = k_205_pad_0, pad_type = k_205_pad_type_0, strides = var_10114, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_403_cast)[name = tensor("k_205_cast")]; + tensor var_10120 = const()[name = tensor("op_10120"), val = tensor([1, 1])]; + tensor var_10122 = const()[name = tensor("op_10122"), val = tensor([1, 1])]; + tensor v_205_pad_type_0 = const()[name = tensor("v_205_pad_type_0"), val = tensor("custom")]; + tensor v_205_pad_0 = const()[name = tensor("v_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3881595264)))]; + tensor v_205_cast = conv(dilations = var_10122, groups = var_6865, pad = v_205_pad_0, pad_type = v_205_pad_type_0, strides = var_10120, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_403_cast)[name = tensor("v_205_cast")]; + tensor var_10126 = const()[name = tensor("op_10126"), val = tensor([2, 20, 64, -1])]; + tensor var_10127_cast = reshape(shape = var_10126, x = q_205_cast)[name = tensor("op_10127_cast")]; + tensor var_10128 = const()[name = tensor("op_10128"), val = tensor([2, 20, 64, -1])]; + tensor var_10129_cast = reshape(shape = var_10128, x = k_205_cast)[name = tensor("op_10129_cast")]; + tensor var_10130 = const()[name = tensor("op_10130"), val = tensor([2, 20, 64, -1])]; + tensor var_10131_cast = reshape(shape = var_10130, x = v_205_cast)[name = tensor("op_10131_cast")]; + tensor attn_weights_409_transpose_x_0 = const()[name = tensor("attn_weights_409_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_409_transpose_y_0 = const()[name = tensor("attn_weights_409_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_409_cast = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_10127_cast, y = var_10129_cast)[name = tensor("attn_weights_409_cast")]; + tensor attn_weights_411_cast = mul(x = attn_weights_409_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_411_cast")]; + tensor var_10135_cast = softmax(axis = var_6849, x = attn_weights_411_cast)[name = tensor("op_10135_cast")]; + tensor attn_205_transpose_x_0 = const()[name = tensor("attn_205_transpose_x_0"), val = tensor(false)]; + tensor attn_205_transpose_y_0 = const()[name = tensor("attn_205_transpose_y_0"), val = tensor(true)]; + tensor attn_205_cast = matmul(transpose_x = attn_205_transpose_x_0, transpose_y = attn_205_transpose_y_0, x = var_10131_cast, y = var_10135_cast)[name = tensor("attn_205_cast")]; + tensor var_10139 = const()[name = tensor("op_10139"), val = tensor([2, 1280, 1, -1])]; + tensor input_593_cast = reshape(shape = var_10139, x = attn_205_cast)[name = tensor("input_593_cast")]; + tensor var_10144 = const()[name = tensor("op_10144"), val = tensor([1, 1])]; + tensor var_10146 = const()[name = tensor("op_10146"), val = tensor([1, 1])]; + tensor var_10148_pad_type_0 = const()[name = tensor("op_10148_pad_type_0"), val = tensor("custom")]; + tensor var_10148_pad_0 = const()[name = tensor("op_10148_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3884872128)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3888148992)))]; + tensor var_10148_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_10146, groups = var_6865, pad = var_10148_pad_0, pad_type = var_10148_pad_type_0, strides = var_10144, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_593_cast)[name = tensor("op_10148_cast")]; + tensor inputs_309_cast = add(x = var_10148_cast, y = inputs_307_cast)[name = tensor("inputs_309_cast")]; + tensor var_10152 = const()[name = tensor("op_10152"), val = tensor([1])]; + tensor channels_mean_309_cast = reduce_mean(axes = var_10152, keep_dims = var_6860, x = inputs_309_cast)[name = tensor("channels_mean_309_cast")]; + tensor zero_mean_309_cast = sub(x = inputs_309_cast, y = channels_mean_309_cast)[name = tensor("zero_mean_309_cast")]; + tensor zero_mean_sq_309_cast = mul(x = zero_mean_309_cast, y = zero_mean_309_cast)[name = tensor("zero_mean_sq_309_cast")]; + tensor var_10156 = const()[name = tensor("op_10156"), val = tensor([1])]; + tensor var_10157_cast = reduce_mean(axes = var_10156, keep_dims = var_6860, x = zero_mean_sq_309_cast)[name = tensor("op_10157_cast")]; + tensor var_10158_to_fp16 = const()[name = tensor("op_10158_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10159_cast = add(x = var_10157_cast, y = var_10158_to_fp16)[name = tensor("op_10159_cast")]; + tensor denom_309_epsilon_0_to_fp16 = const()[name = tensor("denom_309_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_309_cast = rsqrt(epsilon = denom_309_epsilon_0_to_fp16, x = var_10159_cast)[name = tensor("denom_309_cast")]; + tensor out_309_cast = mul(x = zero_mean_309_cast, y = denom_309_cast)[name = tensor("out_309_cast")]; + tensor var_10163_to_fp16 = const()[name = tensor("op_10163_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3888151616)))]; + tensor var_10164_cast = add(x = out_309_cast, y = var_10163_to_fp16)[name = tensor("op_10164_cast")]; + tensor var_10166_to_fp16 = const()[name = tensor("op_10166_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3888154240)))]; + tensor hidden_states_405_cast = mul(x = var_10164_cast, y = var_10166_to_fp16)[name = tensor("hidden_states_405_cast")]; + tensor var_10173 = const()[name = tensor("op_10173"), val = tensor([1, 1])]; + tensor var_10175 = const()[name = tensor("op_10175"), val = tensor([1, 1])]; + tensor q_207_pad_type_0 = const()[name = tensor("q_207_pad_type_0"), val = tensor("custom")]; + tensor q_207_pad_0 = const()[name = tensor("q_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3888156864)))]; + tensor q_207_cast = conv(dilations = var_10175, groups = var_6865, pad = q_207_pad_0, pad_type = q_207_pad_type_0, strides = var_10173, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_405_cast)[name = tensor("q_207_cast")]; + tensor var_10179 = const()[name = tensor("op_10179"), val = tensor([1, 1])]; + tensor var_10181 = const()[name = tensor("op_10181"), val = tensor([1, 1])]; + tensor k_207_pad_type_0 = const()[name = tensor("k_207_pad_type_0"), val = tensor("custom")]; + tensor k_207_pad_0 = const()[name = tensor("k_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3891433728)))]; + tensor k_207_cast = conv(dilations = var_10181, groups = var_6865, pad = k_207_pad_0, pad_type = k_207_pad_type_0, strides = var_10179, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_207_cast")]; + tensor var_10185 = const()[name = tensor("op_10185"), val = tensor([1, 1])]; + tensor var_10187 = const()[name = tensor("op_10187"), val = tensor([1, 1])]; + tensor v_207_pad_type_0 = const()[name = tensor("v_207_pad_type_0"), val = tensor("custom")]; + tensor v_207_pad_0 = const()[name = tensor("v_207_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3896676672)))]; + tensor v_207_cast = conv(dilations = var_10187, groups = var_6865, pad = v_207_pad_0, pad_type = v_207_pad_type_0, strides = var_10185, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_207_cast")]; + tensor var_10191 = const()[name = tensor("op_10191"), val = tensor([2, 20, 64, -1])]; + tensor var_10192_cast = reshape(shape = var_10191, x = q_207_cast)[name = tensor("op_10192_cast")]; + tensor var_10193 = const()[name = tensor("op_10193"), val = tensor([2, 20, 64, -1])]; + tensor var_10194_cast = reshape(shape = var_10193, x = k_207_cast)[name = tensor("op_10194_cast")]; + tensor var_10195 = const()[name = tensor("op_10195"), val = tensor([2, 20, 64, -1])]; + tensor var_10196_cast = reshape(shape = var_10195, x = v_207_cast)[name = tensor("op_10196_cast")]; + tensor attn_weights_413_transpose_x_0 = const()[name = tensor("attn_weights_413_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_413_transpose_y_0 = const()[name = tensor("attn_weights_413_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_413_cast = matmul(transpose_x = attn_weights_413_transpose_x_0, transpose_y = attn_weights_413_transpose_y_0, x = var_10192_cast, y = var_10194_cast)[name = tensor("attn_weights_413_cast")]; + tensor attn_weights_415_cast = mul(x = attn_weights_413_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_415_cast")]; + tensor var_10200_cast = softmax(axis = var_6849, x = attn_weights_415_cast)[name = tensor("op_10200_cast")]; + tensor attn_207_transpose_x_0 = const()[name = tensor("attn_207_transpose_x_0"), val = tensor(false)]; + tensor attn_207_transpose_y_0 = const()[name = tensor("attn_207_transpose_y_0"), val = tensor(true)]; + tensor attn_207_cast = matmul(transpose_x = attn_207_transpose_x_0, transpose_y = attn_207_transpose_y_0, x = var_10196_cast, y = var_10200_cast)[name = tensor("attn_207_cast")]; + tensor var_10204 = const()[name = tensor("op_10204"), val = tensor([2, 1280, 1, -1])]; + tensor input_595_cast = reshape(shape = var_10204, x = attn_207_cast)[name = tensor("input_595_cast")]; + tensor var_10209 = const()[name = tensor("op_10209"), val = tensor([1, 1])]; + tensor var_10211 = const()[name = tensor("op_10211"), val = tensor([1, 1])]; + tensor var_10213_pad_type_0 = const()[name = tensor("op_10213_pad_type_0"), val = tensor("custom")]; + tensor var_10213_pad_0 = const()[name = tensor("op_10213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3901919616)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3905196480)))]; + tensor var_10213_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_10211, groups = var_6865, pad = var_10213_pad_0, pad_type = var_10213_pad_type_0, strides = var_10209, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_595_cast)[name = tensor("op_10213_cast")]; + tensor inputs_311_cast = add(x = var_10213_cast, y = inputs_309_cast)[name = tensor("inputs_311_cast")]; + tensor var_10217 = const()[name = tensor("op_10217"), val = tensor([1])]; + tensor channels_mean_311_cast = reduce_mean(axes = var_10217, keep_dims = var_6860, x = inputs_311_cast)[name = tensor("channels_mean_311_cast")]; + tensor zero_mean_311_cast = sub(x = inputs_311_cast, y = channels_mean_311_cast)[name = tensor("zero_mean_311_cast")]; + tensor zero_mean_sq_311_cast = mul(x = zero_mean_311_cast, y = zero_mean_311_cast)[name = tensor("zero_mean_sq_311_cast")]; + tensor var_10221 = const()[name = tensor("op_10221"), val = tensor([1])]; + tensor var_10222_cast = reduce_mean(axes = var_10221, keep_dims = var_6860, x = zero_mean_sq_311_cast)[name = tensor("op_10222_cast")]; + tensor var_10223_to_fp16 = const()[name = tensor("op_10223_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10224_cast = add(x = var_10222_cast, y = var_10223_to_fp16)[name = tensor("op_10224_cast")]; + tensor denom_311_epsilon_0_to_fp16 = const()[name = tensor("denom_311_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_311_cast = rsqrt(epsilon = denom_311_epsilon_0_to_fp16, x = var_10224_cast)[name = tensor("denom_311_cast")]; + tensor out_311_cast = mul(x = zero_mean_311_cast, y = denom_311_cast)[name = tensor("out_311_cast")]; + tensor var_10228_to_fp16 = const()[name = tensor("op_10228_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3905199104)))]; + tensor var_10229_cast = add(x = out_311_cast, y = var_10228_to_fp16)[name = tensor("op_10229_cast")]; + tensor var_10231_to_fp16 = const()[name = tensor("op_10231_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3905201728)))]; + tensor input_597_cast = mul(x = var_10229_cast, y = var_10231_to_fp16)[name = tensor("input_597_cast")]; + tensor var_10239 = const()[name = tensor("op_10239"), val = tensor([1, 1])]; + tensor var_10241 = const()[name = tensor("op_10241"), val = tensor([1, 1])]; + tensor var_10243_pad_type_0 = const()[name = tensor("op_10243_pad_type_0"), val = tensor("custom")]; + tensor var_10243_pad_0 = const()[name = tensor("op_10243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3905204352)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3931418816)))]; + tensor var_10243_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_10241, groups = var_6865, pad = var_10243_pad_0, pad_type = var_10243_pad_type_0, strides = var_10239, weight = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_597_cast)[name = tensor("op_10243_cast")]; + tensor var_10244_split_sizes_0 = const()[name = tensor("op_10244_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10244_axis_0 = const()[name = tensor("op_10244_axis_0"), val = tensor(1)]; + tensor var_10244_cast_0, tensor var_10244_cast_1 = split(axis = var_10244_axis_0, split_sizes = var_10244_split_sizes_0, x = var_10243_cast)[name = tensor("op_10244_cast")]; + tensor var_10246_mode_0 = const()[name = tensor("op_10246_mode_0"), val = tensor("EXACT")]; + tensor var_10246_cast = gelu(mode = var_10246_mode_0, x = var_10244_cast_1)[name = tensor("op_10246_cast")]; + tensor input_599_cast = mul(x = var_10244_cast_0, y = var_10246_cast)[name = tensor("input_599_cast")]; + tensor var_10250 = const()[name = tensor("op_10250"), val = tensor([1, 1])]; + tensor var_10252 = const()[name = tensor("op_10252"), val = tensor([1, 1])]; + tensor var_10254_pad_type_0 = const()[name = tensor("op_10254_pad_type_0"), val = tensor("custom")]; + tensor var_10254_pad_0 = const()[name = tensor("op_10254_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3931439360)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3944546624)))]; + tensor var_10254_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_10252, groups = var_6865, pad = var_10254_pad_0, pad_type = var_10254_pad_type_0, strides = var_10250, weight = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_599_cast)[name = tensor("op_10254_cast")]; + tensor inputs_313_cast = add(x = var_10254_cast, y = inputs_311_cast)[name = tensor("inputs_313_cast")]; + tensor var_10264 = const()[name = tensor("op_10264"), val = tensor([1])]; + tensor channels_mean_313_cast = reduce_mean(axes = var_10264, keep_dims = var_6860, x = inputs_313_cast)[name = tensor("channels_mean_313_cast")]; + tensor zero_mean_313_cast = sub(x = inputs_313_cast, y = channels_mean_313_cast)[name = tensor("zero_mean_313_cast")]; + tensor zero_mean_sq_313_cast = mul(x = zero_mean_313_cast, y = zero_mean_313_cast)[name = tensor("zero_mean_sq_313_cast")]; + tensor var_10268 = const()[name = tensor("op_10268"), val = tensor([1])]; + tensor var_10269_cast = reduce_mean(axes = var_10268, keep_dims = var_6860, x = zero_mean_sq_313_cast)[name = tensor("op_10269_cast")]; + tensor var_10270_to_fp16 = const()[name = tensor("op_10270_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10271_cast = add(x = var_10269_cast, y = var_10270_to_fp16)[name = tensor("op_10271_cast")]; + tensor denom_313_epsilon_0_to_fp16 = const()[name = tensor("denom_313_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_313_cast = rsqrt(epsilon = denom_313_epsilon_0_to_fp16, x = var_10271_cast)[name = tensor("denom_313_cast")]; + tensor out_313_cast = mul(x = zero_mean_313_cast, y = denom_313_cast)[name = tensor("out_313_cast")]; + tensor var_10275_to_fp16 = const()[name = tensor("op_10275_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3944549248)))]; + tensor var_10276_cast = add(x = out_313_cast, y = var_10275_to_fp16)[name = tensor("op_10276_cast")]; + tensor var_10278_to_fp16 = const()[name = tensor("op_10278_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3944551872)))]; + tensor hidden_states_409_cast = mul(x = var_10276_cast, y = var_10278_to_fp16)[name = tensor("hidden_states_409_cast")]; + tensor var_10285 = const()[name = tensor("op_10285"), val = tensor([1, 1])]; + tensor var_10287 = const()[name = tensor("op_10287"), val = tensor([1, 1])]; + tensor q_209_pad_type_0 = const()[name = tensor("q_209_pad_type_0"), val = tensor("custom")]; + tensor q_209_pad_0 = const()[name = tensor("q_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3944554496)))]; + tensor q_209_cast = conv(dilations = var_10287, groups = var_6865, pad = q_209_pad_0, pad_type = q_209_pad_type_0, strides = var_10285, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_409_cast)[name = tensor("q_209_cast")]; + tensor var_10291 = const()[name = tensor("op_10291"), val = tensor([1, 1])]; + tensor var_10293 = const()[name = tensor("op_10293"), val = tensor([1, 1])]; + tensor k_209_pad_type_0 = const()[name = tensor("k_209_pad_type_0"), val = tensor("custom")]; + tensor k_209_pad_0 = const()[name = tensor("k_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3947831360)))]; + tensor k_209_cast = conv(dilations = var_10293, groups = var_6865, pad = k_209_pad_0, pad_type = k_209_pad_type_0, strides = var_10291, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_409_cast)[name = tensor("k_209_cast")]; + tensor var_10297 = const()[name = tensor("op_10297"), val = tensor([1, 1])]; + tensor var_10299 = const()[name = tensor("op_10299"), val = tensor([1, 1])]; + tensor v_209_pad_type_0 = const()[name = tensor("v_209_pad_type_0"), val = tensor("custom")]; + tensor v_209_pad_0 = const()[name = tensor("v_209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3951108224)))]; + tensor v_209_cast = conv(dilations = var_10299, groups = var_6865, pad = v_209_pad_0, pad_type = v_209_pad_type_0, strides = var_10297, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_409_cast)[name = tensor("v_209_cast")]; + tensor var_10303 = const()[name = tensor("op_10303"), val = tensor([2, 20, 64, -1])]; + tensor var_10304_cast = reshape(shape = var_10303, x = q_209_cast)[name = tensor("op_10304_cast")]; + tensor var_10305 = const()[name = tensor("op_10305"), val = tensor([2, 20, 64, -1])]; + tensor var_10306_cast = reshape(shape = var_10305, x = k_209_cast)[name = tensor("op_10306_cast")]; + tensor var_10307 = const()[name = tensor("op_10307"), val = tensor([2, 20, 64, -1])]; + tensor var_10308_cast = reshape(shape = var_10307, x = v_209_cast)[name = tensor("op_10308_cast")]; + tensor attn_weights_417_transpose_x_0 = const()[name = tensor("attn_weights_417_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_417_transpose_y_0 = const()[name = tensor("attn_weights_417_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_417_cast = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_10304_cast, y = var_10306_cast)[name = tensor("attn_weights_417_cast")]; + tensor attn_weights_419_cast = mul(x = attn_weights_417_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_419_cast")]; + tensor var_10312_cast = softmax(axis = var_6849, x = attn_weights_419_cast)[name = tensor("op_10312_cast")]; + tensor attn_209_transpose_x_0 = const()[name = tensor("attn_209_transpose_x_0"), val = tensor(false)]; + tensor attn_209_transpose_y_0 = const()[name = tensor("attn_209_transpose_y_0"), val = tensor(true)]; + tensor attn_209_cast = matmul(transpose_x = attn_209_transpose_x_0, transpose_y = attn_209_transpose_y_0, x = var_10308_cast, y = var_10312_cast)[name = tensor("attn_209_cast")]; + tensor var_10316 = const()[name = tensor("op_10316"), val = tensor([2, 1280, 1, -1])]; + tensor input_601_cast = reshape(shape = var_10316, x = attn_209_cast)[name = tensor("input_601_cast")]; + tensor var_10321 = const()[name = tensor("op_10321"), val = tensor([1, 1])]; + tensor var_10323 = const()[name = tensor("op_10323"), val = tensor([1, 1])]; + tensor var_10325_pad_type_0 = const()[name = tensor("op_10325_pad_type_0"), val = tensor("custom")]; + tensor var_10325_pad_0 = const()[name = tensor("op_10325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3954385088)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3957661952)))]; + tensor var_10325_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_10323, groups = var_6865, pad = var_10325_pad_0, pad_type = var_10325_pad_type_0, strides = var_10321, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_601_cast)[name = tensor("op_10325_cast")]; + tensor inputs_315_cast = add(x = var_10325_cast, y = inputs_313_cast)[name = tensor("inputs_315_cast")]; + tensor var_10329 = const()[name = tensor("op_10329"), val = tensor([1])]; + tensor channels_mean_315_cast = reduce_mean(axes = var_10329, keep_dims = var_6860, x = inputs_315_cast)[name = tensor("channels_mean_315_cast")]; + tensor zero_mean_315_cast = sub(x = inputs_315_cast, y = channels_mean_315_cast)[name = tensor("zero_mean_315_cast")]; + tensor zero_mean_sq_315_cast = mul(x = zero_mean_315_cast, y = zero_mean_315_cast)[name = tensor("zero_mean_sq_315_cast")]; + tensor var_10333 = const()[name = tensor("op_10333"), val = tensor([1])]; + tensor var_10334_cast = reduce_mean(axes = var_10333, keep_dims = var_6860, x = zero_mean_sq_315_cast)[name = tensor("op_10334_cast")]; + tensor var_10335_to_fp16 = const()[name = tensor("op_10335_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10336_cast = add(x = var_10334_cast, y = var_10335_to_fp16)[name = tensor("op_10336_cast")]; + tensor denom_315_epsilon_0_to_fp16 = const()[name = tensor("denom_315_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_315_cast = rsqrt(epsilon = denom_315_epsilon_0_to_fp16, x = var_10336_cast)[name = tensor("denom_315_cast")]; + tensor out_315_cast = mul(x = zero_mean_315_cast, y = denom_315_cast)[name = tensor("out_315_cast")]; + tensor var_10340_to_fp16 = const()[name = tensor("op_10340_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3957664576)))]; + tensor var_10341_cast = add(x = out_315_cast, y = var_10340_to_fp16)[name = tensor("op_10341_cast")]; + tensor var_10343_to_fp16 = const()[name = tensor("op_10343_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3957667200)))]; + tensor hidden_states_411_cast = mul(x = var_10341_cast, y = var_10343_to_fp16)[name = tensor("hidden_states_411_cast")]; + tensor var_10350 = const()[name = tensor("op_10350"), val = tensor([1, 1])]; + tensor var_10352 = const()[name = tensor("op_10352"), val = tensor([1, 1])]; + tensor q_211_pad_type_0 = const()[name = tensor("q_211_pad_type_0"), val = tensor("custom")]; + tensor q_211_pad_0 = const()[name = tensor("q_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3957669824)))]; + tensor q_211_cast = conv(dilations = var_10352, groups = var_6865, pad = q_211_pad_0, pad_type = q_211_pad_type_0, strides = var_10350, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_411_cast)[name = tensor("q_211_cast")]; + tensor var_10356 = const()[name = tensor("op_10356"), val = tensor([1, 1])]; + tensor var_10358 = const()[name = tensor("op_10358"), val = tensor([1, 1])]; + tensor k_211_pad_type_0 = const()[name = tensor("k_211_pad_type_0"), val = tensor("custom")]; + tensor k_211_pad_0 = const()[name = tensor("k_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3960946688)))]; + tensor k_211_cast = conv(dilations = var_10358, groups = var_6865, pad = k_211_pad_0, pad_type = k_211_pad_type_0, strides = var_10356, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_211_cast")]; + tensor var_10362 = const()[name = tensor("op_10362"), val = tensor([1, 1])]; + tensor var_10364 = const()[name = tensor("op_10364"), val = tensor([1, 1])]; + tensor v_211_pad_type_0 = const()[name = tensor("v_211_pad_type_0"), val = tensor("custom")]; + tensor v_211_pad_0 = const()[name = tensor("v_211_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3966189632)))]; + tensor v_211_cast = conv(dilations = var_10364, groups = var_6865, pad = v_211_pad_0, pad_type = v_211_pad_type_0, strides = var_10362, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_211_cast")]; + tensor var_10368 = const()[name = tensor("op_10368"), val = tensor([2, 20, 64, -1])]; + tensor var_10369_cast = reshape(shape = var_10368, x = q_211_cast)[name = tensor("op_10369_cast")]; + tensor var_10370 = const()[name = tensor("op_10370"), val = tensor([2, 20, 64, -1])]; + tensor var_10371_cast = reshape(shape = var_10370, x = k_211_cast)[name = tensor("op_10371_cast")]; + tensor var_10372 = const()[name = tensor("op_10372"), val = tensor([2, 20, 64, -1])]; + tensor var_10373_cast = reshape(shape = var_10372, x = v_211_cast)[name = tensor("op_10373_cast")]; + tensor attn_weights_421_transpose_x_0 = const()[name = tensor("attn_weights_421_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_421_transpose_y_0 = const()[name = tensor("attn_weights_421_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_421_cast = matmul(transpose_x = attn_weights_421_transpose_x_0, transpose_y = attn_weights_421_transpose_y_0, x = var_10369_cast, y = var_10371_cast)[name = tensor("attn_weights_421_cast")]; + tensor attn_weights_423_cast = mul(x = attn_weights_421_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_423_cast")]; + tensor var_10377_cast = softmax(axis = var_6849, x = attn_weights_423_cast)[name = tensor("op_10377_cast")]; + tensor attn_211_transpose_x_0 = const()[name = tensor("attn_211_transpose_x_0"), val = tensor(false)]; + tensor attn_211_transpose_y_0 = const()[name = tensor("attn_211_transpose_y_0"), val = tensor(true)]; + tensor attn_211_cast = matmul(transpose_x = attn_211_transpose_x_0, transpose_y = attn_211_transpose_y_0, x = var_10373_cast, y = var_10377_cast)[name = tensor("attn_211_cast")]; + tensor var_10381 = const()[name = tensor("op_10381"), val = tensor([2, 1280, 1, -1])]; + tensor input_603_cast = reshape(shape = var_10381, x = attn_211_cast)[name = tensor("input_603_cast")]; + tensor var_10386 = const()[name = tensor("op_10386"), val = tensor([1, 1])]; + tensor var_10388 = const()[name = tensor("op_10388"), val = tensor([1, 1])]; + tensor var_10390_pad_type_0 = const()[name = tensor("op_10390_pad_type_0"), val = tensor("custom")]; + tensor var_10390_pad_0 = const()[name = tensor("op_10390_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3971432576)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3974709440)))]; + tensor var_10390_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_10388, groups = var_6865, pad = var_10390_pad_0, pad_type = var_10390_pad_type_0, strides = var_10386, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_603_cast)[name = tensor("op_10390_cast")]; + tensor inputs_317_cast = add(x = var_10390_cast, y = inputs_315_cast)[name = tensor("inputs_317_cast")]; + tensor var_10394 = const()[name = tensor("op_10394"), val = tensor([1])]; + tensor channels_mean_317_cast = reduce_mean(axes = var_10394, keep_dims = var_6860, x = inputs_317_cast)[name = tensor("channels_mean_317_cast")]; + tensor zero_mean_317_cast = sub(x = inputs_317_cast, y = channels_mean_317_cast)[name = tensor("zero_mean_317_cast")]; + tensor zero_mean_sq_317_cast = mul(x = zero_mean_317_cast, y = zero_mean_317_cast)[name = tensor("zero_mean_sq_317_cast")]; + tensor var_10398 = const()[name = tensor("op_10398"), val = tensor([1])]; + tensor var_10399_cast = reduce_mean(axes = var_10398, keep_dims = var_6860, x = zero_mean_sq_317_cast)[name = tensor("op_10399_cast")]; + tensor var_10400_to_fp16 = const()[name = tensor("op_10400_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10401_cast = add(x = var_10399_cast, y = var_10400_to_fp16)[name = tensor("op_10401_cast")]; + tensor denom_317_epsilon_0_to_fp16 = const()[name = tensor("denom_317_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_317_cast = rsqrt(epsilon = denom_317_epsilon_0_to_fp16, x = var_10401_cast)[name = tensor("denom_317_cast")]; + tensor out_317_cast = mul(x = zero_mean_317_cast, y = denom_317_cast)[name = tensor("out_317_cast")]; + tensor var_10405_to_fp16 = const()[name = tensor("op_10405_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3974712064)))]; + tensor var_10406_cast = add(x = out_317_cast, y = var_10405_to_fp16)[name = tensor("op_10406_cast")]; + tensor var_10408_to_fp16 = const()[name = tensor("op_10408_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3974714688)))]; + tensor input_605_cast = mul(x = var_10406_cast, y = var_10408_to_fp16)[name = tensor("input_605_cast")]; + tensor var_10416 = const()[name = tensor("op_10416"), val = tensor([1, 1])]; + tensor var_10418 = const()[name = tensor("op_10418"), val = tensor([1, 1])]; + tensor var_10420_pad_type_0 = const()[name = tensor("op_10420_pad_type_0"), val = tensor("custom")]; + tensor var_10420_pad_0 = const()[name = tensor("op_10420_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3974717312)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4000931776)))]; + tensor var_10420_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_10418, groups = var_6865, pad = var_10420_pad_0, pad_type = var_10420_pad_type_0, strides = var_10416, weight = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_605_cast)[name = tensor("op_10420_cast")]; + tensor var_10421_split_sizes_0 = const()[name = tensor("op_10421_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10421_axis_0 = const()[name = tensor("op_10421_axis_0"), val = tensor(1)]; + tensor var_10421_cast_0, tensor var_10421_cast_1 = split(axis = var_10421_axis_0, split_sizes = var_10421_split_sizes_0, x = var_10420_cast)[name = tensor("op_10421_cast")]; + tensor var_10423_mode_0 = const()[name = tensor("op_10423_mode_0"), val = tensor("EXACT")]; + tensor var_10423_cast = gelu(mode = var_10423_mode_0, x = var_10421_cast_1)[name = tensor("op_10423_cast")]; + tensor input_607_cast = mul(x = var_10421_cast_0, y = var_10423_cast)[name = tensor("input_607_cast")]; + tensor var_10427 = const()[name = tensor("op_10427"), val = tensor([1, 1])]; + tensor var_10429 = const()[name = tensor("op_10429"), val = tensor([1, 1])]; + tensor var_10431_pad_type_0 = const()[name = tensor("op_10431_pad_type_0"), val = tensor("custom")]; + tensor var_10431_pad_0 = const()[name = tensor("op_10431_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4000952320)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4014059584)))]; + tensor var_10431_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_10429, groups = var_6865, pad = var_10431_pad_0, pad_type = var_10431_pad_type_0, strides = var_10427, weight = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_607_cast)[name = tensor("op_10431_cast")]; + tensor inputs_319_cast = add(x = var_10431_cast, y = inputs_317_cast)[name = tensor("inputs_319_cast")]; + tensor var_10441 = const()[name = tensor("op_10441"), val = tensor([1])]; + tensor channels_mean_319_cast = reduce_mean(axes = var_10441, keep_dims = var_6860, x = inputs_319_cast)[name = tensor("channels_mean_319_cast")]; + tensor zero_mean_319_cast = sub(x = inputs_319_cast, y = channels_mean_319_cast)[name = tensor("zero_mean_319_cast")]; + tensor zero_mean_sq_319_cast = mul(x = zero_mean_319_cast, y = zero_mean_319_cast)[name = tensor("zero_mean_sq_319_cast")]; + tensor var_10445 = const()[name = tensor("op_10445"), val = tensor([1])]; + tensor var_10446_cast = reduce_mean(axes = var_10445, keep_dims = var_6860, x = zero_mean_sq_319_cast)[name = tensor("op_10446_cast")]; + tensor var_10447_to_fp16 = const()[name = tensor("op_10447_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10448_cast = add(x = var_10446_cast, y = var_10447_to_fp16)[name = tensor("op_10448_cast")]; + tensor denom_319_epsilon_0_to_fp16 = const()[name = tensor("denom_319_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_319_cast = rsqrt(epsilon = denom_319_epsilon_0_to_fp16, x = var_10448_cast)[name = tensor("denom_319_cast")]; + tensor out_319_cast = mul(x = zero_mean_319_cast, y = denom_319_cast)[name = tensor("out_319_cast")]; + tensor var_10452_to_fp16 = const()[name = tensor("op_10452_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4014062208)))]; + tensor var_10453_cast = add(x = out_319_cast, y = var_10452_to_fp16)[name = tensor("op_10453_cast")]; + tensor var_10455_to_fp16 = const()[name = tensor("op_10455_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4014064832)))]; + tensor hidden_states_415_cast = mul(x = var_10453_cast, y = var_10455_to_fp16)[name = tensor("hidden_states_415_cast")]; + tensor var_10462 = const()[name = tensor("op_10462"), val = tensor([1, 1])]; + tensor var_10464 = const()[name = tensor("op_10464"), val = tensor([1, 1])]; + tensor q_213_pad_type_0 = const()[name = tensor("q_213_pad_type_0"), val = tensor("custom")]; + tensor q_213_pad_0 = const()[name = tensor("q_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4014067456)))]; + tensor q_213_cast = conv(dilations = var_10464, groups = var_6865, pad = q_213_pad_0, pad_type = q_213_pad_type_0, strides = var_10462, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_415_cast)[name = tensor("q_213_cast")]; + tensor var_10468 = const()[name = tensor("op_10468"), val = tensor([1, 1])]; + tensor var_10470 = const()[name = tensor("op_10470"), val = tensor([1, 1])]; + tensor k_213_pad_type_0 = const()[name = tensor("k_213_pad_type_0"), val = tensor("custom")]; + tensor k_213_pad_0 = const()[name = tensor("k_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4017344320)))]; + tensor k_213_cast = conv(dilations = var_10470, groups = var_6865, pad = k_213_pad_0, pad_type = k_213_pad_type_0, strides = var_10468, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_415_cast)[name = tensor("k_213_cast")]; + tensor var_10474 = const()[name = tensor("op_10474"), val = tensor([1, 1])]; + tensor var_10476 = const()[name = tensor("op_10476"), val = tensor([1, 1])]; + tensor v_213_pad_type_0 = const()[name = tensor("v_213_pad_type_0"), val = tensor("custom")]; + tensor v_213_pad_0 = const()[name = tensor("v_213_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4020621184)))]; + tensor v_213_cast = conv(dilations = var_10476, groups = var_6865, pad = v_213_pad_0, pad_type = v_213_pad_type_0, strides = var_10474, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_415_cast)[name = tensor("v_213_cast")]; + tensor var_10480 = const()[name = tensor("op_10480"), val = tensor([2, 20, 64, -1])]; + tensor var_10481_cast = reshape(shape = var_10480, x = q_213_cast)[name = tensor("op_10481_cast")]; + tensor var_10482 = const()[name = tensor("op_10482"), val = tensor([2, 20, 64, -1])]; + tensor var_10483_cast = reshape(shape = var_10482, x = k_213_cast)[name = tensor("op_10483_cast")]; + tensor var_10484 = const()[name = tensor("op_10484"), val = tensor([2, 20, 64, -1])]; + tensor var_10485_cast = reshape(shape = var_10484, x = v_213_cast)[name = tensor("op_10485_cast")]; + tensor attn_weights_425_transpose_x_0 = const()[name = tensor("attn_weights_425_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_425_transpose_y_0 = const()[name = tensor("attn_weights_425_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_425_cast = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_10481_cast, y = var_10483_cast)[name = tensor("attn_weights_425_cast")]; + tensor attn_weights_427_cast = mul(x = attn_weights_425_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_427_cast")]; + tensor var_10489_cast = softmax(axis = var_6849, x = attn_weights_427_cast)[name = tensor("op_10489_cast")]; + tensor attn_213_transpose_x_0 = const()[name = tensor("attn_213_transpose_x_0"), val = tensor(false)]; + tensor attn_213_transpose_y_0 = const()[name = tensor("attn_213_transpose_y_0"), val = tensor(true)]; + tensor attn_213_cast = matmul(transpose_x = attn_213_transpose_x_0, transpose_y = attn_213_transpose_y_0, x = var_10485_cast, y = var_10489_cast)[name = tensor("attn_213_cast")]; + tensor var_10493 = const()[name = tensor("op_10493"), val = tensor([2, 1280, 1, -1])]; + tensor input_609_cast = reshape(shape = var_10493, x = attn_213_cast)[name = tensor("input_609_cast")]; + tensor var_10498 = const()[name = tensor("op_10498"), val = tensor([1, 1])]; + tensor var_10500 = const()[name = tensor("op_10500"), val = tensor([1, 1])]; + tensor var_10502_pad_type_0 = const()[name = tensor("op_10502_pad_type_0"), val = tensor("custom")]; + tensor var_10502_pad_0 = const()[name = tensor("op_10502_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4023898048)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4027174912)))]; + tensor var_10502_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_10500, groups = var_6865, pad = var_10502_pad_0, pad_type = var_10502_pad_type_0, strides = var_10498, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_609_cast)[name = tensor("op_10502_cast")]; + tensor inputs_321_cast = add(x = var_10502_cast, y = inputs_319_cast)[name = tensor("inputs_321_cast")]; + tensor var_10506 = const()[name = tensor("op_10506"), val = tensor([1])]; + tensor channels_mean_321_cast = reduce_mean(axes = var_10506, keep_dims = var_6860, x = inputs_321_cast)[name = tensor("channels_mean_321_cast")]; + tensor zero_mean_321_cast = sub(x = inputs_321_cast, y = channels_mean_321_cast)[name = tensor("zero_mean_321_cast")]; + tensor zero_mean_sq_321_cast = mul(x = zero_mean_321_cast, y = zero_mean_321_cast)[name = tensor("zero_mean_sq_321_cast")]; + tensor var_10510 = const()[name = tensor("op_10510"), val = tensor([1])]; + tensor var_10511_cast = reduce_mean(axes = var_10510, keep_dims = var_6860, x = zero_mean_sq_321_cast)[name = tensor("op_10511_cast")]; + tensor var_10512_to_fp16 = const()[name = tensor("op_10512_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10513_cast = add(x = var_10511_cast, y = var_10512_to_fp16)[name = tensor("op_10513_cast")]; + tensor denom_321_epsilon_0_to_fp16 = const()[name = tensor("denom_321_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_321_cast = rsqrt(epsilon = denom_321_epsilon_0_to_fp16, x = var_10513_cast)[name = tensor("denom_321_cast")]; + tensor out_321_cast = mul(x = zero_mean_321_cast, y = denom_321_cast)[name = tensor("out_321_cast")]; + tensor var_10517_to_fp16 = const()[name = tensor("op_10517_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4027177536)))]; + tensor var_10518_cast = add(x = out_321_cast, y = var_10517_to_fp16)[name = tensor("op_10518_cast")]; + tensor var_10520_to_fp16 = const()[name = tensor("op_10520_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4027180160)))]; + tensor hidden_states_417_cast = mul(x = var_10518_cast, y = var_10520_to_fp16)[name = tensor("hidden_states_417_cast")]; + tensor var_10527 = const()[name = tensor("op_10527"), val = tensor([1, 1])]; + tensor var_10529 = const()[name = tensor("op_10529"), val = tensor([1, 1])]; + tensor q_215_pad_type_0 = const()[name = tensor("q_215_pad_type_0"), val = tensor("custom")]; + tensor q_215_pad_0 = const()[name = tensor("q_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4027182784)))]; + tensor q_215_cast = conv(dilations = var_10529, groups = var_6865, pad = q_215_pad_0, pad_type = q_215_pad_type_0, strides = var_10527, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_417_cast)[name = tensor("q_215_cast")]; + tensor var_10533 = const()[name = tensor("op_10533"), val = tensor([1, 1])]; + tensor var_10535 = const()[name = tensor("op_10535"), val = tensor([1, 1])]; + tensor k_215_pad_type_0 = const()[name = tensor("k_215_pad_type_0"), val = tensor("custom")]; + tensor k_215_pad_0 = const()[name = tensor("k_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4030459648)))]; + tensor k_215_cast = conv(dilations = var_10535, groups = var_6865, pad = k_215_pad_0, pad_type = k_215_pad_type_0, strides = var_10533, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_215_cast")]; + tensor var_10539 = const()[name = tensor("op_10539"), val = tensor([1, 1])]; + tensor var_10541 = const()[name = tensor("op_10541"), val = tensor([1, 1])]; + tensor v_215_pad_type_0 = const()[name = tensor("v_215_pad_type_0"), val = tensor("custom")]; + tensor v_215_pad_0 = const()[name = tensor("v_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4035702592)))]; + tensor v_215_cast = conv(dilations = var_10541, groups = var_6865, pad = v_215_pad_0, pad_type = v_215_pad_type_0, strides = var_10539, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_215_cast")]; + tensor var_10545 = const()[name = tensor("op_10545"), val = tensor([2, 20, 64, -1])]; + tensor var_10546_cast = reshape(shape = var_10545, x = q_215_cast)[name = tensor("op_10546_cast")]; + tensor var_10547 = const()[name = tensor("op_10547"), val = tensor([2, 20, 64, -1])]; + tensor var_10548_cast = reshape(shape = var_10547, x = k_215_cast)[name = tensor("op_10548_cast")]; + tensor var_10549 = const()[name = tensor("op_10549"), val = tensor([2, 20, 64, -1])]; + tensor var_10550_cast = reshape(shape = var_10549, x = v_215_cast)[name = tensor("op_10550_cast")]; + tensor attn_weights_429_transpose_x_0 = const()[name = tensor("attn_weights_429_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_429_transpose_y_0 = const()[name = tensor("attn_weights_429_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_429_cast = matmul(transpose_x = attn_weights_429_transpose_x_0, transpose_y = attn_weights_429_transpose_y_0, x = var_10546_cast, y = var_10548_cast)[name = tensor("attn_weights_429_cast")]; + tensor attn_weights_431_cast = mul(x = attn_weights_429_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_431_cast")]; + tensor var_10554_cast = softmax(axis = var_6849, x = attn_weights_431_cast)[name = tensor("op_10554_cast")]; + tensor attn_215_transpose_x_0 = const()[name = tensor("attn_215_transpose_x_0"), val = tensor(false)]; + tensor attn_215_transpose_y_0 = const()[name = tensor("attn_215_transpose_y_0"), val = tensor(true)]; + tensor attn_215_cast = matmul(transpose_x = attn_215_transpose_x_0, transpose_y = attn_215_transpose_y_0, x = var_10550_cast, y = var_10554_cast)[name = tensor("attn_215_cast")]; + tensor var_10558 = const()[name = tensor("op_10558"), val = tensor([2, 1280, 1, -1])]; + tensor input_611_cast = reshape(shape = var_10558, x = attn_215_cast)[name = tensor("input_611_cast")]; + tensor var_10563 = const()[name = tensor("op_10563"), val = tensor([1, 1])]; + tensor var_10565 = const()[name = tensor("op_10565"), val = tensor([1, 1])]; + tensor var_10567_pad_type_0 = const()[name = tensor("op_10567_pad_type_0"), val = tensor("custom")]; + tensor var_10567_pad_0 = const()[name = tensor("op_10567_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4040945536)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4044222400)))]; + tensor var_10567_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_10565, groups = var_6865, pad = var_10567_pad_0, pad_type = var_10567_pad_type_0, strides = var_10563, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_611_cast)[name = tensor("op_10567_cast")]; + tensor inputs_323_cast = add(x = var_10567_cast, y = inputs_321_cast)[name = tensor("inputs_323_cast")]; + tensor var_10571 = const()[name = tensor("op_10571"), val = tensor([1])]; + tensor channels_mean_323_cast = reduce_mean(axes = var_10571, keep_dims = var_6860, x = inputs_323_cast)[name = tensor("channels_mean_323_cast")]; + tensor zero_mean_323_cast = sub(x = inputs_323_cast, y = channels_mean_323_cast)[name = tensor("zero_mean_323_cast")]; + tensor zero_mean_sq_323_cast = mul(x = zero_mean_323_cast, y = zero_mean_323_cast)[name = tensor("zero_mean_sq_323_cast")]; + tensor var_10575 = const()[name = tensor("op_10575"), val = tensor([1])]; + tensor var_10576_cast = reduce_mean(axes = var_10575, keep_dims = var_6860, x = zero_mean_sq_323_cast)[name = tensor("op_10576_cast")]; + tensor var_10577_to_fp16 = const()[name = tensor("op_10577_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10578_cast = add(x = var_10576_cast, y = var_10577_to_fp16)[name = tensor("op_10578_cast")]; + tensor denom_323_epsilon_0_to_fp16 = const()[name = tensor("denom_323_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_323_cast = rsqrt(epsilon = denom_323_epsilon_0_to_fp16, x = var_10578_cast)[name = tensor("denom_323_cast")]; + tensor out_323_cast = mul(x = zero_mean_323_cast, y = denom_323_cast)[name = tensor("out_323_cast")]; + tensor var_10582_to_fp16 = const()[name = tensor("op_10582_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4044225024)))]; + tensor var_10583_cast = add(x = out_323_cast, y = var_10582_to_fp16)[name = tensor("op_10583_cast")]; + tensor var_10585_to_fp16 = const()[name = tensor("op_10585_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4044227648)))]; + tensor input_613_cast = mul(x = var_10583_cast, y = var_10585_to_fp16)[name = tensor("input_613_cast")]; + tensor var_10593 = const()[name = tensor("op_10593"), val = tensor([1, 1])]; + tensor var_10595 = const()[name = tensor("op_10595"), val = tensor([1, 1])]; + tensor var_10597_pad_type_0 = const()[name = tensor("op_10597_pad_type_0"), val = tensor("custom")]; + tensor var_10597_pad_0 = const()[name = tensor("op_10597_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4044230272)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4070444736)))]; + tensor var_10597_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_10595, groups = var_6865, pad = var_10597_pad_0, pad_type = var_10597_pad_type_0, strides = var_10593, weight = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_613_cast)[name = tensor("op_10597_cast")]; + tensor var_10598_split_sizes_0 = const()[name = tensor("op_10598_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10598_axis_0 = const()[name = tensor("op_10598_axis_0"), val = tensor(1)]; + tensor var_10598_cast_0, tensor var_10598_cast_1 = split(axis = var_10598_axis_0, split_sizes = var_10598_split_sizes_0, x = var_10597_cast)[name = tensor("op_10598_cast")]; + tensor var_10600_mode_0 = const()[name = tensor("op_10600_mode_0"), val = tensor("EXACT")]; + tensor var_10600_cast = gelu(mode = var_10600_mode_0, x = var_10598_cast_1)[name = tensor("op_10600_cast")]; + tensor input_615_cast = mul(x = var_10598_cast_0, y = var_10600_cast)[name = tensor("input_615_cast")]; + tensor var_10604 = const()[name = tensor("op_10604"), val = tensor([1, 1])]; + tensor var_10606 = const()[name = tensor("op_10606"), val = tensor([1, 1])]; + tensor var_10608_pad_type_0 = const()[name = tensor("op_10608_pad_type_0"), val = tensor("custom")]; + tensor var_10608_pad_0 = const()[name = tensor("op_10608_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4070465280)))]; + tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4083572544)))]; + tensor var_10608_cast = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_10606, groups = var_6865, pad = var_10608_pad_0, pad_type = var_10608_pad_type_0, strides = var_10604, weight = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_615_cast)[name = tensor("op_10608_cast")]; + tensor hidden_states_421_cast = add(x = var_10608_cast, y = inputs_323_cast)[name = tensor("hidden_states_421_cast")]; + tensor var_10610 = const()[name = tensor("op_10610"), val = tensor([2, 1280, 32, 32])]; + tensor input_617_cast = reshape(shape = var_10610, x = hidden_states_421_cast)[name = tensor("input_617_cast")]; + tensor var_10614 = const()[name = tensor("op_10614"), val = tensor([1, 1])]; + tensor var_10616 = const()[name = tensor("op_10616"), val = tensor([1, 1])]; + tensor hidden_states_423_pad_type_0 = const()[name = tensor("hidden_states_423_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_423_pad_0 = const()[name = tensor("hidden_states_423_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4083575168)))]; + tensor up_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086852032)))]; + tensor hidden_states_423_cast = conv(bias = up_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_10616, groups = var_6865, pad = hidden_states_423_pad_0, pad_type = hidden_states_423_pad_type_0, strides = var_10614, weight = up_blocks_0_attentions_1_proj_out_weight_to_fp16, x = input_617_cast)[name = tensor("hidden_states_423_cast")]; + tensor hidden_states_425_cast = add(x = hidden_states_423_cast, y = hidden_states_357_cast)[name = tensor("hidden_states_425_cast")]; + tensor input_619_interleave_0 = const()[name = tensor("input_619_interleave_0"), val = tensor(false)]; + tensor input_619_cast = concat(axis = var_6865, interleave = input_619_interleave_0, values = (hidden_states_425_cast, input_115_cast))[name = tensor("input_619_cast")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([2, 32, 60, 32, 32])]; + tensor reshape_108_cast = reshape(shape = reshape_108_shape_0, x = input_619_cast)[name = tensor("reshape_108_cast")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81_cast = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast)[name = tensor("reduce_mean_81_cast")]; + tensor sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast)[name = tensor("sub_54_cast")]; + tensor square_27_cast = square(x = sub_54_cast)[name = tensor("square_27_cast")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83_cast = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast)[name = tensor("reduce_mean_83_cast")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_54_cast = add(x = reduce_mean_83_cast, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast")]; + tensor sqrt_27_cast = sqrt(x = add_54_cast)[name = tensor("sqrt_27_cast")]; + tensor real_div_27_cast = real_div(x = sub_54_cast, y = sqrt_27_cast)[name = tensor("real_div_27_cast")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([2, 1920, 32, 32])]; + tensor reshape_109_cast = reshape(shape = reshape_109_shape_0, x = real_div_27_cast)[name = tensor("reshape_109_cast")]; + tensor add_55_mean_0_to_fp16 = const()[name = tensor("add_55_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086854656)))]; + tensor add_55_variance_0_to_fp16 = const()[name = tensor("add_55_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086858560)))]; + tensor add_55_gamma_0_to_fp16 = const()[name = tensor("add_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086862464)))]; + tensor add_55_beta_0_to_fp16 = const()[name = tensor("add_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086866368)))]; + tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_55_cast = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_109_cast)[name = tensor("add_55_cast")]; + tensor input_623_cast = silu(x = add_55_cast)[name = tensor("input_623_cast")]; + tensor var_10634 = const()[name = tensor("op_10634"), val = tensor([1, 1])]; + tensor var_10636 = const()[name = tensor("op_10636"), val = tensor([1, 1])]; + tensor hidden_states_427_pad_type_0 = const()[name = tensor("hidden_states_427_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_427_pad_0 = const()[name = tensor("hidden_states_427_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086870272)))]; + tensor up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4131107136)))]; + tensor hidden_states_427_cast = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_10636, groups = var_6865, pad = hidden_states_427_pad_0, pad_type = hidden_states_427_pad_type_0, strides = var_10634, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_623_cast)[name = tensor("hidden_states_427_cast")]; + tensor var_10642 = const()[name = tensor("op_10642"), val = tensor([1, 1])]; + tensor var_10644 = const()[name = tensor("op_10644"), val = tensor([1, 1])]; + tensor temb_21_pad_type_0 = const()[name = tensor("temb_21_pad_type_0"), val = tensor("custom")]; + tensor temb_21_pad_0 = const()[name = tensor("temb_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4131109760)))]; + tensor up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4134386624)))]; + tensor temb_21_cast = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_10644, groups = var_6865, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_10642, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_21_cast")]; + tensor input_627_cast = add(x = hidden_states_427_cast, y = temb_21_cast)[name = tensor("input_627_cast")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_112_cast = reshape(shape = reshape_112_shape_0, x = input_627_cast)[name = tensor("reshape_112_cast")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84_cast = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast)[name = tensor("reduce_mean_84_cast")]; + tensor sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast)[name = tensor("sub_56_cast")]; + tensor square_28_cast = square(x = sub_56_cast)[name = tensor("square_28_cast")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86_cast = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast)[name = tensor("reduce_mean_86_cast")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_56_cast = add(x = reduce_mean_86_cast, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast")]; + tensor sqrt_28_cast = sqrt(x = add_56_cast)[name = tensor("sqrt_28_cast")]; + tensor real_div_28_cast = real_div(x = sub_56_cast, y = sqrt_28_cast)[name = tensor("real_div_28_cast")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_113_cast = reshape(shape = reshape_113_shape_0, x = real_div_28_cast)[name = tensor("reshape_113_cast")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4134389248)))]; + tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4134391872)))]; + tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_57_cast = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_113_cast)[name = tensor("add_57_cast")]; + tensor input_631_cast = silu(x = add_57_cast)[name = tensor("input_631_cast")]; + tensor var_10654 = const()[name = tensor("op_10654"), val = tensor([1, 1])]; + tensor var_10656 = const()[name = tensor("op_10656"), val = tensor([1, 1])]; + tensor hidden_states_429_pad_type_0 = const()[name = tensor("hidden_states_429_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_429_pad_0 = const()[name = tensor("hidden_states_429_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4134394496)))]; + tensor up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4163885760)))]; + tensor hidden_states_429_cast = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_10656, groups = var_6865, pad = hidden_states_429_pad_0, pad_type = hidden_states_429_pad_type_0, strides = var_10654, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_631_cast)[name = tensor("hidden_states_429_cast")]; + tensor var_10661 = const()[name = tensor("op_10661"), val = tensor([1, 1])]; + tensor var_10663 = const()[name = tensor("op_10663"), val = tensor([1, 1])]; + tensor x_9_pad_type_0 = const()[name = tensor("x_9_pad_type_0"), val = tensor("custom")]; + tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4163888384)))]; + tensor up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4168803648)))]; + tensor x_9_cast = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_10663, groups = var_6865, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_10661, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16, x = input_619_cast)[name = tensor("x_9_cast")]; + tensor hidden_states_431_cast = add(x = x_9_cast, y = hidden_states_429_cast)[name = tensor("hidden_states_431_cast")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_116_cast = reshape(shape = reshape_116_shape_0, x = hidden_states_431_cast)[name = tensor("reshape_116_cast")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87_cast = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast)[name = tensor("reduce_mean_87_cast")]; + tensor sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast)[name = tensor("sub_58_cast")]; + tensor square_29_cast = square(x = sub_58_cast)[name = tensor("square_29_cast")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89_cast = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast)[name = tensor("reduce_mean_89_cast")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_58_cast = add(x = reduce_mean_89_cast, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast")]; + tensor sqrt_29_cast = sqrt(x = add_58_cast)[name = tensor("sqrt_29_cast")]; + tensor real_div_29_cast = real_div(x = sub_58_cast, y = sqrt_29_cast)[name = tensor("real_div_29_cast")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_117_cast = reshape(shape = reshape_117_shape_0, x = real_div_29_cast)[name = tensor("reshape_117_cast")]; + tensor add_59_gamma_0_to_fp16 = const()[name = tensor("add_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4168806272)))]; + tensor add_59_beta_0_to_fp16 = const()[name = tensor("add_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4168808896)))]; + tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_59_cast = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_117_cast)[name = tensor("add_59_cast")]; + tensor var_10701 = const()[name = tensor("op_10701"), val = tensor([1, 1])]; + tensor var_10703 = const()[name = tensor("op_10703"), val = tensor([1, 1])]; + tensor hidden_states_433_pad_type_0 = const()[name = tensor("hidden_states_433_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_433_pad_0 = const()[name = tensor("hidden_states_433_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4168811520)))]; + tensor up_blocks_0_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4172088384)))]; + tensor hidden_states_433_cast = conv(bias = up_blocks_0_attentions_2_proj_in_bias_to_fp16, dilations = var_10703, groups = var_6865, pad = hidden_states_433_pad_0, pad_type = hidden_states_433_pad_type_0, strides = var_10701, weight = up_blocks_0_attentions_2_proj_in_weight_to_fp16, x = add_59_cast)[name = tensor("hidden_states_433_cast")]; + tensor var_10708 = const()[name = tensor("op_10708"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_325_cast = reshape(shape = var_10708, x = hidden_states_433_cast)[name = tensor("inputs_325_cast")]; + tensor var_10718 = const()[name = tensor("op_10718"), val = tensor([1])]; + tensor channels_mean_325_cast = reduce_mean(axes = var_10718, keep_dims = var_6860, x = inputs_325_cast)[name = tensor("channels_mean_325_cast")]; + tensor zero_mean_325_cast = sub(x = inputs_325_cast, y = channels_mean_325_cast)[name = tensor("zero_mean_325_cast")]; + tensor zero_mean_sq_325_cast = mul(x = zero_mean_325_cast, y = zero_mean_325_cast)[name = tensor("zero_mean_sq_325_cast")]; + tensor var_10722 = const()[name = tensor("op_10722"), val = tensor([1])]; + tensor var_10723_cast = reduce_mean(axes = var_10722, keep_dims = var_6860, x = zero_mean_sq_325_cast)[name = tensor("op_10723_cast")]; + tensor var_10724_to_fp16 = const()[name = tensor("op_10724_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10725_cast = add(x = var_10723_cast, y = var_10724_to_fp16)[name = tensor("op_10725_cast")]; + tensor denom_325_epsilon_0_to_fp16 = const()[name = tensor("denom_325_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_325_cast = rsqrt(epsilon = denom_325_epsilon_0_to_fp16, x = var_10725_cast)[name = tensor("denom_325_cast")]; + tensor out_325_cast = mul(x = zero_mean_325_cast, y = denom_325_cast)[name = tensor("out_325_cast")]; + tensor var_10729_to_fp16 = const()[name = tensor("op_10729_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4172091008)))]; + tensor var_10730_cast = add(x = out_325_cast, y = var_10729_to_fp16)[name = tensor("op_10730_cast")]; + tensor var_10732_to_fp16 = const()[name = tensor("op_10732_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4172093632)))]; + tensor hidden_states_435_cast = mul(x = var_10730_cast, y = var_10732_to_fp16)[name = tensor("hidden_states_435_cast")]; + tensor var_10739 = const()[name = tensor("op_10739"), val = tensor([1, 1])]; + tensor var_10741 = const()[name = tensor("op_10741"), val = tensor([1, 1])]; + tensor q_217_pad_type_0 = const()[name = tensor("q_217_pad_type_0"), val = tensor("custom")]; + tensor q_217_pad_0 = const()[name = tensor("q_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4172096256)))]; + tensor q_217_cast = conv(dilations = var_10741, groups = var_6865, pad = q_217_pad_0, pad_type = q_217_pad_type_0, strides = var_10739, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_435_cast)[name = tensor("q_217_cast")]; + tensor var_10745 = const()[name = tensor("op_10745"), val = tensor([1, 1])]; + tensor var_10747 = const()[name = tensor("op_10747"), val = tensor([1, 1])]; + tensor k_217_pad_type_0 = const()[name = tensor("k_217_pad_type_0"), val = tensor("custom")]; + tensor k_217_pad_0 = const()[name = tensor("k_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4175373120)))]; + tensor k_217_cast = conv(dilations = var_10747, groups = var_6865, pad = k_217_pad_0, pad_type = k_217_pad_type_0, strides = var_10745, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_435_cast)[name = tensor("k_217_cast")]; + tensor var_10751 = const()[name = tensor("op_10751"), val = tensor([1, 1])]; + tensor var_10753 = const()[name = tensor("op_10753"), val = tensor([1, 1])]; + tensor v_217_pad_type_0 = const()[name = tensor("v_217_pad_type_0"), val = tensor("custom")]; + tensor v_217_pad_0 = const()[name = tensor("v_217_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4178649984)))]; + tensor v_217_cast = conv(dilations = var_10753, groups = var_6865, pad = v_217_pad_0, pad_type = v_217_pad_type_0, strides = var_10751, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_435_cast)[name = tensor("v_217_cast")]; + tensor var_10757 = const()[name = tensor("op_10757"), val = tensor([2, 20, 64, -1])]; + tensor var_10758_cast = reshape(shape = var_10757, x = q_217_cast)[name = tensor("op_10758_cast")]; + tensor var_10759 = const()[name = tensor("op_10759"), val = tensor([2, 20, 64, -1])]; + tensor var_10760_cast = reshape(shape = var_10759, x = k_217_cast)[name = tensor("op_10760_cast")]; + tensor var_10761 = const()[name = tensor("op_10761"), val = tensor([2, 20, 64, -1])]; + tensor var_10762_cast = reshape(shape = var_10761, x = v_217_cast)[name = tensor("op_10762_cast")]; + tensor attn_weights_433_transpose_x_0 = const()[name = tensor("attn_weights_433_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_433_transpose_y_0 = const()[name = tensor("attn_weights_433_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_433_cast = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_10758_cast, y = var_10760_cast)[name = tensor("attn_weights_433_cast")]; + tensor attn_weights_435_cast = mul(x = attn_weights_433_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_435_cast")]; + tensor var_10766_cast = softmax(axis = var_6849, x = attn_weights_435_cast)[name = tensor("op_10766_cast")]; + tensor attn_217_transpose_x_0 = const()[name = tensor("attn_217_transpose_x_0"), val = tensor(false)]; + tensor attn_217_transpose_y_0 = const()[name = tensor("attn_217_transpose_y_0"), val = tensor(true)]; + tensor attn_217_cast = matmul(transpose_x = attn_217_transpose_x_0, transpose_y = attn_217_transpose_y_0, x = var_10762_cast, y = var_10766_cast)[name = tensor("attn_217_cast")]; + tensor var_10770 = const()[name = tensor("op_10770"), val = tensor([2, 1280, 1, -1])]; + tensor input_635_cast = reshape(shape = var_10770, x = attn_217_cast)[name = tensor("input_635_cast")]; + tensor var_10775 = const()[name = tensor("op_10775"), val = tensor([1, 1])]; + tensor var_10777 = const()[name = tensor("op_10777"), val = tensor([1, 1])]; + tensor var_10779_pad_type_0 = const()[name = tensor("op_10779_pad_type_0"), val = tensor("custom")]; + tensor var_10779_pad_0 = const()[name = tensor("op_10779_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4181926848)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4185203712)))]; + tensor var_10779_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_10777, groups = var_6865, pad = var_10779_pad_0, pad_type = var_10779_pad_type_0, strides = var_10775, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_635_cast)[name = tensor("op_10779_cast")]; + tensor inputs_327_cast = add(x = var_10779_cast, y = inputs_325_cast)[name = tensor("inputs_327_cast")]; + tensor var_10783 = const()[name = tensor("op_10783"), val = tensor([1])]; + tensor channels_mean_327_cast = reduce_mean(axes = var_10783, keep_dims = var_6860, x = inputs_327_cast)[name = tensor("channels_mean_327_cast")]; + tensor zero_mean_327_cast = sub(x = inputs_327_cast, y = channels_mean_327_cast)[name = tensor("zero_mean_327_cast")]; + tensor zero_mean_sq_327_cast = mul(x = zero_mean_327_cast, y = zero_mean_327_cast)[name = tensor("zero_mean_sq_327_cast")]; + tensor var_10787 = const()[name = tensor("op_10787"), val = tensor([1])]; + tensor var_10788_cast = reduce_mean(axes = var_10787, keep_dims = var_6860, x = zero_mean_sq_327_cast)[name = tensor("op_10788_cast")]; + tensor var_10789_to_fp16 = const()[name = tensor("op_10789_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10790_cast = add(x = var_10788_cast, y = var_10789_to_fp16)[name = tensor("op_10790_cast")]; + tensor denom_327_epsilon_0_to_fp16 = const()[name = tensor("denom_327_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_327_cast = rsqrt(epsilon = denom_327_epsilon_0_to_fp16, x = var_10790_cast)[name = tensor("denom_327_cast")]; + tensor out_327_cast = mul(x = zero_mean_327_cast, y = denom_327_cast)[name = tensor("out_327_cast")]; + tensor var_10794_to_fp16 = const()[name = tensor("op_10794_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4185206336)))]; + tensor var_10795_cast = add(x = out_327_cast, y = var_10794_to_fp16)[name = tensor("op_10795_cast")]; + tensor var_10797_to_fp16 = const()[name = tensor("op_10797_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4185208960)))]; + tensor hidden_states_437_cast = mul(x = var_10795_cast, y = var_10797_to_fp16)[name = tensor("hidden_states_437_cast")]; + tensor var_10804 = const()[name = tensor("op_10804"), val = tensor([1, 1])]; + tensor var_10806 = const()[name = tensor("op_10806"), val = tensor([1, 1])]; + tensor q_219_pad_type_0 = const()[name = tensor("q_219_pad_type_0"), val = tensor("custom")]; + tensor q_219_pad_0 = const()[name = tensor("q_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4185211584)))]; + tensor q_219_cast = conv(dilations = var_10806, groups = var_6865, pad = q_219_pad_0, pad_type = q_219_pad_type_0, strides = var_10804, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_437_cast)[name = tensor("q_219_cast")]; + tensor var_10810 = const()[name = tensor("op_10810"), val = tensor([1, 1])]; + tensor var_10812 = const()[name = tensor("op_10812"), val = tensor([1, 1])]; + tensor k_219_pad_type_0 = const()[name = tensor("k_219_pad_type_0"), val = tensor("custom")]; + tensor k_219_pad_0 = const()[name = tensor("k_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4188488448)))]; + tensor k_219_cast = conv(dilations = var_10812, groups = var_6865, pad = k_219_pad_0, pad_type = k_219_pad_type_0, strides = var_10810, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_219_cast")]; + tensor var_10816 = const()[name = tensor("op_10816"), val = tensor([1, 1])]; + tensor var_10818 = const()[name = tensor("op_10818"), val = tensor([1, 1])]; + tensor v_219_pad_type_0 = const()[name = tensor("v_219_pad_type_0"), val = tensor("custom")]; + tensor v_219_pad_0 = const()[name = tensor("v_219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4193731392)))]; + tensor v_219_cast = conv(dilations = var_10818, groups = var_6865, pad = v_219_pad_0, pad_type = v_219_pad_type_0, strides = var_10816, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_219_cast")]; + tensor var_10822 = const()[name = tensor("op_10822"), val = tensor([2, 20, 64, -1])]; + tensor var_10823_cast = reshape(shape = var_10822, x = q_219_cast)[name = tensor("op_10823_cast")]; + tensor var_10824 = const()[name = tensor("op_10824"), val = tensor([2, 20, 64, -1])]; + tensor var_10825_cast = reshape(shape = var_10824, x = k_219_cast)[name = tensor("op_10825_cast")]; + tensor var_10826 = const()[name = tensor("op_10826"), val = tensor([2, 20, 64, -1])]; + tensor var_10827_cast = reshape(shape = var_10826, x = v_219_cast)[name = tensor("op_10827_cast")]; + tensor attn_weights_437_transpose_x_0 = const()[name = tensor("attn_weights_437_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_437_transpose_y_0 = const()[name = tensor("attn_weights_437_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_437_cast = matmul(transpose_x = attn_weights_437_transpose_x_0, transpose_y = attn_weights_437_transpose_y_0, x = var_10823_cast, y = var_10825_cast)[name = tensor("attn_weights_437_cast")]; + tensor attn_weights_439_cast = mul(x = attn_weights_437_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_439_cast")]; + tensor var_10831_cast = softmax(axis = var_6849, x = attn_weights_439_cast)[name = tensor("op_10831_cast")]; + tensor attn_219_transpose_x_0 = const()[name = tensor("attn_219_transpose_x_0"), val = tensor(false)]; + tensor attn_219_transpose_y_0 = const()[name = tensor("attn_219_transpose_y_0"), val = tensor(true)]; + tensor attn_219_cast = matmul(transpose_x = attn_219_transpose_x_0, transpose_y = attn_219_transpose_y_0, x = var_10827_cast, y = var_10831_cast)[name = tensor("attn_219_cast")]; + tensor var_10835 = const()[name = tensor("op_10835"), val = tensor([2, 1280, 1, -1])]; + tensor input_637_cast = reshape(shape = var_10835, x = attn_219_cast)[name = tensor("input_637_cast")]; + tensor var_10840 = const()[name = tensor("op_10840"), val = tensor([1, 1])]; + tensor var_10842 = const()[name = tensor("op_10842"), val = tensor([1, 1])]; + tensor var_10844_pad_type_0 = const()[name = tensor("op_10844_pad_type_0"), val = tensor("custom")]; + tensor var_10844_pad_0 = const()[name = tensor("op_10844_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4198974336)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202251200)))]; + tensor var_10844_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_10842, groups = var_6865, pad = var_10844_pad_0, pad_type = var_10844_pad_type_0, strides = var_10840, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_637_cast)[name = tensor("op_10844_cast")]; + tensor inputs_329_cast = add(x = var_10844_cast, y = inputs_327_cast)[name = tensor("inputs_329_cast")]; + tensor var_10848 = const()[name = tensor("op_10848"), val = tensor([1])]; + tensor channels_mean_329_cast = reduce_mean(axes = var_10848, keep_dims = var_6860, x = inputs_329_cast)[name = tensor("channels_mean_329_cast")]; + tensor zero_mean_329_cast = sub(x = inputs_329_cast, y = channels_mean_329_cast)[name = tensor("zero_mean_329_cast")]; + tensor zero_mean_sq_329_cast = mul(x = zero_mean_329_cast, y = zero_mean_329_cast)[name = tensor("zero_mean_sq_329_cast")]; + tensor var_10852 = const()[name = tensor("op_10852"), val = tensor([1])]; + tensor var_10853_cast = reduce_mean(axes = var_10852, keep_dims = var_6860, x = zero_mean_sq_329_cast)[name = tensor("op_10853_cast")]; + tensor var_10854_to_fp16 = const()[name = tensor("op_10854_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10855_cast = add(x = var_10853_cast, y = var_10854_to_fp16)[name = tensor("op_10855_cast")]; + tensor denom_329_epsilon_0_to_fp16 = const()[name = tensor("denom_329_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_329_cast = rsqrt(epsilon = denom_329_epsilon_0_to_fp16, x = var_10855_cast)[name = tensor("denom_329_cast")]; + tensor out_329_cast = mul(x = zero_mean_329_cast, y = denom_329_cast)[name = tensor("out_329_cast")]; + tensor var_10859_to_fp16 = const()[name = tensor("op_10859_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202253824)))]; + tensor var_10860_cast = add(x = out_329_cast, y = var_10859_to_fp16)[name = tensor("op_10860_cast")]; + tensor var_10862_to_fp16 = const()[name = tensor("op_10862_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202256448)))]; + tensor input_639_cast = mul(x = var_10860_cast, y = var_10862_to_fp16)[name = tensor("input_639_cast")]; + tensor var_10870 = const()[name = tensor("op_10870"), val = tensor([1, 1])]; + tensor var_10872 = const()[name = tensor("op_10872"), val = tensor([1, 1])]; + tensor var_10874_pad_type_0 = const()[name = tensor("op_10874_pad_type_0"), val = tensor("custom")]; + tensor var_10874_pad_0 = const()[name = tensor("op_10874_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202259072)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4228473536)))]; + tensor var_10874_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_10872, groups = var_6865, pad = var_10874_pad_0, pad_type = var_10874_pad_type_0, strides = var_10870, weight = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_639_cast)[name = tensor("op_10874_cast")]; + tensor var_10875_split_sizes_0 = const()[name = tensor("op_10875_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10875_axis_0 = const()[name = tensor("op_10875_axis_0"), val = tensor(1)]; + tensor var_10875_cast_0, tensor var_10875_cast_1 = split(axis = var_10875_axis_0, split_sizes = var_10875_split_sizes_0, x = var_10874_cast)[name = tensor("op_10875_cast")]; + tensor var_10877_mode_0 = const()[name = tensor("op_10877_mode_0"), val = tensor("EXACT")]; + tensor var_10877_cast = gelu(mode = var_10877_mode_0, x = var_10875_cast_1)[name = tensor("op_10877_cast")]; + tensor input_641_cast = mul(x = var_10875_cast_0, y = var_10877_cast)[name = tensor("input_641_cast")]; + tensor var_10881 = const()[name = tensor("op_10881"), val = tensor([1, 1])]; + tensor var_10883 = const()[name = tensor("op_10883"), val = tensor([1, 1])]; + tensor var_10885_pad_type_0 = const()[name = tensor("op_10885_pad_type_0"), val = tensor("custom")]; + tensor var_10885_pad_0 = const()[name = tensor("op_10885_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4228494080)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4241601344)))]; + tensor var_10885_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_10883, groups = var_6865, pad = var_10885_pad_0, pad_type = var_10885_pad_type_0, strides = var_10881, weight = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_641_cast)[name = tensor("op_10885_cast")]; + tensor inputs_331_cast = add(x = var_10885_cast, y = inputs_329_cast)[name = tensor("inputs_331_cast")]; + tensor var_10895 = const()[name = tensor("op_10895"), val = tensor([1])]; + tensor channels_mean_331_cast = reduce_mean(axes = var_10895, keep_dims = var_6860, x = inputs_331_cast)[name = tensor("channels_mean_331_cast")]; + tensor zero_mean_331_cast = sub(x = inputs_331_cast, y = channels_mean_331_cast)[name = tensor("zero_mean_331_cast")]; + tensor zero_mean_sq_331_cast = mul(x = zero_mean_331_cast, y = zero_mean_331_cast)[name = tensor("zero_mean_sq_331_cast")]; + tensor var_10899 = const()[name = tensor("op_10899"), val = tensor([1])]; + tensor var_10900_cast = reduce_mean(axes = var_10899, keep_dims = var_6860, x = zero_mean_sq_331_cast)[name = tensor("op_10900_cast")]; + tensor var_10901_to_fp16 = const()[name = tensor("op_10901_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10902_cast = add(x = var_10900_cast, y = var_10901_to_fp16)[name = tensor("op_10902_cast")]; + tensor denom_331_epsilon_0_to_fp16 = const()[name = tensor("denom_331_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_331_cast = rsqrt(epsilon = denom_331_epsilon_0_to_fp16, x = var_10902_cast)[name = tensor("denom_331_cast")]; + tensor out_331_cast = mul(x = zero_mean_331_cast, y = denom_331_cast)[name = tensor("out_331_cast")]; + tensor var_10906_to_fp16 = const()[name = tensor("op_10906_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4241603968)))]; + tensor var_10907_cast = add(x = out_331_cast, y = var_10906_to_fp16)[name = tensor("op_10907_cast")]; + tensor var_10909_to_fp16 = const()[name = tensor("op_10909_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4241606592)))]; + tensor hidden_states_441_cast = mul(x = var_10907_cast, y = var_10909_to_fp16)[name = tensor("hidden_states_441_cast")]; + tensor var_10916 = const()[name = tensor("op_10916"), val = tensor([1, 1])]; + tensor var_10918 = const()[name = tensor("op_10918"), val = tensor([1, 1])]; + tensor q_221_pad_type_0 = const()[name = tensor("q_221_pad_type_0"), val = tensor("custom")]; + tensor q_221_pad_0 = const()[name = tensor("q_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4241609216)))]; + tensor q_221_cast = conv(dilations = var_10918, groups = var_6865, pad = q_221_pad_0, pad_type = q_221_pad_type_0, strides = var_10916, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_441_cast)[name = tensor("q_221_cast")]; + tensor var_10922 = const()[name = tensor("op_10922"), val = tensor([1, 1])]; + tensor var_10924 = const()[name = tensor("op_10924"), val = tensor([1, 1])]; + tensor k_221_pad_type_0 = const()[name = tensor("k_221_pad_type_0"), val = tensor("custom")]; + tensor k_221_pad_0 = const()[name = tensor("k_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4244886080)))]; + tensor k_221_cast = conv(dilations = var_10924, groups = var_6865, pad = k_221_pad_0, pad_type = k_221_pad_type_0, strides = var_10922, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_441_cast)[name = tensor("k_221_cast")]; + tensor var_10928 = const()[name = tensor("op_10928"), val = tensor([1, 1])]; + tensor var_10930 = const()[name = tensor("op_10930"), val = tensor([1, 1])]; + tensor v_221_pad_type_0 = const()[name = tensor("v_221_pad_type_0"), val = tensor("custom")]; + tensor v_221_pad_0 = const()[name = tensor("v_221_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4248162944)))]; + tensor v_221_cast = conv(dilations = var_10930, groups = var_6865, pad = v_221_pad_0, pad_type = v_221_pad_type_0, strides = var_10928, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_441_cast)[name = tensor("v_221_cast")]; + tensor var_10934 = const()[name = tensor("op_10934"), val = tensor([2, 20, 64, -1])]; + tensor var_10935_cast = reshape(shape = var_10934, x = q_221_cast)[name = tensor("op_10935_cast")]; + tensor var_10936 = const()[name = tensor("op_10936"), val = tensor([2, 20, 64, -1])]; + tensor var_10937_cast = reshape(shape = var_10936, x = k_221_cast)[name = tensor("op_10937_cast")]; + tensor var_10938 = const()[name = tensor("op_10938"), val = tensor([2, 20, 64, -1])]; + tensor var_10939_cast = reshape(shape = var_10938, x = v_221_cast)[name = tensor("op_10939_cast")]; + tensor attn_weights_441_transpose_x_0 = const()[name = tensor("attn_weights_441_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_441_transpose_y_0 = const()[name = tensor("attn_weights_441_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_441_cast = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_10935_cast, y = var_10937_cast)[name = tensor("attn_weights_441_cast")]; + tensor attn_weights_443_cast = mul(x = attn_weights_441_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_443_cast")]; + tensor var_10943_cast = softmax(axis = var_6849, x = attn_weights_443_cast)[name = tensor("op_10943_cast")]; + tensor attn_221_transpose_x_0 = const()[name = tensor("attn_221_transpose_x_0"), val = tensor(false)]; + tensor attn_221_transpose_y_0 = const()[name = tensor("attn_221_transpose_y_0"), val = tensor(true)]; + tensor attn_221_cast = matmul(transpose_x = attn_221_transpose_x_0, transpose_y = attn_221_transpose_y_0, x = var_10939_cast, y = var_10943_cast)[name = tensor("attn_221_cast")]; + tensor var_10947 = const()[name = tensor("op_10947"), val = tensor([2, 1280, 1, -1])]; + tensor input_643_cast = reshape(shape = var_10947, x = attn_221_cast)[name = tensor("input_643_cast")]; + tensor var_10952 = const()[name = tensor("op_10952"), val = tensor([1, 1])]; + tensor var_10954 = const()[name = tensor("op_10954"), val = tensor([1, 1])]; + tensor var_10956_pad_type_0 = const()[name = tensor("op_10956_pad_type_0"), val = tensor("custom")]; + tensor var_10956_pad_0 = const()[name = tensor("op_10956_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4251439808)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4254716672)))]; + tensor var_10956_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_10954, groups = var_6865, pad = var_10956_pad_0, pad_type = var_10956_pad_type_0, strides = var_10952, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_643_cast)[name = tensor("op_10956_cast")]; + tensor inputs_333_cast = add(x = var_10956_cast, y = inputs_331_cast)[name = tensor("inputs_333_cast")]; + tensor var_10960 = const()[name = tensor("op_10960"), val = tensor([1])]; + tensor channels_mean_333_cast = reduce_mean(axes = var_10960, keep_dims = var_6860, x = inputs_333_cast)[name = tensor("channels_mean_333_cast")]; + tensor zero_mean_333_cast = sub(x = inputs_333_cast, y = channels_mean_333_cast)[name = tensor("zero_mean_333_cast")]; + tensor zero_mean_sq_333_cast = mul(x = zero_mean_333_cast, y = zero_mean_333_cast)[name = tensor("zero_mean_sq_333_cast")]; + tensor var_10964 = const()[name = tensor("op_10964"), val = tensor([1])]; + tensor var_10965_cast = reduce_mean(axes = var_10964, keep_dims = var_6860, x = zero_mean_sq_333_cast)[name = tensor("op_10965_cast")]; + tensor var_10966_to_fp16 = const()[name = tensor("op_10966_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10967_cast = add(x = var_10965_cast, y = var_10966_to_fp16)[name = tensor("op_10967_cast")]; + tensor denom_333_epsilon_0_to_fp16 = const()[name = tensor("denom_333_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_333_cast = rsqrt(epsilon = denom_333_epsilon_0_to_fp16, x = var_10967_cast)[name = tensor("denom_333_cast")]; + tensor out_333_cast = mul(x = zero_mean_333_cast, y = denom_333_cast)[name = tensor("out_333_cast")]; + tensor var_10971_to_fp16 = const()[name = tensor("op_10971_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4254719296)))]; + tensor var_10972_cast = add(x = out_333_cast, y = var_10971_to_fp16)[name = tensor("op_10972_cast")]; + tensor var_10974_to_fp16 = const()[name = tensor("op_10974_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4254721920)))]; + tensor hidden_states_443_cast = mul(x = var_10972_cast, y = var_10974_to_fp16)[name = tensor("hidden_states_443_cast")]; + tensor var_10981 = const()[name = tensor("op_10981"), val = tensor([1, 1])]; + tensor var_10983 = const()[name = tensor("op_10983"), val = tensor([1, 1])]; + tensor q_223_pad_type_0 = const()[name = tensor("q_223_pad_type_0"), val = tensor("custom")]; + tensor q_223_pad_0 = const()[name = tensor("q_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4254724544)))]; + tensor q_223_cast = conv(dilations = var_10983, groups = var_6865, pad = q_223_pad_0, pad_type = q_223_pad_type_0, strides = var_10981, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_443_cast)[name = tensor("q_223_cast")]; + tensor var_10987 = const()[name = tensor("op_10987"), val = tensor([1, 1])]; + tensor var_10989 = const()[name = tensor("op_10989"), val = tensor([1, 1])]; + tensor k_223_pad_type_0 = const()[name = tensor("k_223_pad_type_0"), val = tensor("custom")]; + tensor k_223_pad_0 = const()[name = tensor("k_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4258001408)))]; + tensor k_223_cast = conv(dilations = var_10989, groups = var_6865, pad = k_223_pad_0, pad_type = k_223_pad_type_0, strides = var_10987, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_223_cast")]; + tensor var_10993 = const()[name = tensor("op_10993"), val = tensor([1, 1])]; + tensor var_10995 = const()[name = tensor("op_10995"), val = tensor([1, 1])]; + tensor v_223_pad_type_0 = const()[name = tensor("v_223_pad_type_0"), val = tensor("custom")]; + tensor v_223_pad_0 = const()[name = tensor("v_223_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4263244352)))]; + tensor v_223_cast = conv(dilations = var_10995, groups = var_6865, pad = v_223_pad_0, pad_type = v_223_pad_type_0, strides = var_10993, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_223_cast")]; + tensor var_10999 = const()[name = tensor("op_10999"), val = tensor([2, 20, 64, -1])]; + tensor var_11000_cast = reshape(shape = var_10999, x = q_223_cast)[name = tensor("op_11000_cast")]; + tensor var_11001 = const()[name = tensor("op_11001"), val = tensor([2, 20, 64, -1])]; + tensor var_11002_cast = reshape(shape = var_11001, x = k_223_cast)[name = tensor("op_11002_cast")]; + tensor var_11003 = const()[name = tensor("op_11003"), val = tensor([2, 20, 64, -1])]; + tensor var_11004_cast = reshape(shape = var_11003, x = v_223_cast)[name = tensor("op_11004_cast")]; + tensor attn_weights_445_transpose_x_0 = const()[name = tensor("attn_weights_445_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_445_transpose_y_0 = const()[name = tensor("attn_weights_445_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_445_cast = matmul(transpose_x = attn_weights_445_transpose_x_0, transpose_y = attn_weights_445_transpose_y_0, x = var_11000_cast, y = var_11002_cast)[name = tensor("attn_weights_445_cast")]; + tensor attn_weights_447_cast = mul(x = attn_weights_445_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_447_cast")]; + tensor var_11008_cast = softmax(axis = var_6849, x = attn_weights_447_cast)[name = tensor("op_11008_cast")]; + tensor attn_223_transpose_x_0 = const()[name = tensor("attn_223_transpose_x_0"), val = tensor(false)]; + tensor attn_223_transpose_y_0 = const()[name = tensor("attn_223_transpose_y_0"), val = tensor(true)]; + tensor attn_223_cast = matmul(transpose_x = attn_223_transpose_x_0, transpose_y = attn_223_transpose_y_0, x = var_11004_cast, y = var_11008_cast)[name = tensor("attn_223_cast")]; + tensor var_11012 = const()[name = tensor("op_11012"), val = tensor([2, 1280, 1, -1])]; + tensor input_645_cast = reshape(shape = var_11012, x = attn_223_cast)[name = tensor("input_645_cast")]; + tensor var_11017 = const()[name = tensor("op_11017"), val = tensor([1, 1])]; + tensor var_11019 = const()[name = tensor("op_11019"), val = tensor([1, 1])]; + tensor var_11021_pad_type_0 = const()[name = tensor("op_11021_pad_type_0"), val = tensor("custom")]; + tensor var_11021_pad_0 = const()[name = tensor("op_11021_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4268487296)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4271764160)))]; + tensor var_11021_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_11019, groups = var_6865, pad = var_11021_pad_0, pad_type = var_11021_pad_type_0, strides = var_11017, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_645_cast)[name = tensor("op_11021_cast")]; + tensor inputs_335_cast = add(x = var_11021_cast, y = inputs_333_cast)[name = tensor("inputs_335_cast")]; + tensor var_11025 = const()[name = tensor("op_11025"), val = tensor([1])]; + tensor channels_mean_335_cast = reduce_mean(axes = var_11025, keep_dims = var_6860, x = inputs_335_cast)[name = tensor("channels_mean_335_cast")]; + tensor zero_mean_335_cast = sub(x = inputs_335_cast, y = channels_mean_335_cast)[name = tensor("zero_mean_335_cast")]; + tensor zero_mean_sq_335_cast = mul(x = zero_mean_335_cast, y = zero_mean_335_cast)[name = tensor("zero_mean_sq_335_cast")]; + tensor var_11029 = const()[name = tensor("op_11029"), val = tensor([1])]; + tensor var_11030_cast = reduce_mean(axes = var_11029, keep_dims = var_6860, x = zero_mean_sq_335_cast)[name = tensor("op_11030_cast")]; + tensor var_11031_to_fp16 = const()[name = tensor("op_11031_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11032_cast = add(x = var_11030_cast, y = var_11031_to_fp16)[name = tensor("op_11032_cast")]; + tensor denom_335_epsilon_0_to_fp16 = const()[name = tensor("denom_335_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_335_cast = rsqrt(epsilon = denom_335_epsilon_0_to_fp16, x = var_11032_cast)[name = tensor("denom_335_cast")]; + tensor out_335_cast = mul(x = zero_mean_335_cast, y = denom_335_cast)[name = tensor("out_335_cast")]; + tensor var_11036_to_fp16 = const()[name = tensor("op_11036_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4271766784)))]; + tensor var_11037_cast = add(x = out_335_cast, y = var_11036_to_fp16)[name = tensor("op_11037_cast")]; + tensor var_11039_to_fp16 = const()[name = tensor("op_11039_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4271769408)))]; + tensor input_647_cast = mul(x = var_11037_cast, y = var_11039_to_fp16)[name = tensor("input_647_cast")]; + tensor var_11047 = const()[name = tensor("op_11047"), val = tensor([1, 1])]; + tensor var_11049 = const()[name = tensor("op_11049"), val = tensor([1, 1])]; + tensor var_11051_pad_type_0 = const()[name = tensor("op_11051_pad_type_0"), val = tensor("custom")]; + tensor var_11051_pad_0 = const()[name = tensor("op_11051_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4271772032)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4297986496)))]; + tensor var_11051_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_11049, groups = var_6865, pad = var_11051_pad_0, pad_type = var_11051_pad_type_0, strides = var_11047, weight = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_647_cast)[name = tensor("op_11051_cast")]; + tensor var_11052_split_sizes_0 = const()[name = tensor("op_11052_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11052_axis_0 = const()[name = tensor("op_11052_axis_0"), val = tensor(1)]; + tensor var_11052_cast_0, tensor var_11052_cast_1 = split(axis = var_11052_axis_0, split_sizes = var_11052_split_sizes_0, x = var_11051_cast)[name = tensor("op_11052_cast")]; + tensor var_11054_mode_0 = const()[name = tensor("op_11054_mode_0"), val = tensor("EXACT")]; + tensor var_11054_cast = gelu(mode = var_11054_mode_0, x = var_11052_cast_1)[name = tensor("op_11054_cast")]; + tensor input_649_cast = mul(x = var_11052_cast_0, y = var_11054_cast)[name = tensor("input_649_cast")]; + tensor var_11058 = const()[name = tensor("op_11058"), val = tensor([1, 1])]; + tensor var_11060 = const()[name = tensor("op_11060"), val = tensor([1, 1])]; + tensor var_11062_pad_type_0 = const()[name = tensor("op_11062_pad_type_0"), val = tensor("custom")]; + tensor var_11062_pad_0 = const()[name = tensor("op_11062_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4298007040)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4311114304)))]; + tensor var_11062_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_11060, groups = var_6865, pad = var_11062_pad_0, pad_type = var_11062_pad_type_0, strides = var_11058, weight = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_649_cast)[name = tensor("op_11062_cast")]; + tensor inputs_337_cast = add(x = var_11062_cast, y = inputs_335_cast)[name = tensor("inputs_337_cast")]; + tensor var_11072 = const()[name = tensor("op_11072"), val = tensor([1])]; + tensor channels_mean_337_cast = reduce_mean(axes = var_11072, keep_dims = var_6860, x = inputs_337_cast)[name = tensor("channels_mean_337_cast")]; + tensor zero_mean_337_cast = sub(x = inputs_337_cast, y = channels_mean_337_cast)[name = tensor("zero_mean_337_cast")]; + tensor zero_mean_sq_337_cast = mul(x = zero_mean_337_cast, y = zero_mean_337_cast)[name = tensor("zero_mean_sq_337_cast")]; + tensor var_11076 = const()[name = tensor("op_11076"), val = tensor([1])]; + tensor var_11077_cast = reduce_mean(axes = var_11076, keep_dims = var_6860, x = zero_mean_sq_337_cast)[name = tensor("op_11077_cast")]; + tensor var_11078_to_fp16 = const()[name = tensor("op_11078_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11079_cast = add(x = var_11077_cast, y = var_11078_to_fp16)[name = tensor("op_11079_cast")]; + tensor denom_337_epsilon_0_to_fp16 = const()[name = tensor("denom_337_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_337_cast = rsqrt(epsilon = denom_337_epsilon_0_to_fp16, x = var_11079_cast)[name = tensor("denom_337_cast")]; + tensor out_337_cast = mul(x = zero_mean_337_cast, y = denom_337_cast)[name = tensor("out_337_cast")]; + tensor var_11083_to_fp16 = const()[name = tensor("op_11083_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4311116928)))]; + tensor var_11084_cast = add(x = out_337_cast, y = var_11083_to_fp16)[name = tensor("op_11084_cast")]; + tensor var_11086_to_fp16 = const()[name = tensor("op_11086_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4311119552)))]; + tensor hidden_states_447_cast = mul(x = var_11084_cast, y = var_11086_to_fp16)[name = tensor("hidden_states_447_cast")]; + tensor var_11093 = const()[name = tensor("op_11093"), val = tensor([1, 1])]; + tensor var_11095 = const()[name = tensor("op_11095"), val = tensor([1, 1])]; + tensor q_225_pad_type_0 = const()[name = tensor("q_225_pad_type_0"), val = tensor("custom")]; + tensor q_225_pad_0 = const()[name = tensor("q_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4311122176)))]; + tensor q_225_cast = conv(dilations = var_11095, groups = var_6865, pad = q_225_pad_0, pad_type = q_225_pad_type_0, strides = var_11093, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_447_cast)[name = tensor("q_225_cast")]; + tensor var_11099 = const()[name = tensor("op_11099"), val = tensor([1, 1])]; + tensor var_11101 = const()[name = tensor("op_11101"), val = tensor([1, 1])]; + tensor k_225_pad_type_0 = const()[name = tensor("k_225_pad_type_0"), val = tensor("custom")]; + tensor k_225_pad_0 = const()[name = tensor("k_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4314399040)))]; + tensor k_225_cast = conv(dilations = var_11101, groups = var_6865, pad = k_225_pad_0, pad_type = k_225_pad_type_0, strides = var_11099, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_447_cast)[name = tensor("k_225_cast")]; + tensor var_11105 = const()[name = tensor("op_11105"), val = tensor([1, 1])]; + tensor var_11107 = const()[name = tensor("op_11107"), val = tensor([1, 1])]; + tensor v_225_pad_type_0 = const()[name = tensor("v_225_pad_type_0"), val = tensor("custom")]; + tensor v_225_pad_0 = const()[name = tensor("v_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4317675904)))]; + tensor v_225_cast = conv(dilations = var_11107, groups = var_6865, pad = v_225_pad_0, pad_type = v_225_pad_type_0, strides = var_11105, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_447_cast)[name = tensor("v_225_cast")]; + tensor var_11111 = const()[name = tensor("op_11111"), val = tensor([2, 20, 64, -1])]; + tensor var_11112_cast = reshape(shape = var_11111, x = q_225_cast)[name = tensor("op_11112_cast")]; + tensor var_11113 = const()[name = tensor("op_11113"), val = tensor([2, 20, 64, -1])]; + tensor var_11114_cast = reshape(shape = var_11113, x = k_225_cast)[name = tensor("op_11114_cast")]; + tensor var_11115 = const()[name = tensor("op_11115"), val = tensor([2, 20, 64, -1])]; + tensor var_11116_cast = reshape(shape = var_11115, x = v_225_cast)[name = tensor("op_11116_cast")]; + tensor attn_weights_449_transpose_x_0 = const()[name = tensor("attn_weights_449_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_449_transpose_y_0 = const()[name = tensor("attn_weights_449_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_449_cast = matmul(transpose_x = attn_weights_449_transpose_x_0, transpose_y = attn_weights_449_transpose_y_0, x = var_11112_cast, y = var_11114_cast)[name = tensor("attn_weights_449_cast")]; + tensor attn_weights_451_cast = mul(x = attn_weights_449_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_451_cast")]; + tensor var_11120_cast = softmax(axis = var_6849, x = attn_weights_451_cast)[name = tensor("op_11120_cast")]; + tensor attn_225_transpose_x_0 = const()[name = tensor("attn_225_transpose_x_0"), val = tensor(false)]; + tensor attn_225_transpose_y_0 = const()[name = tensor("attn_225_transpose_y_0"), val = tensor(true)]; + tensor attn_225_cast = matmul(transpose_x = attn_225_transpose_x_0, transpose_y = attn_225_transpose_y_0, x = var_11116_cast, y = var_11120_cast)[name = tensor("attn_225_cast")]; + tensor var_11124 = const()[name = tensor("op_11124"), val = tensor([2, 1280, 1, -1])]; + tensor input_651_cast = reshape(shape = var_11124, x = attn_225_cast)[name = tensor("input_651_cast")]; + tensor var_11129 = const()[name = tensor("op_11129"), val = tensor([1, 1])]; + tensor var_11131 = const()[name = tensor("op_11131"), val = tensor([1, 1])]; + tensor var_11133_pad_type_0 = const()[name = tensor("op_11133_pad_type_0"), val = tensor("custom")]; + tensor var_11133_pad_0 = const()[name = tensor("op_11133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4320952768)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4324229632)))]; + tensor var_11133_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_11131, groups = var_6865, pad = var_11133_pad_0, pad_type = var_11133_pad_type_0, strides = var_11129, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_651_cast)[name = tensor("op_11133_cast")]; + tensor inputs_339_cast = add(x = var_11133_cast, y = inputs_337_cast)[name = tensor("inputs_339_cast")]; + tensor var_11137 = const()[name = tensor("op_11137"), val = tensor([1])]; + tensor channels_mean_339_cast = reduce_mean(axes = var_11137, keep_dims = var_6860, x = inputs_339_cast)[name = tensor("channels_mean_339_cast")]; + tensor zero_mean_339_cast = sub(x = inputs_339_cast, y = channels_mean_339_cast)[name = tensor("zero_mean_339_cast")]; + tensor zero_mean_sq_339_cast = mul(x = zero_mean_339_cast, y = zero_mean_339_cast)[name = tensor("zero_mean_sq_339_cast")]; + tensor var_11141 = const()[name = tensor("op_11141"), val = tensor([1])]; + tensor var_11142_cast = reduce_mean(axes = var_11141, keep_dims = var_6860, x = zero_mean_sq_339_cast)[name = tensor("op_11142_cast")]; + tensor var_11143_to_fp16 = const()[name = tensor("op_11143_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11144_cast = add(x = var_11142_cast, y = var_11143_to_fp16)[name = tensor("op_11144_cast")]; + tensor denom_339_epsilon_0_to_fp16 = const()[name = tensor("denom_339_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_339_cast = rsqrt(epsilon = denom_339_epsilon_0_to_fp16, x = var_11144_cast)[name = tensor("denom_339_cast")]; + tensor out_339_cast = mul(x = zero_mean_339_cast, y = denom_339_cast)[name = tensor("out_339_cast")]; + tensor var_11148_to_fp16 = const()[name = tensor("op_11148_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4324232256)))]; + tensor var_11149_cast = add(x = out_339_cast, y = var_11148_to_fp16)[name = tensor("op_11149_cast")]; + tensor var_11151_to_fp16 = const()[name = tensor("op_11151_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4324234880)))]; + tensor hidden_states_449_cast = mul(x = var_11149_cast, y = var_11151_to_fp16)[name = tensor("hidden_states_449_cast")]; + tensor var_11158 = const()[name = tensor("op_11158"), val = tensor([1, 1])]; + tensor var_11160 = const()[name = tensor("op_11160"), val = tensor([1, 1])]; + tensor q_227_pad_type_0 = const()[name = tensor("q_227_pad_type_0"), val = tensor("custom")]; + tensor q_227_pad_0 = const()[name = tensor("q_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4324237504)))]; + tensor q_227_cast = conv(dilations = var_11160, groups = var_6865, pad = q_227_pad_0, pad_type = q_227_pad_type_0, strides = var_11158, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_449_cast)[name = tensor("q_227_cast")]; + tensor var_11164 = const()[name = tensor("op_11164"), val = tensor([1, 1])]; + tensor var_11166 = const()[name = tensor("op_11166"), val = tensor([1, 1])]; + tensor k_227_pad_type_0 = const()[name = tensor("k_227_pad_type_0"), val = tensor("custom")]; + tensor k_227_pad_0 = const()[name = tensor("k_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4327514368)))]; + tensor k_227_cast = conv(dilations = var_11166, groups = var_6865, pad = k_227_pad_0, pad_type = k_227_pad_type_0, strides = var_11164, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_227_cast")]; + tensor var_11170 = const()[name = tensor("op_11170"), val = tensor([1, 1])]; + tensor var_11172 = const()[name = tensor("op_11172"), val = tensor([1, 1])]; + tensor v_227_pad_type_0 = const()[name = tensor("v_227_pad_type_0"), val = tensor("custom")]; + tensor v_227_pad_0 = const()[name = tensor("v_227_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4332757312)))]; + tensor v_227_cast = conv(dilations = var_11172, groups = var_6865, pad = v_227_pad_0, pad_type = v_227_pad_type_0, strides = var_11170, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_227_cast")]; + tensor var_11176 = const()[name = tensor("op_11176"), val = tensor([2, 20, 64, -1])]; + tensor var_11177_cast = reshape(shape = var_11176, x = q_227_cast)[name = tensor("op_11177_cast")]; + tensor var_11178 = const()[name = tensor("op_11178"), val = tensor([2, 20, 64, -1])]; + tensor var_11179_cast = reshape(shape = var_11178, x = k_227_cast)[name = tensor("op_11179_cast")]; + tensor var_11180 = const()[name = tensor("op_11180"), val = tensor([2, 20, 64, -1])]; + tensor var_11181_cast = reshape(shape = var_11180, x = v_227_cast)[name = tensor("op_11181_cast")]; + tensor attn_weights_453_transpose_x_0 = const()[name = tensor("attn_weights_453_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_453_transpose_y_0 = const()[name = tensor("attn_weights_453_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_453_cast = matmul(transpose_x = attn_weights_453_transpose_x_0, transpose_y = attn_weights_453_transpose_y_0, x = var_11177_cast, y = var_11179_cast)[name = tensor("attn_weights_453_cast")]; + tensor attn_weights_455_cast = mul(x = attn_weights_453_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_455_cast")]; + tensor var_11185_cast = softmax(axis = var_6849, x = attn_weights_455_cast)[name = tensor("op_11185_cast")]; + tensor attn_227_transpose_x_0 = const()[name = tensor("attn_227_transpose_x_0"), val = tensor(false)]; + tensor attn_227_transpose_y_0 = const()[name = tensor("attn_227_transpose_y_0"), val = tensor(true)]; + tensor attn_227_cast = matmul(transpose_x = attn_227_transpose_x_0, transpose_y = attn_227_transpose_y_0, x = var_11181_cast, y = var_11185_cast)[name = tensor("attn_227_cast")]; + tensor var_11189 = const()[name = tensor("op_11189"), val = tensor([2, 1280, 1, -1])]; + tensor input_653_cast = reshape(shape = var_11189, x = attn_227_cast)[name = tensor("input_653_cast")]; + tensor var_11194 = const()[name = tensor("op_11194"), val = tensor([1, 1])]; + tensor var_11196 = const()[name = tensor("op_11196"), val = tensor([1, 1])]; + tensor var_11198_pad_type_0 = const()[name = tensor("op_11198_pad_type_0"), val = tensor("custom")]; + tensor var_11198_pad_0 = const()[name = tensor("op_11198_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4338000256)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4341277120)))]; + tensor var_11198_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_11196, groups = var_6865, pad = var_11198_pad_0, pad_type = var_11198_pad_type_0, strides = var_11194, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_653_cast)[name = tensor("op_11198_cast")]; + tensor inputs_341_cast = add(x = var_11198_cast, y = inputs_339_cast)[name = tensor("inputs_341_cast")]; + tensor var_11202 = const()[name = tensor("op_11202"), val = tensor([1])]; + tensor channels_mean_341_cast = reduce_mean(axes = var_11202, keep_dims = var_6860, x = inputs_341_cast)[name = tensor("channels_mean_341_cast")]; + tensor zero_mean_341_cast = sub(x = inputs_341_cast, y = channels_mean_341_cast)[name = tensor("zero_mean_341_cast")]; + tensor zero_mean_sq_341_cast = mul(x = zero_mean_341_cast, y = zero_mean_341_cast)[name = tensor("zero_mean_sq_341_cast")]; + tensor var_11206 = const()[name = tensor("op_11206"), val = tensor([1])]; + tensor var_11207_cast = reduce_mean(axes = var_11206, keep_dims = var_6860, x = zero_mean_sq_341_cast)[name = tensor("op_11207_cast")]; + tensor var_11208_to_fp16 = const()[name = tensor("op_11208_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11209_cast = add(x = var_11207_cast, y = var_11208_to_fp16)[name = tensor("op_11209_cast")]; + tensor denom_341_epsilon_0_to_fp16 = const()[name = tensor("denom_341_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_341_cast = rsqrt(epsilon = denom_341_epsilon_0_to_fp16, x = var_11209_cast)[name = tensor("denom_341_cast")]; + tensor out_341_cast = mul(x = zero_mean_341_cast, y = denom_341_cast)[name = tensor("out_341_cast")]; + tensor var_11213_to_fp16 = const()[name = tensor("op_11213_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4341279744)))]; + tensor var_11214_cast = add(x = out_341_cast, y = var_11213_to_fp16)[name = tensor("op_11214_cast")]; + tensor var_11216_to_fp16 = const()[name = tensor("op_11216_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4341282368)))]; + tensor input_655_cast = mul(x = var_11214_cast, y = var_11216_to_fp16)[name = tensor("input_655_cast")]; + tensor var_11224 = const()[name = tensor("op_11224"), val = tensor([1, 1])]; + tensor var_11226 = const()[name = tensor("op_11226"), val = tensor([1, 1])]; + tensor var_11228_pad_type_0 = const()[name = tensor("op_11228_pad_type_0"), val = tensor("custom")]; + tensor var_11228_pad_0 = const()[name = tensor("op_11228_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4341284992)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4367499456)))]; + tensor var_11228_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_11226, groups = var_6865, pad = var_11228_pad_0, pad_type = var_11228_pad_type_0, strides = var_11224, weight = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_655_cast)[name = tensor("op_11228_cast")]; + tensor var_11229_split_sizes_0 = const()[name = tensor("op_11229_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11229_axis_0 = const()[name = tensor("op_11229_axis_0"), val = tensor(1)]; + tensor var_11229_cast_0, tensor var_11229_cast_1 = split(axis = var_11229_axis_0, split_sizes = var_11229_split_sizes_0, x = var_11228_cast)[name = tensor("op_11229_cast")]; + tensor var_11231_mode_0 = const()[name = tensor("op_11231_mode_0"), val = tensor("EXACT")]; + tensor var_11231_cast = gelu(mode = var_11231_mode_0, x = var_11229_cast_1)[name = tensor("op_11231_cast")]; + tensor input_657_cast = mul(x = var_11229_cast_0, y = var_11231_cast)[name = tensor("input_657_cast")]; + tensor var_11235 = const()[name = tensor("op_11235"), val = tensor([1, 1])]; + tensor var_11237 = const()[name = tensor("op_11237"), val = tensor([1, 1])]; + tensor var_11239_pad_type_0 = const()[name = tensor("op_11239_pad_type_0"), val = tensor("custom")]; + tensor var_11239_pad_0 = const()[name = tensor("op_11239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4367520000)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4380627264)))]; + tensor var_11239_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_11237, groups = var_6865, pad = var_11239_pad_0, pad_type = var_11239_pad_type_0, strides = var_11235, weight = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_657_cast)[name = tensor("op_11239_cast")]; + tensor inputs_343_cast = add(x = var_11239_cast, y = inputs_341_cast)[name = tensor("inputs_343_cast")]; + tensor var_11249 = const()[name = tensor("op_11249"), val = tensor([1])]; + tensor channels_mean_343_cast = reduce_mean(axes = var_11249, keep_dims = var_6860, x = inputs_343_cast)[name = tensor("channels_mean_343_cast")]; + tensor zero_mean_343_cast = sub(x = inputs_343_cast, y = channels_mean_343_cast)[name = tensor("zero_mean_343_cast")]; + tensor zero_mean_sq_343_cast = mul(x = zero_mean_343_cast, y = zero_mean_343_cast)[name = tensor("zero_mean_sq_343_cast")]; + tensor var_11253 = const()[name = tensor("op_11253"), val = tensor([1])]; + tensor var_11254_cast = reduce_mean(axes = var_11253, keep_dims = var_6860, x = zero_mean_sq_343_cast)[name = tensor("op_11254_cast")]; + tensor var_11255_to_fp16 = const()[name = tensor("op_11255_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11256_cast = add(x = var_11254_cast, y = var_11255_to_fp16)[name = tensor("op_11256_cast")]; + tensor denom_343_epsilon_0_to_fp16 = const()[name = tensor("denom_343_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_343_cast = rsqrt(epsilon = denom_343_epsilon_0_to_fp16, x = var_11256_cast)[name = tensor("denom_343_cast")]; + tensor out_343_cast = mul(x = zero_mean_343_cast, y = denom_343_cast)[name = tensor("out_343_cast")]; + tensor var_11260_to_fp16 = const()[name = tensor("op_11260_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4380629888)))]; + tensor var_11261_cast = add(x = out_343_cast, y = var_11260_to_fp16)[name = tensor("op_11261_cast")]; + tensor var_11263_to_fp16 = const()[name = tensor("op_11263_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4380632512)))]; + tensor hidden_states_453_cast = mul(x = var_11261_cast, y = var_11263_to_fp16)[name = tensor("hidden_states_453_cast")]; + tensor var_11270 = const()[name = tensor("op_11270"), val = tensor([1, 1])]; + tensor var_11272 = const()[name = tensor("op_11272"), val = tensor([1, 1])]; + tensor q_229_pad_type_0 = const()[name = tensor("q_229_pad_type_0"), val = tensor("custom")]; + tensor q_229_pad_0 = const()[name = tensor("q_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4380635136)))]; + tensor q_229_cast = conv(dilations = var_11272, groups = var_6865, pad = q_229_pad_0, pad_type = q_229_pad_type_0, strides = var_11270, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_453_cast)[name = tensor("q_229_cast")]; + tensor var_11276 = const()[name = tensor("op_11276"), val = tensor([1, 1])]; + tensor var_11278 = const()[name = tensor("op_11278"), val = tensor([1, 1])]; + tensor k_229_pad_type_0 = const()[name = tensor("k_229_pad_type_0"), val = tensor("custom")]; + tensor k_229_pad_0 = const()[name = tensor("k_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4383912000)))]; + tensor k_229_cast = conv(dilations = var_11278, groups = var_6865, pad = k_229_pad_0, pad_type = k_229_pad_type_0, strides = var_11276, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_453_cast)[name = tensor("k_229_cast")]; + tensor var_11282 = const()[name = tensor("op_11282"), val = tensor([1, 1])]; + tensor var_11284 = const()[name = tensor("op_11284"), val = tensor([1, 1])]; + tensor v_229_pad_type_0 = const()[name = tensor("v_229_pad_type_0"), val = tensor("custom")]; + tensor v_229_pad_0 = const()[name = tensor("v_229_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4387188864)))]; + tensor v_229_cast = conv(dilations = var_11284, groups = var_6865, pad = v_229_pad_0, pad_type = v_229_pad_type_0, strides = var_11282, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_453_cast)[name = tensor("v_229_cast")]; + tensor var_11288 = const()[name = tensor("op_11288"), val = tensor([2, 20, 64, -1])]; + tensor var_11289_cast = reshape(shape = var_11288, x = q_229_cast)[name = tensor("op_11289_cast")]; + tensor var_11290 = const()[name = tensor("op_11290"), val = tensor([2, 20, 64, -1])]; + tensor var_11291_cast = reshape(shape = var_11290, x = k_229_cast)[name = tensor("op_11291_cast")]; + tensor var_11292 = const()[name = tensor("op_11292"), val = tensor([2, 20, 64, -1])]; + tensor var_11293_cast = reshape(shape = var_11292, x = v_229_cast)[name = tensor("op_11293_cast")]; + tensor attn_weights_457_transpose_x_0 = const()[name = tensor("attn_weights_457_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_457_transpose_y_0 = const()[name = tensor("attn_weights_457_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_457_cast = matmul(transpose_x = attn_weights_457_transpose_x_0, transpose_y = attn_weights_457_transpose_y_0, x = var_11289_cast, y = var_11291_cast)[name = tensor("attn_weights_457_cast")]; + tensor attn_weights_459_cast = mul(x = attn_weights_457_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_459_cast")]; + tensor var_11297_cast = softmax(axis = var_6849, x = attn_weights_459_cast)[name = tensor("op_11297_cast")]; + tensor attn_229_transpose_x_0 = const()[name = tensor("attn_229_transpose_x_0"), val = tensor(false)]; + tensor attn_229_transpose_y_0 = const()[name = tensor("attn_229_transpose_y_0"), val = tensor(true)]; + tensor attn_229_cast = matmul(transpose_x = attn_229_transpose_x_0, transpose_y = attn_229_transpose_y_0, x = var_11293_cast, y = var_11297_cast)[name = tensor("attn_229_cast")]; + tensor var_11301 = const()[name = tensor("op_11301"), val = tensor([2, 1280, 1, -1])]; + tensor input_659_cast = reshape(shape = var_11301, x = attn_229_cast)[name = tensor("input_659_cast")]; + tensor var_11306 = const()[name = tensor("op_11306"), val = tensor([1, 1])]; + tensor var_11308 = const()[name = tensor("op_11308"), val = tensor([1, 1])]; + tensor var_11310_pad_type_0 = const()[name = tensor("op_11310_pad_type_0"), val = tensor("custom")]; + tensor var_11310_pad_0 = const()[name = tensor("op_11310_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4390465728)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4393742592)))]; + tensor var_11310_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_11308, groups = var_6865, pad = var_11310_pad_0, pad_type = var_11310_pad_type_0, strides = var_11306, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_659_cast)[name = tensor("op_11310_cast")]; + tensor inputs_345_cast = add(x = var_11310_cast, y = inputs_343_cast)[name = tensor("inputs_345_cast")]; + tensor var_11314 = const()[name = tensor("op_11314"), val = tensor([1])]; + tensor channels_mean_345_cast = reduce_mean(axes = var_11314, keep_dims = var_6860, x = inputs_345_cast)[name = tensor("channels_mean_345_cast")]; + tensor zero_mean_345_cast = sub(x = inputs_345_cast, y = channels_mean_345_cast)[name = tensor("zero_mean_345_cast")]; + tensor zero_mean_sq_345_cast = mul(x = zero_mean_345_cast, y = zero_mean_345_cast)[name = tensor("zero_mean_sq_345_cast")]; + tensor var_11318 = const()[name = tensor("op_11318"), val = tensor([1])]; + tensor var_11319_cast = reduce_mean(axes = var_11318, keep_dims = var_6860, x = zero_mean_sq_345_cast)[name = tensor("op_11319_cast")]; + tensor var_11320_to_fp16 = const()[name = tensor("op_11320_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11321_cast = add(x = var_11319_cast, y = var_11320_to_fp16)[name = tensor("op_11321_cast")]; + tensor denom_345_epsilon_0_to_fp16 = const()[name = tensor("denom_345_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_345_cast = rsqrt(epsilon = denom_345_epsilon_0_to_fp16, x = var_11321_cast)[name = tensor("denom_345_cast")]; + tensor out_345_cast = mul(x = zero_mean_345_cast, y = denom_345_cast)[name = tensor("out_345_cast")]; + tensor var_11325_to_fp16 = const()[name = tensor("op_11325_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4393745216)))]; + tensor var_11326_cast = add(x = out_345_cast, y = var_11325_to_fp16)[name = tensor("op_11326_cast")]; + tensor var_11328_to_fp16 = const()[name = tensor("op_11328_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4393747840)))]; + tensor hidden_states_455_cast = mul(x = var_11326_cast, y = var_11328_to_fp16)[name = tensor("hidden_states_455_cast")]; + tensor var_11335 = const()[name = tensor("op_11335"), val = tensor([1, 1])]; + tensor var_11337 = const()[name = tensor("op_11337"), val = tensor([1, 1])]; + tensor q_231_pad_type_0 = const()[name = tensor("q_231_pad_type_0"), val = tensor("custom")]; + tensor q_231_pad_0 = const()[name = tensor("q_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4393750464)))]; + tensor q_231_cast = conv(dilations = var_11337, groups = var_6865, pad = q_231_pad_0, pad_type = q_231_pad_type_0, strides = var_11335, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_455_cast)[name = tensor("q_231_cast")]; + tensor var_11341 = const()[name = tensor("op_11341"), val = tensor([1, 1])]; + tensor var_11343 = const()[name = tensor("op_11343"), val = tensor([1, 1])]; + tensor k_231_pad_type_0 = const()[name = tensor("k_231_pad_type_0"), val = tensor("custom")]; + tensor k_231_pad_0 = const()[name = tensor("k_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4397027328)))]; + tensor k_231_cast = conv(dilations = var_11343, groups = var_6865, pad = k_231_pad_0, pad_type = k_231_pad_type_0, strides = var_11341, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_231_cast")]; + tensor var_11347 = const()[name = tensor("op_11347"), val = tensor([1, 1])]; + tensor var_11349 = const()[name = tensor("op_11349"), val = tensor([1, 1])]; + tensor v_231_pad_type_0 = const()[name = tensor("v_231_pad_type_0"), val = tensor("custom")]; + tensor v_231_pad_0 = const()[name = tensor("v_231_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4402270272)))]; + tensor v_231_cast = conv(dilations = var_11349, groups = var_6865, pad = v_231_pad_0, pad_type = v_231_pad_type_0, strides = var_11347, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_231_cast")]; + tensor var_11353 = const()[name = tensor("op_11353"), val = tensor([2, 20, 64, -1])]; + tensor var_11354_cast = reshape(shape = var_11353, x = q_231_cast)[name = tensor("op_11354_cast")]; + tensor var_11355 = const()[name = tensor("op_11355"), val = tensor([2, 20, 64, -1])]; + tensor var_11356_cast = reshape(shape = var_11355, x = k_231_cast)[name = tensor("op_11356_cast")]; + tensor var_11357 = const()[name = tensor("op_11357"), val = tensor([2, 20, 64, -1])]; + tensor var_11358_cast = reshape(shape = var_11357, x = v_231_cast)[name = tensor("op_11358_cast")]; + tensor attn_weights_461_transpose_x_0 = const()[name = tensor("attn_weights_461_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_461_transpose_y_0 = const()[name = tensor("attn_weights_461_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_461_cast = matmul(transpose_x = attn_weights_461_transpose_x_0, transpose_y = attn_weights_461_transpose_y_0, x = var_11354_cast, y = var_11356_cast)[name = tensor("attn_weights_461_cast")]; + tensor attn_weights_463_cast = mul(x = attn_weights_461_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_463_cast")]; + tensor var_11362_cast = softmax(axis = var_6849, x = attn_weights_463_cast)[name = tensor("op_11362_cast")]; + tensor attn_231_transpose_x_0 = const()[name = tensor("attn_231_transpose_x_0"), val = tensor(false)]; + tensor attn_231_transpose_y_0 = const()[name = tensor("attn_231_transpose_y_0"), val = tensor(true)]; + tensor attn_231_cast = matmul(transpose_x = attn_231_transpose_x_0, transpose_y = attn_231_transpose_y_0, x = var_11358_cast, y = var_11362_cast)[name = tensor("attn_231_cast")]; + tensor var_11366 = const()[name = tensor("op_11366"), val = tensor([2, 1280, 1, -1])]; + tensor input_661_cast = reshape(shape = var_11366, x = attn_231_cast)[name = tensor("input_661_cast")]; + tensor var_11371 = const()[name = tensor("op_11371"), val = tensor([1, 1])]; + tensor var_11373 = const()[name = tensor("op_11373"), val = tensor([1, 1])]; + tensor var_11375_pad_type_0 = const()[name = tensor("op_11375_pad_type_0"), val = tensor("custom")]; + tensor var_11375_pad_0 = const()[name = tensor("op_11375_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4407513216)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410790080)))]; + tensor var_11375_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_11373, groups = var_6865, pad = var_11375_pad_0, pad_type = var_11375_pad_type_0, strides = var_11371, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_661_cast)[name = tensor("op_11375_cast")]; + tensor inputs_347_cast = add(x = var_11375_cast, y = inputs_345_cast)[name = tensor("inputs_347_cast")]; + tensor var_11379 = const()[name = tensor("op_11379"), val = tensor([1])]; + tensor channels_mean_347_cast = reduce_mean(axes = var_11379, keep_dims = var_6860, x = inputs_347_cast)[name = tensor("channels_mean_347_cast")]; + tensor zero_mean_347_cast = sub(x = inputs_347_cast, y = channels_mean_347_cast)[name = tensor("zero_mean_347_cast")]; + tensor zero_mean_sq_347_cast = mul(x = zero_mean_347_cast, y = zero_mean_347_cast)[name = tensor("zero_mean_sq_347_cast")]; + tensor var_11383 = const()[name = tensor("op_11383"), val = tensor([1])]; + tensor var_11384_cast = reduce_mean(axes = var_11383, keep_dims = var_6860, x = zero_mean_sq_347_cast)[name = tensor("op_11384_cast")]; + tensor var_11385_to_fp16 = const()[name = tensor("op_11385_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11386_cast = add(x = var_11384_cast, y = var_11385_to_fp16)[name = tensor("op_11386_cast")]; + tensor denom_347_epsilon_0_to_fp16 = const()[name = tensor("denom_347_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_347_cast = rsqrt(epsilon = denom_347_epsilon_0_to_fp16, x = var_11386_cast)[name = tensor("denom_347_cast")]; + tensor out_347_cast = mul(x = zero_mean_347_cast, y = denom_347_cast)[name = tensor("out_347_cast")]; + tensor var_11390_to_fp16 = const()[name = tensor("op_11390_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410792704)))]; + tensor var_11391_cast = add(x = out_347_cast, y = var_11390_to_fp16)[name = tensor("op_11391_cast")]; + tensor var_11393_to_fp16 = const()[name = tensor("op_11393_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410795328)))]; + tensor input_663_cast = mul(x = var_11391_cast, y = var_11393_to_fp16)[name = tensor("input_663_cast")]; + tensor var_11401 = const()[name = tensor("op_11401"), val = tensor([1, 1])]; + tensor var_11403 = const()[name = tensor("op_11403"), val = tensor([1, 1])]; + tensor var_11405_pad_type_0 = const()[name = tensor("op_11405_pad_type_0"), val = tensor("custom")]; + tensor var_11405_pad_0 = const()[name = tensor("op_11405_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410797952)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4437012416)))]; + tensor var_11405_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_11403, groups = var_6865, pad = var_11405_pad_0, pad_type = var_11405_pad_type_0, strides = var_11401, weight = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_663_cast)[name = tensor("op_11405_cast")]; + tensor var_11406_split_sizes_0 = const()[name = tensor("op_11406_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11406_axis_0 = const()[name = tensor("op_11406_axis_0"), val = tensor(1)]; + tensor var_11406_cast_0, tensor var_11406_cast_1 = split(axis = var_11406_axis_0, split_sizes = var_11406_split_sizes_0, x = var_11405_cast)[name = tensor("op_11406_cast")]; + tensor var_11408_mode_0 = const()[name = tensor("op_11408_mode_0"), val = tensor("EXACT")]; + tensor var_11408_cast = gelu(mode = var_11408_mode_0, x = var_11406_cast_1)[name = tensor("op_11408_cast")]; + tensor input_665_cast = mul(x = var_11406_cast_0, y = var_11408_cast)[name = tensor("input_665_cast")]; + tensor var_11412 = const()[name = tensor("op_11412"), val = tensor([1, 1])]; + tensor var_11414 = const()[name = tensor("op_11414"), val = tensor([1, 1])]; + tensor var_11416_pad_type_0 = const()[name = tensor("op_11416_pad_type_0"), val = tensor("custom")]; + tensor var_11416_pad_0 = const()[name = tensor("op_11416_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4437032960)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4450140224)))]; + tensor var_11416_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_11414, groups = var_6865, pad = var_11416_pad_0, pad_type = var_11416_pad_type_0, strides = var_11412, weight = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_665_cast)[name = tensor("op_11416_cast")]; + tensor inputs_349_cast = add(x = var_11416_cast, y = inputs_347_cast)[name = tensor("inputs_349_cast")]; + tensor var_11426 = const()[name = tensor("op_11426"), val = tensor([1])]; + tensor channels_mean_349_cast = reduce_mean(axes = var_11426, keep_dims = var_6860, x = inputs_349_cast)[name = tensor("channels_mean_349_cast")]; + tensor zero_mean_349_cast = sub(x = inputs_349_cast, y = channels_mean_349_cast)[name = tensor("zero_mean_349_cast")]; + tensor zero_mean_sq_349_cast = mul(x = zero_mean_349_cast, y = zero_mean_349_cast)[name = tensor("zero_mean_sq_349_cast")]; + tensor var_11430 = const()[name = tensor("op_11430"), val = tensor([1])]; + tensor var_11431_cast = reduce_mean(axes = var_11430, keep_dims = var_6860, x = zero_mean_sq_349_cast)[name = tensor("op_11431_cast")]; + tensor var_11432_to_fp16 = const()[name = tensor("op_11432_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11433_cast = add(x = var_11431_cast, y = var_11432_to_fp16)[name = tensor("op_11433_cast")]; + tensor denom_349_epsilon_0_to_fp16 = const()[name = tensor("denom_349_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_349_cast = rsqrt(epsilon = denom_349_epsilon_0_to_fp16, x = var_11433_cast)[name = tensor("denom_349_cast")]; + tensor out_349_cast = mul(x = zero_mean_349_cast, y = denom_349_cast)[name = tensor("out_349_cast")]; + tensor var_11437_to_fp16 = const()[name = tensor("op_11437_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4450142848)))]; + tensor var_11438_cast = add(x = out_349_cast, y = var_11437_to_fp16)[name = tensor("op_11438_cast")]; + tensor var_11440_to_fp16 = const()[name = tensor("op_11440_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4450145472)))]; + tensor hidden_states_459_cast = mul(x = var_11438_cast, y = var_11440_to_fp16)[name = tensor("hidden_states_459_cast")]; + tensor var_11447 = const()[name = tensor("op_11447"), val = tensor([1, 1])]; + tensor var_11449 = const()[name = tensor("op_11449"), val = tensor([1, 1])]; + tensor q_233_pad_type_0 = const()[name = tensor("q_233_pad_type_0"), val = tensor("custom")]; + tensor q_233_pad_0 = const()[name = tensor("q_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4450148096)))]; + tensor q_233_cast = conv(dilations = var_11449, groups = var_6865, pad = q_233_pad_0, pad_type = q_233_pad_type_0, strides = var_11447, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_459_cast)[name = tensor("q_233_cast")]; + tensor var_11453 = const()[name = tensor("op_11453"), val = tensor([1, 1])]; + tensor var_11455 = const()[name = tensor("op_11455"), val = tensor([1, 1])]; + tensor k_233_pad_type_0 = const()[name = tensor("k_233_pad_type_0"), val = tensor("custom")]; + tensor k_233_pad_0 = const()[name = tensor("k_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4453424960)))]; + tensor k_233_cast = conv(dilations = var_11455, groups = var_6865, pad = k_233_pad_0, pad_type = k_233_pad_type_0, strides = var_11453, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_459_cast)[name = tensor("k_233_cast")]; + tensor var_11459 = const()[name = tensor("op_11459"), val = tensor([1, 1])]; + tensor var_11461 = const()[name = tensor("op_11461"), val = tensor([1, 1])]; + tensor v_233_pad_type_0 = const()[name = tensor("v_233_pad_type_0"), val = tensor("custom")]; + tensor v_233_pad_0 = const()[name = tensor("v_233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4456701824)))]; + tensor v_233_cast = conv(dilations = var_11461, groups = var_6865, pad = v_233_pad_0, pad_type = v_233_pad_type_0, strides = var_11459, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_459_cast)[name = tensor("v_233_cast")]; + tensor var_11465 = const()[name = tensor("op_11465"), val = tensor([2, 20, 64, -1])]; + tensor var_11466_cast = reshape(shape = var_11465, x = q_233_cast)[name = tensor("op_11466_cast")]; + tensor var_11467 = const()[name = tensor("op_11467"), val = tensor([2, 20, 64, -1])]; + tensor var_11468_cast = reshape(shape = var_11467, x = k_233_cast)[name = tensor("op_11468_cast")]; + tensor var_11469 = const()[name = tensor("op_11469"), val = tensor([2, 20, 64, -1])]; + tensor var_11470_cast = reshape(shape = var_11469, x = v_233_cast)[name = tensor("op_11470_cast")]; + tensor attn_weights_465_transpose_x_0 = const()[name = tensor("attn_weights_465_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_465_transpose_y_0 = const()[name = tensor("attn_weights_465_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_465_cast = matmul(transpose_x = attn_weights_465_transpose_x_0, transpose_y = attn_weights_465_transpose_y_0, x = var_11466_cast, y = var_11468_cast)[name = tensor("attn_weights_465_cast")]; + tensor attn_weights_467_cast = mul(x = attn_weights_465_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_467_cast")]; + tensor var_11474_cast = softmax(axis = var_6849, x = attn_weights_467_cast)[name = tensor("op_11474_cast")]; + tensor attn_233_transpose_x_0 = const()[name = tensor("attn_233_transpose_x_0"), val = tensor(false)]; + tensor attn_233_transpose_y_0 = const()[name = tensor("attn_233_transpose_y_0"), val = tensor(true)]; + tensor attn_233_cast = matmul(transpose_x = attn_233_transpose_x_0, transpose_y = attn_233_transpose_y_0, x = var_11470_cast, y = var_11474_cast)[name = tensor("attn_233_cast")]; + tensor var_11478 = const()[name = tensor("op_11478"), val = tensor([2, 1280, 1, -1])]; + tensor input_667_cast = reshape(shape = var_11478, x = attn_233_cast)[name = tensor("input_667_cast")]; + tensor var_11483 = const()[name = tensor("op_11483"), val = tensor([1, 1])]; + tensor var_11485 = const()[name = tensor("op_11485"), val = tensor([1, 1])]; + tensor var_11487_pad_type_0 = const()[name = tensor("op_11487_pad_type_0"), val = tensor("custom")]; + tensor var_11487_pad_0 = const()[name = tensor("op_11487_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4459978688)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4463255552)))]; + tensor var_11487_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_11485, groups = var_6865, pad = var_11487_pad_0, pad_type = var_11487_pad_type_0, strides = var_11483, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_667_cast)[name = tensor("op_11487_cast")]; + tensor inputs_351_cast = add(x = var_11487_cast, y = inputs_349_cast)[name = tensor("inputs_351_cast")]; + tensor var_11491 = const()[name = tensor("op_11491"), val = tensor([1])]; + tensor channels_mean_351_cast = reduce_mean(axes = var_11491, keep_dims = var_6860, x = inputs_351_cast)[name = tensor("channels_mean_351_cast")]; + tensor zero_mean_351_cast = sub(x = inputs_351_cast, y = channels_mean_351_cast)[name = tensor("zero_mean_351_cast")]; + tensor zero_mean_sq_351_cast = mul(x = zero_mean_351_cast, y = zero_mean_351_cast)[name = tensor("zero_mean_sq_351_cast")]; + tensor var_11495 = const()[name = tensor("op_11495"), val = tensor([1])]; + tensor var_11496_cast = reduce_mean(axes = var_11495, keep_dims = var_6860, x = zero_mean_sq_351_cast)[name = tensor("op_11496_cast")]; + tensor var_11497_to_fp16 = const()[name = tensor("op_11497_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11498_cast = add(x = var_11496_cast, y = var_11497_to_fp16)[name = tensor("op_11498_cast")]; + tensor denom_351_epsilon_0_to_fp16 = const()[name = tensor("denom_351_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_351_cast = rsqrt(epsilon = denom_351_epsilon_0_to_fp16, x = var_11498_cast)[name = tensor("denom_351_cast")]; + tensor out_351_cast = mul(x = zero_mean_351_cast, y = denom_351_cast)[name = tensor("out_351_cast")]; + tensor var_11502_to_fp16 = const()[name = tensor("op_11502_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4463258176)))]; + tensor var_11503_cast = add(x = out_351_cast, y = var_11502_to_fp16)[name = tensor("op_11503_cast")]; + tensor var_11505_to_fp16 = const()[name = tensor("op_11505_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4463260800)))]; + tensor hidden_states_461_cast = mul(x = var_11503_cast, y = var_11505_to_fp16)[name = tensor("hidden_states_461_cast")]; + tensor var_11512 = const()[name = tensor("op_11512"), val = tensor([1, 1])]; + tensor var_11514 = const()[name = tensor("op_11514"), val = tensor([1, 1])]; + tensor q_235_pad_type_0 = const()[name = tensor("q_235_pad_type_0"), val = tensor("custom")]; + tensor q_235_pad_0 = const()[name = tensor("q_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4463263424)))]; + tensor q_235_cast = conv(dilations = var_11514, groups = var_6865, pad = q_235_pad_0, pad_type = q_235_pad_type_0, strides = var_11512, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_461_cast)[name = tensor("q_235_cast")]; + tensor var_11518 = const()[name = tensor("op_11518"), val = tensor([1, 1])]; + tensor var_11520 = const()[name = tensor("op_11520"), val = tensor([1, 1])]; + tensor k_235_pad_type_0 = const()[name = tensor("k_235_pad_type_0"), val = tensor("custom")]; + tensor k_235_pad_0 = const()[name = tensor("k_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4466540288)))]; + tensor k_235_cast = conv(dilations = var_11520, groups = var_6865, pad = k_235_pad_0, pad_type = k_235_pad_type_0, strides = var_11518, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_235_cast")]; + tensor var_11524 = const()[name = tensor("op_11524"), val = tensor([1, 1])]; + tensor var_11526 = const()[name = tensor("op_11526"), val = tensor([1, 1])]; + tensor v_235_pad_type_0 = const()[name = tensor("v_235_pad_type_0"), val = tensor("custom")]; + tensor v_235_pad_0 = const()[name = tensor("v_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4471783232)))]; + tensor v_235_cast = conv(dilations = var_11526, groups = var_6865, pad = v_235_pad_0, pad_type = v_235_pad_type_0, strides = var_11524, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_235_cast")]; + tensor var_11530 = const()[name = tensor("op_11530"), val = tensor([2, 20, 64, -1])]; + tensor var_11531_cast = reshape(shape = var_11530, x = q_235_cast)[name = tensor("op_11531_cast")]; + tensor var_11532 = const()[name = tensor("op_11532"), val = tensor([2, 20, 64, -1])]; + tensor var_11533_cast = reshape(shape = var_11532, x = k_235_cast)[name = tensor("op_11533_cast")]; + tensor var_11534 = const()[name = tensor("op_11534"), val = tensor([2, 20, 64, -1])]; + tensor var_11535_cast = reshape(shape = var_11534, x = v_235_cast)[name = tensor("op_11535_cast")]; + tensor attn_weights_469_transpose_x_0 = const()[name = tensor("attn_weights_469_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_469_transpose_y_0 = const()[name = tensor("attn_weights_469_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_469_cast = matmul(transpose_x = attn_weights_469_transpose_x_0, transpose_y = attn_weights_469_transpose_y_0, x = var_11531_cast, y = var_11533_cast)[name = tensor("attn_weights_469_cast")]; + tensor attn_weights_471_cast = mul(x = attn_weights_469_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_471_cast")]; + tensor var_11539_cast = softmax(axis = var_6849, x = attn_weights_471_cast)[name = tensor("op_11539_cast")]; + tensor attn_235_transpose_x_0 = const()[name = tensor("attn_235_transpose_x_0"), val = tensor(false)]; + tensor attn_235_transpose_y_0 = const()[name = tensor("attn_235_transpose_y_0"), val = tensor(true)]; + tensor attn_235_cast = matmul(transpose_x = attn_235_transpose_x_0, transpose_y = attn_235_transpose_y_0, x = var_11535_cast, y = var_11539_cast)[name = tensor("attn_235_cast")]; + tensor var_11543 = const()[name = tensor("op_11543"), val = tensor([2, 1280, 1, -1])]; + tensor input_669_cast = reshape(shape = var_11543, x = attn_235_cast)[name = tensor("input_669_cast")]; + tensor var_11548 = const()[name = tensor("op_11548"), val = tensor([1, 1])]; + tensor var_11550 = const()[name = tensor("op_11550"), val = tensor([1, 1])]; + tensor var_11552_pad_type_0 = const()[name = tensor("op_11552_pad_type_0"), val = tensor("custom")]; + tensor var_11552_pad_0 = const()[name = tensor("op_11552_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4477026176)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4480303040)))]; + tensor var_11552_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_11550, groups = var_6865, pad = var_11552_pad_0, pad_type = var_11552_pad_type_0, strides = var_11548, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_669_cast)[name = tensor("op_11552_cast")]; + tensor inputs_353_cast = add(x = var_11552_cast, y = inputs_351_cast)[name = tensor("inputs_353_cast")]; + tensor var_11556 = const()[name = tensor("op_11556"), val = tensor([1])]; + tensor channels_mean_353_cast = reduce_mean(axes = var_11556, keep_dims = var_6860, x = inputs_353_cast)[name = tensor("channels_mean_353_cast")]; + tensor zero_mean_353_cast = sub(x = inputs_353_cast, y = channels_mean_353_cast)[name = tensor("zero_mean_353_cast")]; + tensor zero_mean_sq_353_cast = mul(x = zero_mean_353_cast, y = zero_mean_353_cast)[name = tensor("zero_mean_sq_353_cast")]; + tensor var_11560 = const()[name = tensor("op_11560"), val = tensor([1])]; + tensor var_11561_cast = reduce_mean(axes = var_11560, keep_dims = var_6860, x = zero_mean_sq_353_cast)[name = tensor("op_11561_cast")]; + tensor var_11562_to_fp16 = const()[name = tensor("op_11562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11563_cast = add(x = var_11561_cast, y = var_11562_to_fp16)[name = tensor("op_11563_cast")]; + tensor denom_353_epsilon_0_to_fp16 = const()[name = tensor("denom_353_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_353_cast = rsqrt(epsilon = denom_353_epsilon_0_to_fp16, x = var_11563_cast)[name = tensor("denom_353_cast")]; + tensor out_353_cast = mul(x = zero_mean_353_cast, y = denom_353_cast)[name = tensor("out_353_cast")]; + tensor var_11567_to_fp16 = const()[name = tensor("op_11567_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4480305664)))]; + tensor var_11568_cast = add(x = out_353_cast, y = var_11567_to_fp16)[name = tensor("op_11568_cast")]; + tensor var_11570_to_fp16 = const()[name = tensor("op_11570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4480308288)))]; + tensor input_671_cast = mul(x = var_11568_cast, y = var_11570_to_fp16)[name = tensor("input_671_cast")]; + tensor var_11578 = const()[name = tensor("op_11578"), val = tensor([1, 1])]; + tensor var_11580 = const()[name = tensor("op_11580"), val = tensor([1, 1])]; + tensor var_11582_pad_type_0 = const()[name = tensor("op_11582_pad_type_0"), val = tensor("custom")]; + tensor var_11582_pad_0 = const()[name = tensor("op_11582_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4480310912)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4506525376)))]; + tensor var_11582_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_11580, groups = var_6865, pad = var_11582_pad_0, pad_type = var_11582_pad_type_0, strides = var_11578, weight = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_671_cast)[name = tensor("op_11582_cast")]; + tensor var_11583_split_sizes_0 = const()[name = tensor("op_11583_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11583_axis_0 = const()[name = tensor("op_11583_axis_0"), val = tensor(1)]; + tensor var_11583_cast_0, tensor var_11583_cast_1 = split(axis = var_11583_axis_0, split_sizes = var_11583_split_sizes_0, x = var_11582_cast)[name = tensor("op_11583_cast")]; + tensor var_11585_mode_0 = const()[name = tensor("op_11585_mode_0"), val = tensor("EXACT")]; + tensor var_11585_cast = gelu(mode = var_11585_mode_0, x = var_11583_cast_1)[name = tensor("op_11585_cast")]; + tensor input_673_cast = mul(x = var_11583_cast_0, y = var_11585_cast)[name = tensor("input_673_cast")]; + tensor var_11589 = const()[name = tensor("op_11589"), val = tensor([1, 1])]; + tensor var_11591 = const()[name = tensor("op_11591"), val = tensor([1, 1])]; + tensor var_11593_pad_type_0 = const()[name = tensor("op_11593_pad_type_0"), val = tensor("custom")]; + tensor var_11593_pad_0 = const()[name = tensor("op_11593_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4506545920)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519653184)))]; + tensor var_11593_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_11591, groups = var_6865, pad = var_11593_pad_0, pad_type = var_11593_pad_type_0, strides = var_11589, weight = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_673_cast)[name = tensor("op_11593_cast")]; + tensor inputs_355_cast = add(x = var_11593_cast, y = inputs_353_cast)[name = tensor("inputs_355_cast")]; + tensor var_11603 = const()[name = tensor("op_11603"), val = tensor([1])]; + tensor channels_mean_355_cast = reduce_mean(axes = var_11603, keep_dims = var_6860, x = inputs_355_cast)[name = tensor("channels_mean_355_cast")]; + tensor zero_mean_355_cast = sub(x = inputs_355_cast, y = channels_mean_355_cast)[name = tensor("zero_mean_355_cast")]; + tensor zero_mean_sq_355_cast = mul(x = zero_mean_355_cast, y = zero_mean_355_cast)[name = tensor("zero_mean_sq_355_cast")]; + tensor var_11607 = const()[name = tensor("op_11607"), val = tensor([1])]; + tensor var_11608_cast = reduce_mean(axes = var_11607, keep_dims = var_6860, x = zero_mean_sq_355_cast)[name = tensor("op_11608_cast")]; + tensor var_11609_to_fp16 = const()[name = tensor("op_11609_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11610_cast = add(x = var_11608_cast, y = var_11609_to_fp16)[name = tensor("op_11610_cast")]; + tensor denom_355_epsilon_0_to_fp16 = const()[name = tensor("denom_355_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_355_cast = rsqrt(epsilon = denom_355_epsilon_0_to_fp16, x = var_11610_cast)[name = tensor("denom_355_cast")]; + tensor out_355_cast = mul(x = zero_mean_355_cast, y = denom_355_cast)[name = tensor("out_355_cast")]; + tensor var_11614_to_fp16 = const()[name = tensor("op_11614_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519655808)))]; + tensor var_11615_cast = add(x = out_355_cast, y = var_11614_to_fp16)[name = tensor("op_11615_cast")]; + tensor var_11617_to_fp16 = const()[name = tensor("op_11617_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519658432)))]; + tensor hidden_states_465_cast = mul(x = var_11615_cast, y = var_11617_to_fp16)[name = tensor("hidden_states_465_cast")]; + tensor var_11624 = const()[name = tensor("op_11624"), val = tensor([1, 1])]; + tensor var_11626 = const()[name = tensor("op_11626"), val = tensor([1, 1])]; + tensor q_237_pad_type_0 = const()[name = tensor("q_237_pad_type_0"), val = tensor("custom")]; + tensor q_237_pad_0 = const()[name = tensor("q_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519661056)))]; + tensor q_237_cast = conv(dilations = var_11626, groups = var_6865, pad = q_237_pad_0, pad_type = q_237_pad_type_0, strides = var_11624, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_465_cast)[name = tensor("q_237_cast")]; + tensor var_11630 = const()[name = tensor("op_11630"), val = tensor([1, 1])]; + tensor var_11632 = const()[name = tensor("op_11632"), val = tensor([1, 1])]; + tensor k_237_pad_type_0 = const()[name = tensor("k_237_pad_type_0"), val = tensor("custom")]; + tensor k_237_pad_0 = const()[name = tensor("k_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4522937920)))]; + tensor k_237_cast = conv(dilations = var_11632, groups = var_6865, pad = k_237_pad_0, pad_type = k_237_pad_type_0, strides = var_11630, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_465_cast)[name = tensor("k_237_cast")]; + tensor var_11636 = const()[name = tensor("op_11636"), val = tensor([1, 1])]; + tensor var_11638 = const()[name = tensor("op_11638"), val = tensor([1, 1])]; + tensor v_237_pad_type_0 = const()[name = tensor("v_237_pad_type_0"), val = tensor("custom")]; + tensor v_237_pad_0 = const()[name = tensor("v_237_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4526214784)))]; + tensor v_237_cast = conv(dilations = var_11638, groups = var_6865, pad = v_237_pad_0, pad_type = v_237_pad_type_0, strides = var_11636, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_465_cast)[name = tensor("v_237_cast")]; + tensor var_11642 = const()[name = tensor("op_11642"), val = tensor([2, 20, 64, -1])]; + tensor var_11643_cast = reshape(shape = var_11642, x = q_237_cast)[name = tensor("op_11643_cast")]; + tensor var_11644 = const()[name = tensor("op_11644"), val = tensor([2, 20, 64, -1])]; + tensor var_11645_cast = reshape(shape = var_11644, x = k_237_cast)[name = tensor("op_11645_cast")]; + tensor var_11646 = const()[name = tensor("op_11646"), val = tensor([2, 20, 64, -1])]; + tensor var_11647_cast = reshape(shape = var_11646, x = v_237_cast)[name = tensor("op_11647_cast")]; + tensor attn_weights_473_transpose_x_0 = const()[name = tensor("attn_weights_473_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_473_transpose_y_0 = const()[name = tensor("attn_weights_473_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_473_cast = matmul(transpose_x = attn_weights_473_transpose_x_0, transpose_y = attn_weights_473_transpose_y_0, x = var_11643_cast, y = var_11645_cast)[name = tensor("attn_weights_473_cast")]; + tensor attn_weights_475_cast = mul(x = attn_weights_473_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_475_cast")]; + tensor var_11651_cast = softmax(axis = var_6849, x = attn_weights_475_cast)[name = tensor("op_11651_cast")]; + tensor attn_237_transpose_x_0 = const()[name = tensor("attn_237_transpose_x_0"), val = tensor(false)]; + tensor attn_237_transpose_y_0 = const()[name = tensor("attn_237_transpose_y_0"), val = tensor(true)]; + tensor attn_237_cast = matmul(transpose_x = attn_237_transpose_x_0, transpose_y = attn_237_transpose_y_0, x = var_11647_cast, y = var_11651_cast)[name = tensor("attn_237_cast")]; + tensor var_11655 = const()[name = tensor("op_11655"), val = tensor([2, 1280, 1, -1])]; + tensor input_675_cast = reshape(shape = var_11655, x = attn_237_cast)[name = tensor("input_675_cast")]; + tensor var_11660 = const()[name = tensor("op_11660"), val = tensor([1, 1])]; + tensor var_11662 = const()[name = tensor("op_11662"), val = tensor([1, 1])]; + tensor var_11664_pad_type_0 = const()[name = tensor("op_11664_pad_type_0"), val = tensor("custom")]; + tensor var_11664_pad_0 = const()[name = tensor("op_11664_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4529491648)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4532768512)))]; + tensor var_11664_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_11662, groups = var_6865, pad = var_11664_pad_0, pad_type = var_11664_pad_type_0, strides = var_11660, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_675_cast)[name = tensor("op_11664_cast")]; + tensor inputs_357_cast = add(x = var_11664_cast, y = inputs_355_cast)[name = tensor("inputs_357_cast")]; + tensor var_11668 = const()[name = tensor("op_11668"), val = tensor([1])]; + tensor channels_mean_357_cast = reduce_mean(axes = var_11668, keep_dims = var_6860, x = inputs_357_cast)[name = tensor("channels_mean_357_cast")]; + tensor zero_mean_357_cast = sub(x = inputs_357_cast, y = channels_mean_357_cast)[name = tensor("zero_mean_357_cast")]; + tensor zero_mean_sq_357_cast = mul(x = zero_mean_357_cast, y = zero_mean_357_cast)[name = tensor("zero_mean_sq_357_cast")]; + tensor var_11672 = const()[name = tensor("op_11672"), val = tensor([1])]; + tensor var_11673_cast = reduce_mean(axes = var_11672, keep_dims = var_6860, x = zero_mean_sq_357_cast)[name = tensor("op_11673_cast")]; + tensor var_11674_to_fp16 = const()[name = tensor("op_11674_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11675_cast = add(x = var_11673_cast, y = var_11674_to_fp16)[name = tensor("op_11675_cast")]; + tensor denom_357_epsilon_0_to_fp16 = const()[name = tensor("denom_357_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_357_cast = rsqrt(epsilon = denom_357_epsilon_0_to_fp16, x = var_11675_cast)[name = tensor("denom_357_cast")]; + tensor out_357_cast = mul(x = zero_mean_357_cast, y = denom_357_cast)[name = tensor("out_357_cast")]; + tensor var_11679_to_fp16 = const()[name = tensor("op_11679_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4532771136)))]; + tensor var_11680_cast = add(x = out_357_cast, y = var_11679_to_fp16)[name = tensor("op_11680_cast")]; + tensor var_11682_to_fp16 = const()[name = tensor("op_11682_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4532773760)))]; + tensor hidden_states_467_cast = mul(x = var_11680_cast, y = var_11682_to_fp16)[name = tensor("hidden_states_467_cast")]; + tensor var_11689 = const()[name = tensor("op_11689"), val = tensor([1, 1])]; + tensor var_11691 = const()[name = tensor("op_11691"), val = tensor([1, 1])]; + tensor q_239_pad_type_0 = const()[name = tensor("q_239_pad_type_0"), val = tensor("custom")]; + tensor q_239_pad_0 = const()[name = tensor("q_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4532776384)))]; + tensor q_239_cast = conv(dilations = var_11691, groups = var_6865, pad = q_239_pad_0, pad_type = q_239_pad_type_0, strides = var_11689, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_467_cast)[name = tensor("q_239_cast")]; + tensor var_11695 = const()[name = tensor("op_11695"), val = tensor([1, 1])]; + tensor var_11697 = const()[name = tensor("op_11697"), val = tensor([1, 1])]; + tensor k_239_pad_type_0 = const()[name = tensor("k_239_pad_type_0"), val = tensor("custom")]; + tensor k_239_pad_0 = const()[name = tensor("k_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4536053248)))]; + tensor k_239_cast = conv(dilations = var_11697, groups = var_6865, pad = k_239_pad_0, pad_type = k_239_pad_type_0, strides = var_11695, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_239_cast")]; + tensor var_11701 = const()[name = tensor("op_11701"), val = tensor([1, 1])]; + tensor var_11703 = const()[name = tensor("op_11703"), val = tensor([1, 1])]; + tensor v_239_pad_type_0 = const()[name = tensor("v_239_pad_type_0"), val = tensor("custom")]; + tensor v_239_pad_0 = const()[name = tensor("v_239_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4541296192)))]; + tensor v_239_cast = conv(dilations = var_11703, groups = var_6865, pad = v_239_pad_0, pad_type = v_239_pad_type_0, strides = var_11701, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_239_cast")]; + tensor var_11707 = const()[name = tensor("op_11707"), val = tensor([2, 20, 64, -1])]; + tensor var_11708_cast = reshape(shape = var_11707, x = q_239_cast)[name = tensor("op_11708_cast")]; + tensor var_11709 = const()[name = tensor("op_11709"), val = tensor([2, 20, 64, -1])]; + tensor var_11710_cast = reshape(shape = var_11709, x = k_239_cast)[name = tensor("op_11710_cast")]; + tensor var_11711 = const()[name = tensor("op_11711"), val = tensor([2, 20, 64, -1])]; + tensor var_11712_cast = reshape(shape = var_11711, x = v_239_cast)[name = tensor("op_11712_cast")]; + tensor attn_weights_477_transpose_x_0 = const()[name = tensor("attn_weights_477_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_477_transpose_y_0 = const()[name = tensor("attn_weights_477_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_477_cast = matmul(transpose_x = attn_weights_477_transpose_x_0, transpose_y = attn_weights_477_transpose_y_0, x = var_11708_cast, y = var_11710_cast)[name = tensor("attn_weights_477_cast")]; + tensor attn_weights_479_cast = mul(x = attn_weights_477_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_479_cast")]; + tensor var_11716_cast = softmax(axis = var_6849, x = attn_weights_479_cast)[name = tensor("op_11716_cast")]; + tensor attn_239_transpose_x_0 = const()[name = tensor("attn_239_transpose_x_0"), val = tensor(false)]; + tensor attn_239_transpose_y_0 = const()[name = tensor("attn_239_transpose_y_0"), val = tensor(true)]; + tensor attn_239_cast = matmul(transpose_x = attn_239_transpose_x_0, transpose_y = attn_239_transpose_y_0, x = var_11712_cast, y = var_11716_cast)[name = tensor("attn_239_cast")]; + tensor var_11720 = const()[name = tensor("op_11720"), val = tensor([2, 1280, 1, -1])]; + tensor input_677_cast = reshape(shape = var_11720, x = attn_239_cast)[name = tensor("input_677_cast")]; + tensor var_11725 = const()[name = tensor("op_11725"), val = tensor([1, 1])]; + tensor var_11727 = const()[name = tensor("op_11727"), val = tensor([1, 1])]; + tensor var_11729_pad_type_0 = const()[name = tensor("op_11729_pad_type_0"), val = tensor("custom")]; + tensor var_11729_pad_0 = const()[name = tensor("op_11729_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4546539136)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4549816000)))]; + tensor var_11729_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_11727, groups = var_6865, pad = var_11729_pad_0, pad_type = var_11729_pad_type_0, strides = var_11725, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_677_cast)[name = tensor("op_11729_cast")]; + tensor inputs_359_cast = add(x = var_11729_cast, y = inputs_357_cast)[name = tensor("inputs_359_cast")]; + tensor var_11733 = const()[name = tensor("op_11733"), val = tensor([1])]; + tensor channels_mean_359_cast = reduce_mean(axes = var_11733, keep_dims = var_6860, x = inputs_359_cast)[name = tensor("channels_mean_359_cast")]; + tensor zero_mean_359_cast = sub(x = inputs_359_cast, y = channels_mean_359_cast)[name = tensor("zero_mean_359_cast")]; + tensor zero_mean_sq_359_cast = mul(x = zero_mean_359_cast, y = zero_mean_359_cast)[name = tensor("zero_mean_sq_359_cast")]; + tensor var_11737 = const()[name = tensor("op_11737"), val = tensor([1])]; + tensor var_11738_cast = reduce_mean(axes = var_11737, keep_dims = var_6860, x = zero_mean_sq_359_cast)[name = tensor("op_11738_cast")]; + tensor var_11739_to_fp16 = const()[name = tensor("op_11739_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11740_cast = add(x = var_11738_cast, y = var_11739_to_fp16)[name = tensor("op_11740_cast")]; + tensor denom_359_epsilon_0_to_fp16 = const()[name = tensor("denom_359_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_359_cast = rsqrt(epsilon = denom_359_epsilon_0_to_fp16, x = var_11740_cast)[name = tensor("denom_359_cast")]; + tensor out_359_cast = mul(x = zero_mean_359_cast, y = denom_359_cast)[name = tensor("out_359_cast")]; + tensor var_11744_to_fp16 = const()[name = tensor("op_11744_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4549818624)))]; + tensor var_11745_cast = add(x = out_359_cast, y = var_11744_to_fp16)[name = tensor("op_11745_cast")]; + tensor var_11747_to_fp16 = const()[name = tensor("op_11747_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4549821248)))]; + tensor input_679_cast = mul(x = var_11745_cast, y = var_11747_to_fp16)[name = tensor("input_679_cast")]; + tensor var_11755 = const()[name = tensor("op_11755"), val = tensor([1, 1])]; + tensor var_11757 = const()[name = tensor("op_11757"), val = tensor([1, 1])]; + tensor var_11759_pad_type_0 = const()[name = tensor("op_11759_pad_type_0"), val = tensor("custom")]; + tensor var_11759_pad_0 = const()[name = tensor("op_11759_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4549823872)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4576038336)))]; + tensor var_11759_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_11757, groups = var_6865, pad = var_11759_pad_0, pad_type = var_11759_pad_type_0, strides = var_11755, weight = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_679_cast)[name = tensor("op_11759_cast")]; + tensor var_11760_split_sizes_0 = const()[name = tensor("op_11760_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11760_axis_0 = const()[name = tensor("op_11760_axis_0"), val = tensor(1)]; + tensor var_11760_cast_0, tensor var_11760_cast_1 = split(axis = var_11760_axis_0, split_sizes = var_11760_split_sizes_0, x = var_11759_cast)[name = tensor("op_11760_cast")]; + tensor var_11762_mode_0 = const()[name = tensor("op_11762_mode_0"), val = tensor("EXACT")]; + tensor var_11762_cast = gelu(mode = var_11762_mode_0, x = var_11760_cast_1)[name = tensor("op_11762_cast")]; + tensor input_681_cast = mul(x = var_11760_cast_0, y = var_11762_cast)[name = tensor("input_681_cast")]; + tensor var_11766 = const()[name = tensor("op_11766"), val = tensor([1, 1])]; + tensor var_11768 = const()[name = tensor("op_11768"), val = tensor([1, 1])]; + tensor var_11770_pad_type_0 = const()[name = tensor("op_11770_pad_type_0"), val = tensor("custom")]; + tensor var_11770_pad_0 = const()[name = tensor("op_11770_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4576058880)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4589166144)))]; + tensor var_11770_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_11768, groups = var_6865, pad = var_11770_pad_0, pad_type = var_11770_pad_type_0, strides = var_11766, weight = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_681_cast)[name = tensor("op_11770_cast")]; + tensor inputs_361_cast = add(x = var_11770_cast, y = inputs_359_cast)[name = tensor("inputs_361_cast")]; + tensor var_11780 = const()[name = tensor("op_11780"), val = tensor([1])]; + tensor channels_mean_361_cast = reduce_mean(axes = var_11780, keep_dims = var_6860, x = inputs_361_cast)[name = tensor("channels_mean_361_cast")]; + tensor zero_mean_361_cast = sub(x = inputs_361_cast, y = channels_mean_361_cast)[name = tensor("zero_mean_361_cast")]; + tensor zero_mean_sq_361_cast = mul(x = zero_mean_361_cast, y = zero_mean_361_cast)[name = tensor("zero_mean_sq_361_cast")]; + tensor var_11784 = const()[name = tensor("op_11784"), val = tensor([1])]; + tensor var_11785_cast = reduce_mean(axes = var_11784, keep_dims = var_6860, x = zero_mean_sq_361_cast)[name = tensor("op_11785_cast")]; + tensor var_11786_to_fp16 = const()[name = tensor("op_11786_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11787_cast = add(x = var_11785_cast, y = var_11786_to_fp16)[name = tensor("op_11787_cast")]; + tensor denom_361_epsilon_0_to_fp16 = const()[name = tensor("denom_361_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_361_cast = rsqrt(epsilon = denom_361_epsilon_0_to_fp16, x = var_11787_cast)[name = tensor("denom_361_cast")]; + tensor out_361_cast = mul(x = zero_mean_361_cast, y = denom_361_cast)[name = tensor("out_361_cast")]; + tensor var_11791_to_fp16 = const()[name = tensor("op_11791_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4589168768)))]; + tensor var_11792_cast = add(x = out_361_cast, y = var_11791_to_fp16)[name = tensor("op_11792_cast")]; + tensor var_11794_to_fp16 = const()[name = tensor("op_11794_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4589171392)))]; + tensor hidden_states_471_cast = mul(x = var_11792_cast, y = var_11794_to_fp16)[name = tensor("hidden_states_471_cast")]; + tensor var_11801 = const()[name = tensor("op_11801"), val = tensor([1, 1])]; + tensor var_11803 = const()[name = tensor("op_11803"), val = tensor([1, 1])]; + tensor q_241_pad_type_0 = const()[name = tensor("q_241_pad_type_0"), val = tensor("custom")]; + tensor q_241_pad_0 = const()[name = tensor("q_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4589174016)))]; + tensor q_241_cast = conv(dilations = var_11803, groups = var_6865, pad = q_241_pad_0, pad_type = q_241_pad_type_0, strides = var_11801, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_471_cast)[name = tensor("q_241_cast")]; + tensor var_11807 = const()[name = tensor("op_11807"), val = tensor([1, 1])]; + tensor var_11809 = const()[name = tensor("op_11809"), val = tensor([1, 1])]; + tensor k_241_pad_type_0 = const()[name = tensor("k_241_pad_type_0"), val = tensor("custom")]; + tensor k_241_pad_0 = const()[name = tensor("k_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4592450880)))]; + tensor k_241_cast = conv(dilations = var_11809, groups = var_6865, pad = k_241_pad_0, pad_type = k_241_pad_type_0, strides = var_11807, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_471_cast)[name = tensor("k_241_cast")]; + tensor var_11813 = const()[name = tensor("op_11813"), val = tensor([1, 1])]; + tensor var_11815 = const()[name = tensor("op_11815"), val = tensor([1, 1])]; + tensor v_241_pad_type_0 = const()[name = tensor("v_241_pad_type_0"), val = tensor("custom")]; + tensor v_241_pad_0 = const()[name = tensor("v_241_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4595727744)))]; + tensor v_241_cast = conv(dilations = var_11815, groups = var_6865, pad = v_241_pad_0, pad_type = v_241_pad_type_0, strides = var_11813, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_471_cast)[name = tensor("v_241_cast")]; + tensor var_11819 = const()[name = tensor("op_11819"), val = tensor([2, 20, 64, -1])]; + tensor var_11820_cast = reshape(shape = var_11819, x = q_241_cast)[name = tensor("op_11820_cast")]; + tensor var_11821 = const()[name = tensor("op_11821"), val = tensor([2, 20, 64, -1])]; + tensor var_11822_cast = reshape(shape = var_11821, x = k_241_cast)[name = tensor("op_11822_cast")]; + tensor var_11823 = const()[name = tensor("op_11823"), val = tensor([2, 20, 64, -1])]; + tensor var_11824_cast = reshape(shape = var_11823, x = v_241_cast)[name = tensor("op_11824_cast")]; + tensor attn_weights_481_transpose_x_0 = const()[name = tensor("attn_weights_481_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_481_transpose_y_0 = const()[name = tensor("attn_weights_481_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_481_cast = matmul(transpose_x = attn_weights_481_transpose_x_0, transpose_y = attn_weights_481_transpose_y_0, x = var_11820_cast, y = var_11822_cast)[name = tensor("attn_weights_481_cast")]; + tensor attn_weights_483_cast = mul(x = attn_weights_481_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_483_cast")]; + tensor var_11828_cast = softmax(axis = var_6849, x = attn_weights_483_cast)[name = tensor("op_11828_cast")]; + tensor attn_241_transpose_x_0 = const()[name = tensor("attn_241_transpose_x_0"), val = tensor(false)]; + tensor attn_241_transpose_y_0 = const()[name = tensor("attn_241_transpose_y_0"), val = tensor(true)]; + tensor attn_241_cast = matmul(transpose_x = attn_241_transpose_x_0, transpose_y = attn_241_transpose_y_0, x = var_11824_cast, y = var_11828_cast)[name = tensor("attn_241_cast")]; + tensor var_11832 = const()[name = tensor("op_11832"), val = tensor([2, 1280, 1, -1])]; + tensor input_683_cast = reshape(shape = var_11832, x = attn_241_cast)[name = tensor("input_683_cast")]; + tensor var_11837 = const()[name = tensor("op_11837"), val = tensor([1, 1])]; + tensor var_11839 = const()[name = tensor("op_11839"), val = tensor([1, 1])]; + tensor var_11841_pad_type_0 = const()[name = tensor("op_11841_pad_type_0"), val = tensor("custom")]; + tensor var_11841_pad_0 = const()[name = tensor("op_11841_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4599004608)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4602281472)))]; + tensor var_11841_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_11839, groups = var_6865, pad = var_11841_pad_0, pad_type = var_11841_pad_type_0, strides = var_11837, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_683_cast)[name = tensor("op_11841_cast")]; + tensor inputs_363_cast = add(x = var_11841_cast, y = inputs_361_cast)[name = tensor("inputs_363_cast")]; + tensor var_11845 = const()[name = tensor("op_11845"), val = tensor([1])]; + tensor channels_mean_363_cast = reduce_mean(axes = var_11845, keep_dims = var_6860, x = inputs_363_cast)[name = tensor("channels_mean_363_cast")]; + tensor zero_mean_363_cast = sub(x = inputs_363_cast, y = channels_mean_363_cast)[name = tensor("zero_mean_363_cast")]; + tensor zero_mean_sq_363_cast = mul(x = zero_mean_363_cast, y = zero_mean_363_cast)[name = tensor("zero_mean_sq_363_cast")]; + tensor var_11849 = const()[name = tensor("op_11849"), val = tensor([1])]; + tensor var_11850_cast = reduce_mean(axes = var_11849, keep_dims = var_6860, x = zero_mean_sq_363_cast)[name = tensor("op_11850_cast")]; + tensor var_11851_to_fp16 = const()[name = tensor("op_11851_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11852_cast = add(x = var_11850_cast, y = var_11851_to_fp16)[name = tensor("op_11852_cast")]; + tensor denom_363_epsilon_0_to_fp16 = const()[name = tensor("denom_363_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_363_cast = rsqrt(epsilon = denom_363_epsilon_0_to_fp16, x = var_11852_cast)[name = tensor("denom_363_cast")]; + tensor out_363_cast = mul(x = zero_mean_363_cast, y = denom_363_cast)[name = tensor("out_363_cast")]; + tensor var_11856_to_fp16 = const()[name = tensor("op_11856_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4602284096)))]; + tensor var_11857_cast = add(x = out_363_cast, y = var_11856_to_fp16)[name = tensor("op_11857_cast")]; + tensor var_11859_to_fp16 = const()[name = tensor("op_11859_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4602286720)))]; + tensor hidden_states_473_cast = mul(x = var_11857_cast, y = var_11859_to_fp16)[name = tensor("hidden_states_473_cast")]; + tensor var_11866 = const()[name = tensor("op_11866"), val = tensor([1, 1])]; + tensor var_11868 = const()[name = tensor("op_11868"), val = tensor([1, 1])]; + tensor q_243_pad_type_0 = const()[name = tensor("q_243_pad_type_0"), val = tensor("custom")]; + tensor q_243_pad_0 = const()[name = tensor("q_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4602289344)))]; + tensor q_243_cast = conv(dilations = var_11868, groups = var_6865, pad = q_243_pad_0, pad_type = q_243_pad_type_0, strides = var_11866, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_473_cast)[name = tensor("q_243_cast")]; + tensor var_11872 = const()[name = tensor("op_11872"), val = tensor([1, 1])]; + tensor var_11874 = const()[name = tensor("op_11874"), val = tensor([1, 1])]; + tensor k_243_pad_type_0 = const()[name = tensor("k_243_pad_type_0"), val = tensor("custom")]; + tensor k_243_pad_0 = const()[name = tensor("k_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4605566208)))]; + tensor k_243_cast = conv(dilations = var_11874, groups = var_6865, pad = k_243_pad_0, pad_type = k_243_pad_type_0, strides = var_11872, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_243_cast")]; + tensor var_11878 = const()[name = tensor("op_11878"), val = tensor([1, 1])]; + tensor var_11880 = const()[name = tensor("op_11880"), val = tensor([1, 1])]; + tensor v_243_pad_type_0 = const()[name = tensor("v_243_pad_type_0"), val = tensor("custom")]; + tensor v_243_pad_0 = const()[name = tensor("v_243_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4610809152)))]; + tensor v_243_cast = conv(dilations = var_11880, groups = var_6865, pad = v_243_pad_0, pad_type = v_243_pad_type_0, strides = var_11878, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_243_cast")]; + tensor var_11884 = const()[name = tensor("op_11884"), val = tensor([2, 20, 64, -1])]; + tensor var_11885_cast = reshape(shape = var_11884, x = q_243_cast)[name = tensor("op_11885_cast")]; + tensor var_11886 = const()[name = tensor("op_11886"), val = tensor([2, 20, 64, -1])]; + tensor var_11887_cast = reshape(shape = var_11886, x = k_243_cast)[name = tensor("op_11887_cast")]; + tensor var_11888 = const()[name = tensor("op_11888"), val = tensor([2, 20, 64, -1])]; + tensor var_11889_cast = reshape(shape = var_11888, x = v_243_cast)[name = tensor("op_11889_cast")]; + tensor attn_weights_485_transpose_x_0 = const()[name = tensor("attn_weights_485_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_485_transpose_y_0 = const()[name = tensor("attn_weights_485_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_485_cast = matmul(transpose_x = attn_weights_485_transpose_x_0, transpose_y = attn_weights_485_transpose_y_0, x = var_11885_cast, y = var_11887_cast)[name = tensor("attn_weights_485_cast")]; + tensor attn_weights_487_cast = mul(x = attn_weights_485_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_487_cast")]; + tensor var_11893_cast = softmax(axis = var_6849, x = attn_weights_487_cast)[name = tensor("op_11893_cast")]; + tensor attn_243_transpose_x_0 = const()[name = tensor("attn_243_transpose_x_0"), val = tensor(false)]; + tensor attn_243_transpose_y_0 = const()[name = tensor("attn_243_transpose_y_0"), val = tensor(true)]; + tensor attn_243_cast = matmul(transpose_x = attn_243_transpose_x_0, transpose_y = attn_243_transpose_y_0, x = var_11889_cast, y = var_11893_cast)[name = tensor("attn_243_cast")]; + tensor var_11897 = const()[name = tensor("op_11897"), val = tensor([2, 1280, 1, -1])]; + tensor input_685_cast = reshape(shape = var_11897, x = attn_243_cast)[name = tensor("input_685_cast")]; + tensor var_11902 = const()[name = tensor("op_11902"), val = tensor([1, 1])]; + tensor var_11904 = const()[name = tensor("op_11904"), val = tensor([1, 1])]; + tensor var_11906_pad_type_0 = const()[name = tensor("op_11906_pad_type_0"), val = tensor("custom")]; + tensor var_11906_pad_0 = const()[name = tensor("op_11906_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4616052096)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4619328960)))]; + tensor var_11906_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_11904, groups = var_6865, pad = var_11906_pad_0, pad_type = var_11906_pad_type_0, strides = var_11902, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_685_cast)[name = tensor("op_11906_cast")]; + tensor inputs_365_cast = add(x = var_11906_cast, y = inputs_363_cast)[name = tensor("inputs_365_cast")]; + tensor var_11910 = const()[name = tensor("op_11910"), val = tensor([1])]; + tensor channels_mean_365_cast = reduce_mean(axes = var_11910, keep_dims = var_6860, x = inputs_365_cast)[name = tensor("channels_mean_365_cast")]; + tensor zero_mean_365_cast = sub(x = inputs_365_cast, y = channels_mean_365_cast)[name = tensor("zero_mean_365_cast")]; + tensor zero_mean_sq_365_cast = mul(x = zero_mean_365_cast, y = zero_mean_365_cast)[name = tensor("zero_mean_sq_365_cast")]; + tensor var_11914 = const()[name = tensor("op_11914"), val = tensor([1])]; + tensor var_11915_cast = reduce_mean(axes = var_11914, keep_dims = var_6860, x = zero_mean_sq_365_cast)[name = tensor("op_11915_cast")]; + tensor var_11916_to_fp16 = const()[name = tensor("op_11916_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11917_cast = add(x = var_11915_cast, y = var_11916_to_fp16)[name = tensor("op_11917_cast")]; + tensor denom_365_epsilon_0_to_fp16 = const()[name = tensor("denom_365_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_365_cast = rsqrt(epsilon = denom_365_epsilon_0_to_fp16, x = var_11917_cast)[name = tensor("denom_365_cast")]; + tensor out_365_cast = mul(x = zero_mean_365_cast, y = denom_365_cast)[name = tensor("out_365_cast")]; + tensor var_11921_to_fp16 = const()[name = tensor("op_11921_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4619331584)))]; + tensor var_11922_cast = add(x = out_365_cast, y = var_11921_to_fp16)[name = tensor("op_11922_cast")]; + tensor var_11924_to_fp16 = const()[name = tensor("op_11924_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4619334208)))]; + tensor input_687_cast = mul(x = var_11922_cast, y = var_11924_to_fp16)[name = tensor("input_687_cast")]; + tensor var_11932 = const()[name = tensor("op_11932"), val = tensor([1, 1])]; + tensor var_11934 = const()[name = tensor("op_11934"), val = tensor([1, 1])]; + tensor var_11936_pad_type_0 = const()[name = tensor("op_11936_pad_type_0"), val = tensor("custom")]; + tensor var_11936_pad_0 = const()[name = tensor("op_11936_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4619336832)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4645551296)))]; + tensor var_11936_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_11934, groups = var_6865, pad = var_11936_pad_0, pad_type = var_11936_pad_type_0, strides = var_11932, weight = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_687_cast)[name = tensor("op_11936_cast")]; + tensor var_11937_split_sizes_0 = const()[name = tensor("op_11937_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11937_axis_0 = const()[name = tensor("op_11937_axis_0"), val = tensor(1)]; + tensor var_11937_cast_0, tensor var_11937_cast_1 = split(axis = var_11937_axis_0, split_sizes = var_11937_split_sizes_0, x = var_11936_cast)[name = tensor("op_11937_cast")]; + tensor var_11939_mode_0 = const()[name = tensor("op_11939_mode_0"), val = tensor("EXACT")]; + tensor var_11939_cast = gelu(mode = var_11939_mode_0, x = var_11937_cast_1)[name = tensor("op_11939_cast")]; + tensor input_689_cast = mul(x = var_11937_cast_0, y = var_11939_cast)[name = tensor("input_689_cast")]; + tensor var_11943 = const()[name = tensor("op_11943"), val = tensor([1, 1])]; + tensor var_11945 = const()[name = tensor("op_11945"), val = tensor([1, 1])]; + tensor var_11947_pad_type_0 = const()[name = tensor("op_11947_pad_type_0"), val = tensor("custom")]; + tensor var_11947_pad_0 = const()[name = tensor("op_11947_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4645571840)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4658679104)))]; + tensor var_11947_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_11945, groups = var_6865, pad = var_11947_pad_0, pad_type = var_11947_pad_type_0, strides = var_11943, weight = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_689_cast)[name = tensor("op_11947_cast")]; + tensor inputs_367_cast = add(x = var_11947_cast, y = inputs_365_cast)[name = tensor("inputs_367_cast")]; + tensor var_11957 = const()[name = tensor("op_11957"), val = tensor([1])]; + tensor channels_mean_367_cast = reduce_mean(axes = var_11957, keep_dims = var_6860, x = inputs_367_cast)[name = tensor("channels_mean_367_cast")]; + tensor zero_mean_367_cast = sub(x = inputs_367_cast, y = channels_mean_367_cast)[name = tensor("zero_mean_367_cast")]; + tensor zero_mean_sq_367_cast = mul(x = zero_mean_367_cast, y = zero_mean_367_cast)[name = tensor("zero_mean_sq_367_cast")]; + tensor var_11961 = const()[name = tensor("op_11961"), val = tensor([1])]; + tensor var_11962_cast = reduce_mean(axes = var_11961, keep_dims = var_6860, x = zero_mean_sq_367_cast)[name = tensor("op_11962_cast")]; + tensor var_11963_to_fp16 = const()[name = tensor("op_11963_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11964_cast = add(x = var_11962_cast, y = var_11963_to_fp16)[name = tensor("op_11964_cast")]; + tensor denom_367_epsilon_0_to_fp16 = const()[name = tensor("denom_367_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_367_cast = rsqrt(epsilon = denom_367_epsilon_0_to_fp16, x = var_11964_cast)[name = tensor("denom_367_cast")]; + tensor out_367_cast = mul(x = zero_mean_367_cast, y = denom_367_cast)[name = tensor("out_367_cast")]; + tensor var_11968_to_fp16 = const()[name = tensor("op_11968_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4658681728)))]; + tensor var_11969_cast = add(x = out_367_cast, y = var_11968_to_fp16)[name = tensor("op_11969_cast")]; + tensor var_11971_to_fp16 = const()[name = tensor("op_11971_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4658684352)))]; + tensor hidden_states_477_cast = mul(x = var_11969_cast, y = var_11971_to_fp16)[name = tensor("hidden_states_477_cast")]; + tensor var_11978 = const()[name = tensor("op_11978"), val = tensor([1, 1])]; + tensor var_11980 = const()[name = tensor("op_11980"), val = tensor([1, 1])]; + tensor q_245_pad_type_0 = const()[name = tensor("q_245_pad_type_0"), val = tensor("custom")]; + tensor q_245_pad_0 = const()[name = tensor("q_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4658686976)))]; + tensor q_245_cast = conv(dilations = var_11980, groups = var_6865, pad = q_245_pad_0, pad_type = q_245_pad_type_0, strides = var_11978, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_477_cast)[name = tensor("q_245_cast")]; + tensor var_11984 = const()[name = tensor("op_11984"), val = tensor([1, 1])]; + tensor var_11986 = const()[name = tensor("op_11986"), val = tensor([1, 1])]; + tensor k_245_pad_type_0 = const()[name = tensor("k_245_pad_type_0"), val = tensor("custom")]; + tensor k_245_pad_0 = const()[name = tensor("k_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4661963840)))]; + tensor k_245_cast = conv(dilations = var_11986, groups = var_6865, pad = k_245_pad_0, pad_type = k_245_pad_type_0, strides = var_11984, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_477_cast)[name = tensor("k_245_cast")]; + tensor var_11990 = const()[name = tensor("op_11990"), val = tensor([1, 1])]; + tensor var_11992 = const()[name = tensor("op_11992"), val = tensor([1, 1])]; + tensor v_245_pad_type_0 = const()[name = tensor("v_245_pad_type_0"), val = tensor("custom")]; + tensor v_245_pad_0 = const()[name = tensor("v_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4665240704)))]; + tensor v_245_cast = conv(dilations = var_11992, groups = var_6865, pad = v_245_pad_0, pad_type = v_245_pad_type_0, strides = var_11990, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_477_cast)[name = tensor("v_245_cast")]; + tensor var_11996 = const()[name = tensor("op_11996"), val = tensor([2, 20, 64, -1])]; + tensor var_11997_cast = reshape(shape = var_11996, x = q_245_cast)[name = tensor("op_11997_cast")]; + tensor var_11998 = const()[name = tensor("op_11998"), val = tensor([2, 20, 64, -1])]; + tensor var_11999_cast = reshape(shape = var_11998, x = k_245_cast)[name = tensor("op_11999_cast")]; + tensor var_12000 = const()[name = tensor("op_12000"), val = tensor([2, 20, 64, -1])]; + tensor var_12001_cast = reshape(shape = var_12000, x = v_245_cast)[name = tensor("op_12001_cast")]; + tensor attn_weights_489_transpose_x_0 = const()[name = tensor("attn_weights_489_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_489_transpose_y_0 = const()[name = tensor("attn_weights_489_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_489_cast = matmul(transpose_x = attn_weights_489_transpose_x_0, transpose_y = attn_weights_489_transpose_y_0, x = var_11997_cast, y = var_11999_cast)[name = tensor("attn_weights_489_cast")]; + tensor attn_weights_491_cast = mul(x = attn_weights_489_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_491_cast")]; + tensor var_12005_cast = softmax(axis = var_6849, x = attn_weights_491_cast)[name = tensor("op_12005_cast")]; + tensor attn_245_transpose_x_0 = const()[name = tensor("attn_245_transpose_x_0"), val = tensor(false)]; + tensor attn_245_transpose_y_0 = const()[name = tensor("attn_245_transpose_y_0"), val = tensor(true)]; + tensor attn_245_cast = matmul(transpose_x = attn_245_transpose_x_0, transpose_y = attn_245_transpose_y_0, x = var_12001_cast, y = var_12005_cast)[name = tensor("attn_245_cast")]; + tensor var_12009 = const()[name = tensor("op_12009"), val = tensor([2, 1280, 1, -1])]; + tensor input_691_cast = reshape(shape = var_12009, x = attn_245_cast)[name = tensor("input_691_cast")]; + tensor var_12014 = const()[name = tensor("op_12014"), val = tensor([1, 1])]; + tensor var_12016 = const()[name = tensor("op_12016"), val = tensor([1, 1])]; + tensor var_12018_pad_type_0 = const()[name = tensor("op_12018_pad_type_0"), val = tensor("custom")]; + tensor var_12018_pad_0 = const()[name = tensor("op_12018_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4668517568)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4671794432)))]; + tensor var_12018_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_12016, groups = var_6865, pad = var_12018_pad_0, pad_type = var_12018_pad_type_0, strides = var_12014, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_691_cast)[name = tensor("op_12018_cast")]; + tensor inputs_369_cast = add(x = var_12018_cast, y = inputs_367_cast)[name = tensor("inputs_369_cast")]; + tensor var_12022 = const()[name = tensor("op_12022"), val = tensor([1])]; + tensor channels_mean_369_cast = reduce_mean(axes = var_12022, keep_dims = var_6860, x = inputs_369_cast)[name = tensor("channels_mean_369_cast")]; + tensor zero_mean_369_cast = sub(x = inputs_369_cast, y = channels_mean_369_cast)[name = tensor("zero_mean_369_cast")]; + tensor zero_mean_sq_369_cast = mul(x = zero_mean_369_cast, y = zero_mean_369_cast)[name = tensor("zero_mean_sq_369_cast")]; + tensor var_12026 = const()[name = tensor("op_12026"), val = tensor([1])]; + tensor var_12027_cast = reduce_mean(axes = var_12026, keep_dims = var_6860, x = zero_mean_sq_369_cast)[name = tensor("op_12027_cast")]; + tensor var_12028_to_fp16 = const()[name = tensor("op_12028_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12029_cast = add(x = var_12027_cast, y = var_12028_to_fp16)[name = tensor("op_12029_cast")]; + tensor denom_369_epsilon_0_to_fp16 = const()[name = tensor("denom_369_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_369_cast = rsqrt(epsilon = denom_369_epsilon_0_to_fp16, x = var_12029_cast)[name = tensor("denom_369_cast")]; + tensor out_369_cast = mul(x = zero_mean_369_cast, y = denom_369_cast)[name = tensor("out_369_cast")]; + tensor var_12033_to_fp16 = const()[name = tensor("op_12033_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4671797056)))]; + tensor var_12034_cast = add(x = out_369_cast, y = var_12033_to_fp16)[name = tensor("op_12034_cast")]; + tensor var_12036_to_fp16 = const()[name = tensor("op_12036_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4671799680)))]; + tensor hidden_states_479_cast = mul(x = var_12034_cast, y = var_12036_to_fp16)[name = tensor("hidden_states_479_cast")]; + tensor var_12043 = const()[name = tensor("op_12043"), val = tensor([1, 1])]; + tensor var_12045 = const()[name = tensor("op_12045"), val = tensor([1, 1])]; + tensor q_247_pad_type_0 = const()[name = tensor("q_247_pad_type_0"), val = tensor("custom")]; + tensor q_247_pad_0 = const()[name = tensor("q_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4671802304)))]; + tensor q_247_cast = conv(dilations = var_12045, groups = var_6865, pad = q_247_pad_0, pad_type = q_247_pad_type_0, strides = var_12043, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_479_cast)[name = tensor("q_247_cast")]; + tensor var_12049 = const()[name = tensor("op_12049"), val = tensor([1, 1])]; + tensor var_12051 = const()[name = tensor("op_12051"), val = tensor([1, 1])]; + tensor k_247_pad_type_0 = const()[name = tensor("k_247_pad_type_0"), val = tensor("custom")]; + tensor k_247_pad_0 = const()[name = tensor("k_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4675079168)))]; + tensor k_247_cast = conv(dilations = var_12051, groups = var_6865, pad = k_247_pad_0, pad_type = k_247_pad_type_0, strides = var_12049, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_247_cast")]; + tensor var_12055 = const()[name = tensor("op_12055"), val = tensor([1, 1])]; + tensor var_12057 = const()[name = tensor("op_12057"), val = tensor([1, 1])]; + tensor v_247_pad_type_0 = const()[name = tensor("v_247_pad_type_0"), val = tensor("custom")]; + tensor v_247_pad_0 = const()[name = tensor("v_247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4680322112)))]; + tensor v_247_cast = conv(dilations = var_12057, groups = var_6865, pad = v_247_pad_0, pad_type = v_247_pad_type_0, strides = var_12055, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_247_cast")]; + tensor var_12061 = const()[name = tensor("op_12061"), val = tensor([2, 20, 64, -1])]; + tensor var_12062_cast = reshape(shape = var_12061, x = q_247_cast)[name = tensor("op_12062_cast")]; + tensor var_12063 = const()[name = tensor("op_12063"), val = tensor([2, 20, 64, -1])]; + tensor var_12064_cast = reshape(shape = var_12063, x = k_247_cast)[name = tensor("op_12064_cast")]; + tensor var_12065 = const()[name = tensor("op_12065"), val = tensor([2, 20, 64, -1])]; + tensor var_12066_cast = reshape(shape = var_12065, x = v_247_cast)[name = tensor("op_12066_cast")]; + tensor attn_weights_493_transpose_x_0 = const()[name = tensor("attn_weights_493_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_493_transpose_y_0 = const()[name = tensor("attn_weights_493_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_493_cast = matmul(transpose_x = attn_weights_493_transpose_x_0, transpose_y = attn_weights_493_transpose_y_0, x = var_12062_cast, y = var_12064_cast)[name = tensor("attn_weights_493_cast")]; + tensor attn_weights_495_cast = mul(x = attn_weights_493_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_495_cast")]; + tensor var_12070_cast = softmax(axis = var_6849, x = attn_weights_495_cast)[name = tensor("op_12070_cast")]; + tensor attn_247_transpose_x_0 = const()[name = tensor("attn_247_transpose_x_0"), val = tensor(false)]; + tensor attn_247_transpose_y_0 = const()[name = tensor("attn_247_transpose_y_0"), val = tensor(true)]; + tensor attn_247_cast = matmul(transpose_x = attn_247_transpose_x_0, transpose_y = attn_247_transpose_y_0, x = var_12066_cast, y = var_12070_cast)[name = tensor("attn_247_cast")]; + tensor var_12074 = const()[name = tensor("op_12074"), val = tensor([2, 1280, 1, -1])]; + tensor input_693_cast = reshape(shape = var_12074, x = attn_247_cast)[name = tensor("input_693_cast")]; + tensor var_12079 = const()[name = tensor("op_12079"), val = tensor([1, 1])]; + tensor var_12081 = const()[name = tensor("op_12081"), val = tensor([1, 1])]; + tensor var_12083_pad_type_0 = const()[name = tensor("op_12083_pad_type_0"), val = tensor("custom")]; + tensor var_12083_pad_0 = const()[name = tensor("op_12083_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4685565056)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4688841920)))]; + tensor var_12083_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_12081, groups = var_6865, pad = var_12083_pad_0, pad_type = var_12083_pad_type_0, strides = var_12079, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_693_cast)[name = tensor("op_12083_cast")]; + tensor inputs_371_cast = add(x = var_12083_cast, y = inputs_369_cast)[name = tensor("inputs_371_cast")]; + tensor var_12087 = const()[name = tensor("op_12087"), val = tensor([1])]; + tensor channels_mean_371_cast = reduce_mean(axes = var_12087, keep_dims = var_6860, x = inputs_371_cast)[name = tensor("channels_mean_371_cast")]; + tensor zero_mean_371_cast = sub(x = inputs_371_cast, y = channels_mean_371_cast)[name = tensor("zero_mean_371_cast")]; + tensor zero_mean_sq_371_cast = mul(x = zero_mean_371_cast, y = zero_mean_371_cast)[name = tensor("zero_mean_sq_371_cast")]; + tensor var_12091 = const()[name = tensor("op_12091"), val = tensor([1])]; + tensor var_12092_cast = reduce_mean(axes = var_12091, keep_dims = var_6860, x = zero_mean_sq_371_cast)[name = tensor("op_12092_cast")]; + tensor var_12093_to_fp16 = const()[name = tensor("op_12093_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12094_cast = add(x = var_12092_cast, y = var_12093_to_fp16)[name = tensor("op_12094_cast")]; + tensor denom_371_epsilon_0_to_fp16 = const()[name = tensor("denom_371_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_371_cast = rsqrt(epsilon = denom_371_epsilon_0_to_fp16, x = var_12094_cast)[name = tensor("denom_371_cast")]; + tensor out_371_cast = mul(x = zero_mean_371_cast, y = denom_371_cast)[name = tensor("out_371_cast")]; + tensor var_12098_to_fp16 = const()[name = tensor("op_12098_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4688844544)))]; + tensor var_12099_cast = add(x = out_371_cast, y = var_12098_to_fp16)[name = tensor("op_12099_cast")]; + tensor var_12101_to_fp16 = const()[name = tensor("op_12101_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4688847168)))]; + tensor input_695_cast = mul(x = var_12099_cast, y = var_12101_to_fp16)[name = tensor("input_695_cast")]; + tensor var_12109 = const()[name = tensor("op_12109"), val = tensor([1, 1])]; + tensor var_12111 = const()[name = tensor("op_12111"), val = tensor([1, 1])]; + tensor var_12113_pad_type_0 = const()[name = tensor("op_12113_pad_type_0"), val = tensor("custom")]; + tensor var_12113_pad_0 = const()[name = tensor("op_12113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4688849792)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4715064256)))]; + tensor var_12113_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_12111, groups = var_6865, pad = var_12113_pad_0, pad_type = var_12113_pad_type_0, strides = var_12109, weight = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_695_cast)[name = tensor("op_12113_cast")]; + tensor var_12114_split_sizes_0 = const()[name = tensor("op_12114_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12114_axis_0 = const()[name = tensor("op_12114_axis_0"), val = tensor(1)]; + tensor var_12114_cast_0, tensor var_12114_cast_1 = split(axis = var_12114_axis_0, split_sizes = var_12114_split_sizes_0, x = var_12113_cast)[name = tensor("op_12114_cast")]; + tensor var_12116_mode_0 = const()[name = tensor("op_12116_mode_0"), val = tensor("EXACT")]; + tensor var_12116_cast = gelu(mode = var_12116_mode_0, x = var_12114_cast_1)[name = tensor("op_12116_cast")]; + tensor input_697_cast = mul(x = var_12114_cast_0, y = var_12116_cast)[name = tensor("input_697_cast")]; + tensor var_12120 = const()[name = tensor("op_12120"), val = tensor([1, 1])]; + tensor var_12122 = const()[name = tensor("op_12122"), val = tensor([1, 1])]; + tensor var_12124_pad_type_0 = const()[name = tensor("op_12124_pad_type_0"), val = tensor("custom")]; + tensor var_12124_pad_0 = const()[name = tensor("op_12124_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4715084800)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4728192064)))]; + tensor var_12124_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_12122, groups = var_6865, pad = var_12124_pad_0, pad_type = var_12124_pad_type_0, strides = var_12120, weight = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_697_cast)[name = tensor("op_12124_cast")]; + tensor inputs_373_cast = add(x = var_12124_cast, y = inputs_371_cast)[name = tensor("inputs_373_cast")]; + tensor var_12134 = const()[name = tensor("op_12134"), val = tensor([1])]; + tensor channels_mean_373_cast = reduce_mean(axes = var_12134, keep_dims = var_6860, x = inputs_373_cast)[name = tensor("channels_mean_373_cast")]; + tensor zero_mean_373_cast = sub(x = inputs_373_cast, y = channels_mean_373_cast)[name = tensor("zero_mean_373_cast")]; + tensor zero_mean_sq_373_cast = mul(x = zero_mean_373_cast, y = zero_mean_373_cast)[name = tensor("zero_mean_sq_373_cast")]; + tensor var_12138 = const()[name = tensor("op_12138"), val = tensor([1])]; + tensor var_12139_cast = reduce_mean(axes = var_12138, keep_dims = var_6860, x = zero_mean_sq_373_cast)[name = tensor("op_12139_cast")]; + tensor var_12140_to_fp16 = const()[name = tensor("op_12140_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12141_cast = add(x = var_12139_cast, y = var_12140_to_fp16)[name = tensor("op_12141_cast")]; + tensor denom_373_epsilon_0_to_fp16 = const()[name = tensor("denom_373_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_373_cast = rsqrt(epsilon = denom_373_epsilon_0_to_fp16, x = var_12141_cast)[name = tensor("denom_373_cast")]; + tensor out_373_cast = mul(x = zero_mean_373_cast, y = denom_373_cast)[name = tensor("out_373_cast")]; + tensor var_12145_to_fp16 = const()[name = tensor("op_12145_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4728194688)))]; + tensor var_12146_cast = add(x = out_373_cast, y = var_12145_to_fp16)[name = tensor("op_12146_cast")]; + tensor var_12148_to_fp16 = const()[name = tensor("op_12148_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4728197312)))]; + tensor hidden_states_483_cast = mul(x = var_12146_cast, y = var_12148_to_fp16)[name = tensor("hidden_states_483_cast")]; + tensor var_12155 = const()[name = tensor("op_12155"), val = tensor([1, 1])]; + tensor var_12157 = const()[name = tensor("op_12157"), val = tensor([1, 1])]; + tensor q_249_pad_type_0 = const()[name = tensor("q_249_pad_type_0"), val = tensor("custom")]; + tensor q_249_pad_0 = const()[name = tensor("q_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4728199936)))]; + tensor q_249_cast = conv(dilations = var_12157, groups = var_6865, pad = q_249_pad_0, pad_type = q_249_pad_type_0, strides = var_12155, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_483_cast)[name = tensor("q_249_cast")]; + tensor var_12161 = const()[name = tensor("op_12161"), val = tensor([1, 1])]; + tensor var_12163 = const()[name = tensor("op_12163"), val = tensor([1, 1])]; + tensor k_249_pad_type_0 = const()[name = tensor("k_249_pad_type_0"), val = tensor("custom")]; + tensor k_249_pad_0 = const()[name = tensor("k_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4731476800)))]; + tensor k_249_cast = conv(dilations = var_12163, groups = var_6865, pad = k_249_pad_0, pad_type = k_249_pad_type_0, strides = var_12161, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_483_cast)[name = tensor("k_249_cast")]; + tensor var_12167 = const()[name = tensor("op_12167"), val = tensor([1, 1])]; + tensor var_12169 = const()[name = tensor("op_12169"), val = tensor([1, 1])]; + tensor v_249_pad_type_0 = const()[name = tensor("v_249_pad_type_0"), val = tensor("custom")]; + tensor v_249_pad_0 = const()[name = tensor("v_249_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4734753664)))]; + tensor v_249_cast = conv(dilations = var_12169, groups = var_6865, pad = v_249_pad_0, pad_type = v_249_pad_type_0, strides = var_12167, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_483_cast)[name = tensor("v_249_cast")]; + tensor var_12173 = const()[name = tensor("op_12173"), val = tensor([2, 20, 64, -1])]; + tensor var_12174_cast = reshape(shape = var_12173, x = q_249_cast)[name = tensor("op_12174_cast")]; + tensor var_12175 = const()[name = tensor("op_12175"), val = tensor([2, 20, 64, -1])]; + tensor var_12176_cast = reshape(shape = var_12175, x = k_249_cast)[name = tensor("op_12176_cast")]; + tensor var_12177 = const()[name = tensor("op_12177"), val = tensor([2, 20, 64, -1])]; + tensor var_12178_cast = reshape(shape = var_12177, x = v_249_cast)[name = tensor("op_12178_cast")]; + tensor attn_weights_497_transpose_x_0 = const()[name = tensor("attn_weights_497_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_497_transpose_y_0 = const()[name = tensor("attn_weights_497_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_497_cast = matmul(transpose_x = attn_weights_497_transpose_x_0, transpose_y = attn_weights_497_transpose_y_0, x = var_12174_cast, y = var_12176_cast)[name = tensor("attn_weights_497_cast")]; + tensor attn_weights_499_cast = mul(x = attn_weights_497_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_499_cast")]; + tensor var_12182_cast = softmax(axis = var_6849, x = attn_weights_499_cast)[name = tensor("op_12182_cast")]; + tensor attn_249_transpose_x_0 = const()[name = tensor("attn_249_transpose_x_0"), val = tensor(false)]; + tensor attn_249_transpose_y_0 = const()[name = tensor("attn_249_transpose_y_0"), val = tensor(true)]; + tensor attn_249_cast = matmul(transpose_x = attn_249_transpose_x_0, transpose_y = attn_249_transpose_y_0, x = var_12178_cast, y = var_12182_cast)[name = tensor("attn_249_cast")]; + tensor var_12186 = const()[name = tensor("op_12186"), val = tensor([2, 1280, 1, -1])]; + tensor input_699_cast = reshape(shape = var_12186, x = attn_249_cast)[name = tensor("input_699_cast")]; + tensor var_12191 = const()[name = tensor("op_12191"), val = tensor([1, 1])]; + tensor var_12193 = const()[name = tensor("op_12193"), val = tensor([1, 1])]; + tensor var_12195_pad_type_0 = const()[name = tensor("op_12195_pad_type_0"), val = tensor("custom")]; + tensor var_12195_pad_0 = const()[name = tensor("op_12195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4738030528)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4741307392)))]; + tensor var_12195_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_12193, groups = var_6865, pad = var_12195_pad_0, pad_type = var_12195_pad_type_0, strides = var_12191, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_699_cast)[name = tensor("op_12195_cast")]; + tensor inputs_375_cast = add(x = var_12195_cast, y = inputs_373_cast)[name = tensor("inputs_375_cast")]; + tensor var_12199 = const()[name = tensor("op_12199"), val = tensor([1])]; + tensor channels_mean_375_cast = reduce_mean(axes = var_12199, keep_dims = var_6860, x = inputs_375_cast)[name = tensor("channels_mean_375_cast")]; + tensor zero_mean_375_cast = sub(x = inputs_375_cast, y = channels_mean_375_cast)[name = tensor("zero_mean_375_cast")]; + tensor zero_mean_sq_375_cast = mul(x = zero_mean_375_cast, y = zero_mean_375_cast)[name = tensor("zero_mean_sq_375_cast")]; + tensor var_12203 = const()[name = tensor("op_12203"), val = tensor([1])]; + tensor var_12204_cast = reduce_mean(axes = var_12203, keep_dims = var_6860, x = zero_mean_sq_375_cast)[name = tensor("op_12204_cast")]; + tensor var_12205_to_fp16 = const()[name = tensor("op_12205_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12206_cast = add(x = var_12204_cast, y = var_12205_to_fp16)[name = tensor("op_12206_cast")]; + tensor denom_375_epsilon_0_to_fp16 = const()[name = tensor("denom_375_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_375_cast = rsqrt(epsilon = denom_375_epsilon_0_to_fp16, x = var_12206_cast)[name = tensor("denom_375_cast")]; + tensor out_375_cast = mul(x = zero_mean_375_cast, y = denom_375_cast)[name = tensor("out_375_cast")]; + tensor var_12210_to_fp16 = const()[name = tensor("op_12210_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4741310016)))]; + tensor var_12211_cast = add(x = out_375_cast, y = var_12210_to_fp16)[name = tensor("op_12211_cast")]; + tensor var_12213_to_fp16 = const()[name = tensor("op_12213_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4741312640)))]; + tensor hidden_states_485_cast = mul(x = var_12211_cast, y = var_12213_to_fp16)[name = tensor("hidden_states_485_cast")]; + tensor var_12220 = const()[name = tensor("op_12220"), val = tensor([1, 1])]; + tensor var_12222 = const()[name = tensor("op_12222"), val = tensor([1, 1])]; + tensor q_251_pad_type_0 = const()[name = tensor("q_251_pad_type_0"), val = tensor("custom")]; + tensor q_251_pad_0 = const()[name = tensor("q_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4741315264)))]; + tensor q_251_cast = conv(dilations = var_12222, groups = var_6865, pad = q_251_pad_0, pad_type = q_251_pad_type_0, strides = var_12220, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_485_cast)[name = tensor("q_251_cast")]; + tensor var_12226 = const()[name = tensor("op_12226"), val = tensor([1, 1])]; + tensor var_12228 = const()[name = tensor("op_12228"), val = tensor([1, 1])]; + tensor k_251_pad_type_0 = const()[name = tensor("k_251_pad_type_0"), val = tensor("custom")]; + tensor k_251_pad_0 = const()[name = tensor("k_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4744592128)))]; + tensor k_251_cast = conv(dilations = var_12228, groups = var_6865, pad = k_251_pad_0, pad_type = k_251_pad_type_0, strides = var_12226, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_251_cast")]; + tensor var_12232 = const()[name = tensor("op_12232"), val = tensor([1, 1])]; + tensor var_12234 = const()[name = tensor("op_12234"), val = tensor([1, 1])]; + tensor v_251_pad_type_0 = const()[name = tensor("v_251_pad_type_0"), val = tensor("custom")]; + tensor v_251_pad_0 = const()[name = tensor("v_251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4749835072)))]; + tensor v_251_cast = conv(dilations = var_12234, groups = var_6865, pad = v_251_pad_0, pad_type = v_251_pad_type_0, strides = var_12232, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_251_cast")]; + tensor var_12238 = const()[name = tensor("op_12238"), val = tensor([2, 20, 64, -1])]; + tensor var_12239_cast = reshape(shape = var_12238, x = q_251_cast)[name = tensor("op_12239_cast")]; + tensor var_12240 = const()[name = tensor("op_12240"), val = tensor([2, 20, 64, -1])]; + tensor var_12241_cast = reshape(shape = var_12240, x = k_251_cast)[name = tensor("op_12241_cast")]; + tensor var_12242 = const()[name = tensor("op_12242"), val = tensor([2, 20, 64, -1])]; + tensor var_12243_cast = reshape(shape = var_12242, x = v_251_cast)[name = tensor("op_12243_cast")]; + tensor attn_weights_501_transpose_x_0 = const()[name = tensor("attn_weights_501_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_501_transpose_y_0 = const()[name = tensor("attn_weights_501_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_501_cast = matmul(transpose_x = attn_weights_501_transpose_x_0, transpose_y = attn_weights_501_transpose_y_0, x = var_12239_cast, y = var_12241_cast)[name = tensor("attn_weights_501_cast")]; + tensor attn_weights_503_cast = mul(x = attn_weights_501_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_503_cast")]; + tensor var_12247_cast = softmax(axis = var_6849, x = attn_weights_503_cast)[name = tensor("op_12247_cast")]; + tensor attn_251_transpose_x_0 = const()[name = tensor("attn_251_transpose_x_0"), val = tensor(false)]; + tensor attn_251_transpose_y_0 = const()[name = tensor("attn_251_transpose_y_0"), val = tensor(true)]; + tensor attn_251_cast = matmul(transpose_x = attn_251_transpose_x_0, transpose_y = attn_251_transpose_y_0, x = var_12243_cast, y = var_12247_cast)[name = tensor("attn_251_cast")]; + tensor var_12251 = const()[name = tensor("op_12251"), val = tensor([2, 1280, 1, -1])]; + tensor input_701_cast = reshape(shape = var_12251, x = attn_251_cast)[name = tensor("input_701_cast")]; + tensor var_12256 = const()[name = tensor("op_12256"), val = tensor([1, 1])]; + tensor var_12258 = const()[name = tensor("op_12258"), val = tensor([1, 1])]; + tensor var_12260_pad_type_0 = const()[name = tensor("op_12260_pad_type_0"), val = tensor("custom")]; + tensor var_12260_pad_0 = const()[name = tensor("op_12260_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4755078016)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4758354880)))]; + tensor var_12260_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_12258, groups = var_6865, pad = var_12260_pad_0, pad_type = var_12260_pad_type_0, strides = var_12256, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_701_cast)[name = tensor("op_12260_cast")]; + tensor inputs_377_cast = add(x = var_12260_cast, y = inputs_375_cast)[name = tensor("inputs_377_cast")]; + tensor var_12264 = const()[name = tensor("op_12264"), val = tensor([1])]; + tensor channels_mean_377_cast = reduce_mean(axes = var_12264, keep_dims = var_6860, x = inputs_377_cast)[name = tensor("channels_mean_377_cast")]; + tensor zero_mean_377_cast = sub(x = inputs_377_cast, y = channels_mean_377_cast)[name = tensor("zero_mean_377_cast")]; + tensor zero_mean_sq_377_cast = mul(x = zero_mean_377_cast, y = zero_mean_377_cast)[name = tensor("zero_mean_sq_377_cast")]; + tensor var_12268 = const()[name = tensor("op_12268"), val = tensor([1])]; + tensor var_12269_cast = reduce_mean(axes = var_12268, keep_dims = var_6860, x = zero_mean_sq_377_cast)[name = tensor("op_12269_cast")]; + tensor var_12270_to_fp16 = const()[name = tensor("op_12270_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12271_cast = add(x = var_12269_cast, y = var_12270_to_fp16)[name = tensor("op_12271_cast")]; + tensor denom_377_epsilon_0_to_fp16 = const()[name = tensor("denom_377_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_377_cast = rsqrt(epsilon = denom_377_epsilon_0_to_fp16, x = var_12271_cast)[name = tensor("denom_377_cast")]; + tensor out_377_cast = mul(x = zero_mean_377_cast, y = denom_377_cast)[name = tensor("out_377_cast")]; + tensor var_12275_to_fp16 = const()[name = tensor("op_12275_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4758357504)))]; + tensor var_12276_cast = add(x = out_377_cast, y = var_12275_to_fp16)[name = tensor("op_12276_cast")]; + tensor var_12278_to_fp16 = const()[name = tensor("op_12278_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4758360128)))]; + tensor input_703_cast = mul(x = var_12276_cast, y = var_12278_to_fp16)[name = tensor("input_703_cast")]; + tensor var_12286 = const()[name = tensor("op_12286"), val = tensor([1, 1])]; + tensor var_12288 = const()[name = tensor("op_12288"), val = tensor([1, 1])]; + tensor var_12290_pad_type_0 = const()[name = tensor("op_12290_pad_type_0"), val = tensor("custom")]; + tensor var_12290_pad_0 = const()[name = tensor("op_12290_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4758362752)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4784577216)))]; + tensor var_12290_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_12288, groups = var_6865, pad = var_12290_pad_0, pad_type = var_12290_pad_type_0, strides = var_12286, weight = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_703_cast)[name = tensor("op_12290_cast")]; + tensor var_12291_split_sizes_0 = const()[name = tensor("op_12291_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12291_axis_0 = const()[name = tensor("op_12291_axis_0"), val = tensor(1)]; + tensor var_12291_cast_0, tensor var_12291_cast_1 = split(axis = var_12291_axis_0, split_sizes = var_12291_split_sizes_0, x = var_12290_cast)[name = tensor("op_12291_cast")]; + tensor var_12293_mode_0 = const()[name = tensor("op_12293_mode_0"), val = tensor("EXACT")]; + tensor var_12293_cast = gelu(mode = var_12293_mode_0, x = var_12291_cast_1)[name = tensor("op_12293_cast")]; + tensor input_705_cast = mul(x = var_12291_cast_0, y = var_12293_cast)[name = tensor("input_705_cast")]; + tensor var_12297 = const()[name = tensor("op_12297"), val = tensor([1, 1])]; + tensor var_12299 = const()[name = tensor("op_12299"), val = tensor([1, 1])]; + tensor var_12301_pad_type_0 = const()[name = tensor("op_12301_pad_type_0"), val = tensor("custom")]; + tensor var_12301_pad_0 = const()[name = tensor("op_12301_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4784597760)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4797705024)))]; + tensor var_12301_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_12299, groups = var_6865, pad = var_12301_pad_0, pad_type = var_12301_pad_type_0, strides = var_12297, weight = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_705_cast)[name = tensor("op_12301_cast")]; + tensor inputs_379_cast = add(x = var_12301_cast, y = inputs_377_cast)[name = tensor("inputs_379_cast")]; + tensor var_12311 = const()[name = tensor("op_12311"), val = tensor([1])]; + tensor channels_mean_379_cast = reduce_mean(axes = var_12311, keep_dims = var_6860, x = inputs_379_cast)[name = tensor("channels_mean_379_cast")]; + tensor zero_mean_379_cast = sub(x = inputs_379_cast, y = channels_mean_379_cast)[name = tensor("zero_mean_379_cast")]; + tensor zero_mean_sq_379_cast = mul(x = zero_mean_379_cast, y = zero_mean_379_cast)[name = tensor("zero_mean_sq_379_cast")]; + tensor var_12315 = const()[name = tensor("op_12315"), val = tensor([1])]; + tensor var_12316_cast = reduce_mean(axes = var_12315, keep_dims = var_6860, x = zero_mean_sq_379_cast)[name = tensor("op_12316_cast")]; + tensor var_12317_to_fp16 = const()[name = tensor("op_12317_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12318_cast = add(x = var_12316_cast, y = var_12317_to_fp16)[name = tensor("op_12318_cast")]; + tensor denom_379_epsilon_0_to_fp16 = const()[name = tensor("denom_379_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_379_cast = rsqrt(epsilon = denom_379_epsilon_0_to_fp16, x = var_12318_cast)[name = tensor("denom_379_cast")]; + tensor out_379_cast = mul(x = zero_mean_379_cast, y = denom_379_cast)[name = tensor("out_379_cast")]; + tensor var_12322_to_fp16 = const()[name = tensor("op_12322_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4797707648)))]; + tensor var_12323_cast = add(x = out_379_cast, y = var_12322_to_fp16)[name = tensor("op_12323_cast")]; + tensor var_12325_to_fp16 = const()[name = tensor("op_12325_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4797710272)))]; + tensor hidden_states_489_cast = mul(x = var_12323_cast, y = var_12325_to_fp16)[name = tensor("hidden_states_489_cast")]; + tensor var_12332 = const()[name = tensor("op_12332"), val = tensor([1, 1])]; + tensor var_12334 = const()[name = tensor("op_12334"), val = tensor([1, 1])]; + tensor q_253_pad_type_0 = const()[name = tensor("q_253_pad_type_0"), val = tensor("custom")]; + tensor q_253_pad_0 = const()[name = tensor("q_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4797712896)))]; + tensor q_253_cast = conv(dilations = var_12334, groups = var_6865, pad = q_253_pad_0, pad_type = q_253_pad_type_0, strides = var_12332, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_489_cast)[name = tensor("q_253_cast")]; + tensor var_12338 = const()[name = tensor("op_12338"), val = tensor([1, 1])]; + tensor var_12340 = const()[name = tensor("op_12340"), val = tensor([1, 1])]; + tensor k_253_pad_type_0 = const()[name = tensor("k_253_pad_type_0"), val = tensor("custom")]; + tensor k_253_pad_0 = const()[name = tensor("k_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4800989760)))]; + tensor k_253_cast = conv(dilations = var_12340, groups = var_6865, pad = k_253_pad_0, pad_type = k_253_pad_type_0, strides = var_12338, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_489_cast)[name = tensor("k_253_cast")]; + tensor var_12344 = const()[name = tensor("op_12344"), val = tensor([1, 1])]; + tensor var_12346 = const()[name = tensor("op_12346"), val = tensor([1, 1])]; + tensor v_253_pad_type_0 = const()[name = tensor("v_253_pad_type_0"), val = tensor("custom")]; + tensor v_253_pad_0 = const()[name = tensor("v_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4804266624)))]; + tensor v_253_cast = conv(dilations = var_12346, groups = var_6865, pad = v_253_pad_0, pad_type = v_253_pad_type_0, strides = var_12344, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_489_cast)[name = tensor("v_253_cast")]; + tensor var_12350 = const()[name = tensor("op_12350"), val = tensor([2, 20, 64, -1])]; + tensor var_12351_cast = reshape(shape = var_12350, x = q_253_cast)[name = tensor("op_12351_cast")]; + tensor var_12352 = const()[name = tensor("op_12352"), val = tensor([2, 20, 64, -1])]; + tensor var_12353_cast = reshape(shape = var_12352, x = k_253_cast)[name = tensor("op_12353_cast")]; + tensor var_12354 = const()[name = tensor("op_12354"), val = tensor([2, 20, 64, -1])]; + tensor var_12355_cast = reshape(shape = var_12354, x = v_253_cast)[name = tensor("op_12355_cast")]; + tensor attn_weights_505_transpose_x_0 = const()[name = tensor("attn_weights_505_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_505_transpose_y_0 = const()[name = tensor("attn_weights_505_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_505_cast = matmul(transpose_x = attn_weights_505_transpose_x_0, transpose_y = attn_weights_505_transpose_y_0, x = var_12351_cast, y = var_12353_cast)[name = tensor("attn_weights_505_cast")]; + tensor attn_weights_507_cast = mul(x = attn_weights_505_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_507_cast")]; + tensor var_12359_cast = softmax(axis = var_6849, x = attn_weights_507_cast)[name = tensor("op_12359_cast")]; + tensor attn_253_transpose_x_0 = const()[name = tensor("attn_253_transpose_x_0"), val = tensor(false)]; + tensor attn_253_transpose_y_0 = const()[name = tensor("attn_253_transpose_y_0"), val = tensor(true)]; + tensor attn_253_cast = matmul(transpose_x = attn_253_transpose_x_0, transpose_y = attn_253_transpose_y_0, x = var_12355_cast, y = var_12359_cast)[name = tensor("attn_253_cast")]; + tensor var_12363 = const()[name = tensor("op_12363"), val = tensor([2, 1280, 1, -1])]; + tensor input_707_cast = reshape(shape = var_12363, x = attn_253_cast)[name = tensor("input_707_cast")]; + tensor var_12368 = const()[name = tensor("op_12368"), val = tensor([1, 1])]; + tensor var_12370 = const()[name = tensor("op_12370"), val = tensor([1, 1])]; + tensor var_12372_pad_type_0 = const()[name = tensor("op_12372_pad_type_0"), val = tensor("custom")]; + tensor var_12372_pad_0 = const()[name = tensor("op_12372_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4807543488)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4810820352)))]; + tensor var_12372_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_12370, groups = var_6865, pad = var_12372_pad_0, pad_type = var_12372_pad_type_0, strides = var_12368, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_707_cast)[name = tensor("op_12372_cast")]; + tensor inputs_381_cast = add(x = var_12372_cast, y = inputs_379_cast)[name = tensor("inputs_381_cast")]; + tensor var_12376 = const()[name = tensor("op_12376"), val = tensor([1])]; + tensor channels_mean_381_cast = reduce_mean(axes = var_12376, keep_dims = var_6860, x = inputs_381_cast)[name = tensor("channels_mean_381_cast")]; + tensor zero_mean_381_cast = sub(x = inputs_381_cast, y = channels_mean_381_cast)[name = tensor("zero_mean_381_cast")]; + tensor zero_mean_sq_381_cast = mul(x = zero_mean_381_cast, y = zero_mean_381_cast)[name = tensor("zero_mean_sq_381_cast")]; + tensor var_12380 = const()[name = tensor("op_12380"), val = tensor([1])]; + tensor var_12381_cast = reduce_mean(axes = var_12380, keep_dims = var_6860, x = zero_mean_sq_381_cast)[name = tensor("op_12381_cast")]; + tensor var_12382_to_fp16 = const()[name = tensor("op_12382_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12383_cast = add(x = var_12381_cast, y = var_12382_to_fp16)[name = tensor("op_12383_cast")]; + tensor denom_381_epsilon_0_to_fp16 = const()[name = tensor("denom_381_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_381_cast = rsqrt(epsilon = denom_381_epsilon_0_to_fp16, x = var_12383_cast)[name = tensor("denom_381_cast")]; + tensor out_381_cast = mul(x = zero_mean_381_cast, y = denom_381_cast)[name = tensor("out_381_cast")]; + tensor var_12387_to_fp16 = const()[name = tensor("op_12387_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4810822976)))]; + tensor var_12388_cast = add(x = out_381_cast, y = var_12387_to_fp16)[name = tensor("op_12388_cast")]; + tensor var_12390_to_fp16 = const()[name = tensor("op_12390_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4810825600)))]; + tensor hidden_states_491_cast = mul(x = var_12388_cast, y = var_12390_to_fp16)[name = tensor("hidden_states_491_cast")]; + tensor var_12397 = const()[name = tensor("op_12397"), val = tensor([1, 1])]; + tensor var_12399 = const()[name = tensor("op_12399"), val = tensor([1, 1])]; + tensor q_255_pad_type_0 = const()[name = tensor("q_255_pad_type_0"), val = tensor("custom")]; + tensor q_255_pad_0 = const()[name = tensor("q_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4810828224)))]; + tensor q_255_cast = conv(dilations = var_12399, groups = var_6865, pad = q_255_pad_0, pad_type = q_255_pad_type_0, strides = var_12397, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_491_cast)[name = tensor("q_255_cast")]; + tensor var_12403 = const()[name = tensor("op_12403"), val = tensor([1, 1])]; + tensor var_12405 = const()[name = tensor("op_12405"), val = tensor([1, 1])]; + tensor k_255_pad_type_0 = const()[name = tensor("k_255_pad_type_0"), val = tensor("custom")]; + tensor k_255_pad_0 = const()[name = tensor("k_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4814105088)))]; + tensor k_255_cast = conv(dilations = var_12405, groups = var_6865, pad = k_255_pad_0, pad_type = k_255_pad_type_0, strides = var_12403, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_255_cast")]; + tensor var_12409 = const()[name = tensor("op_12409"), val = tensor([1, 1])]; + tensor var_12411 = const()[name = tensor("op_12411"), val = tensor([1, 1])]; + tensor v_255_pad_type_0 = const()[name = tensor("v_255_pad_type_0"), val = tensor("custom")]; + tensor v_255_pad_0 = const()[name = tensor("v_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4819348032)))]; + tensor v_255_cast = conv(dilations = var_12411, groups = var_6865, pad = v_255_pad_0, pad_type = v_255_pad_type_0, strides = var_12409, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_255_cast")]; + tensor var_12415 = const()[name = tensor("op_12415"), val = tensor([2, 20, 64, -1])]; + tensor var_12416_cast = reshape(shape = var_12415, x = q_255_cast)[name = tensor("op_12416_cast")]; + tensor var_12417 = const()[name = tensor("op_12417"), val = tensor([2, 20, 64, -1])]; + tensor var_12418_cast = reshape(shape = var_12417, x = k_255_cast)[name = tensor("op_12418_cast")]; + tensor var_12419 = const()[name = tensor("op_12419"), val = tensor([2, 20, 64, -1])]; + tensor var_12420_cast = reshape(shape = var_12419, x = v_255_cast)[name = tensor("op_12420_cast")]; + tensor attn_weights_509_transpose_x_0 = const()[name = tensor("attn_weights_509_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_509_transpose_y_0 = const()[name = tensor("attn_weights_509_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_509_cast = matmul(transpose_x = attn_weights_509_transpose_x_0, transpose_y = attn_weights_509_transpose_y_0, x = var_12416_cast, y = var_12418_cast)[name = tensor("attn_weights_509_cast")]; + tensor attn_weights_511_cast = mul(x = attn_weights_509_cast, y = var_6856_to_fp16)[name = tensor("attn_weights_511_cast")]; + tensor var_12424_cast = softmax(axis = var_6849, x = attn_weights_511_cast)[name = tensor("op_12424_cast")]; + tensor attn_255_transpose_x_0 = const()[name = tensor("attn_255_transpose_x_0"), val = tensor(false)]; + tensor attn_255_transpose_y_0 = const()[name = tensor("attn_255_transpose_y_0"), val = tensor(true)]; + tensor attn_255_cast = matmul(transpose_x = attn_255_transpose_x_0, transpose_y = attn_255_transpose_y_0, x = var_12420_cast, y = var_12424_cast)[name = tensor("attn_255_cast")]; + tensor var_12428 = const()[name = tensor("op_12428"), val = tensor([2, 1280, 1, -1])]; + tensor input_709_cast = reshape(shape = var_12428, x = attn_255_cast)[name = tensor("input_709_cast")]; + tensor var_12433 = const()[name = tensor("op_12433"), val = tensor([1, 1])]; + tensor var_12435 = const()[name = tensor("op_12435"), val = tensor([1, 1])]; + tensor var_12437_pad_type_0 = const()[name = tensor("op_12437_pad_type_0"), val = tensor("custom")]; + tensor var_12437_pad_0 = const()[name = tensor("op_12437_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4824590976)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4827867840)))]; + tensor var_12437_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_12435, groups = var_6865, pad = var_12437_pad_0, pad_type = var_12437_pad_type_0, strides = var_12433, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_709_cast)[name = tensor("op_12437_cast")]; + tensor inputs_383_cast = add(x = var_12437_cast, y = inputs_381_cast)[name = tensor("inputs_383_cast")]; + tensor var_12441 = const()[name = tensor("op_12441"), val = tensor([1])]; + tensor channels_mean_383_cast = reduce_mean(axes = var_12441, keep_dims = var_6860, x = inputs_383_cast)[name = tensor("channels_mean_383_cast")]; + tensor zero_mean_383_cast = sub(x = inputs_383_cast, y = channels_mean_383_cast)[name = tensor("zero_mean_383_cast")]; + tensor zero_mean_sq_383_cast = mul(x = zero_mean_383_cast, y = zero_mean_383_cast)[name = tensor("zero_mean_sq_383_cast")]; + tensor var_12445 = const()[name = tensor("op_12445"), val = tensor([1])]; + tensor var_12446_cast = reduce_mean(axes = var_12445, keep_dims = var_6860, x = zero_mean_sq_383_cast)[name = tensor("op_12446_cast")]; + tensor var_12447_to_fp16 = const()[name = tensor("op_12447_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12448_cast = add(x = var_12446_cast, y = var_12447_to_fp16)[name = tensor("op_12448_cast")]; + tensor denom_383_epsilon_0_to_fp16 = const()[name = tensor("denom_383_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_383_cast = rsqrt(epsilon = denom_383_epsilon_0_to_fp16, x = var_12448_cast)[name = tensor("denom_383_cast")]; + tensor out_383_cast = mul(x = zero_mean_383_cast, y = denom_383_cast)[name = tensor("out_383_cast")]; + tensor var_12452_to_fp16 = const()[name = tensor("op_12452_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4827870464)))]; + tensor var_12453_cast = add(x = out_383_cast, y = var_12452_to_fp16)[name = tensor("op_12453_cast")]; + tensor var_12455_to_fp16 = const()[name = tensor("op_12455_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4827873088)))]; + tensor input_711_cast = mul(x = var_12453_cast, y = var_12455_to_fp16)[name = tensor("input_711_cast")]; + tensor var_12463 = const()[name = tensor("op_12463"), val = tensor([1, 1])]; + tensor var_12465 = const()[name = tensor("op_12465"), val = tensor([1, 1])]; + tensor var_12467_pad_type_0 = const()[name = tensor("op_12467_pad_type_0"), val = tensor("custom")]; + tensor var_12467_pad_0 = const()[name = tensor("op_12467_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4827875712)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4854090176)))]; + tensor var_12467_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_12465, groups = var_6865, pad = var_12467_pad_0, pad_type = var_12467_pad_type_0, strides = var_12463, weight = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_711_cast)[name = tensor("op_12467_cast")]; + tensor var_12468_split_sizes_0 = const()[name = tensor("op_12468_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12468_axis_0 = const()[name = tensor("op_12468_axis_0"), val = tensor(1)]; + tensor var_12468_cast_0, tensor var_12468_cast_1 = split(axis = var_12468_axis_0, split_sizes = var_12468_split_sizes_0, x = var_12467_cast)[name = tensor("op_12468_cast")]; + tensor var_12470_mode_0 = const()[name = tensor("op_12470_mode_0"), val = tensor("EXACT")]; + tensor var_12470_cast = gelu(mode = var_12470_mode_0, x = var_12468_cast_1)[name = tensor("op_12470_cast")]; + tensor input_713_cast = mul(x = var_12468_cast_0, y = var_12470_cast)[name = tensor("input_713_cast")]; + tensor var_12474 = const()[name = tensor("op_12474"), val = tensor([1, 1])]; + tensor var_12476 = const()[name = tensor("op_12476"), val = tensor([1, 1])]; + tensor var_12478_pad_type_0 = const()[name = tensor("op_12478_pad_type_0"), val = tensor("custom")]; + tensor var_12478_pad_0 = const()[name = tensor("op_12478_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4854110720)))]; + tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4867217984)))]; + tensor var_12478_cast = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_12476, groups = var_6865, pad = var_12478_pad_0, pad_type = var_12478_pad_type_0, strides = var_12474, weight = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_713_cast)[name = tensor("op_12478_cast")]; + tensor hidden_states_495_cast = add(x = var_12478_cast, y = inputs_383_cast)[name = tensor("hidden_states_495_cast")]; + tensor var_12480 = const()[name = tensor("op_12480"), val = tensor([2, 1280, 32, 32])]; + tensor input_715_cast = reshape(shape = var_12480, x = hidden_states_495_cast)[name = tensor("input_715_cast")]; + tensor var_12484 = const()[name = tensor("op_12484"), val = tensor([1, 1])]; + tensor var_12486 = const()[name = tensor("op_12486"), val = tensor([1, 1])]; + tensor hidden_states_497_pad_type_0 = const()[name = tensor("hidden_states_497_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_497_pad_0 = const()[name = tensor("hidden_states_497_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4867220608)))]; + tensor up_blocks_0_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_0_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4870497472)))]; + tensor hidden_states_497_cast = conv(bias = up_blocks_0_attentions_2_proj_out_bias_to_fp16, dilations = var_12486, groups = var_6865, pad = hidden_states_497_pad_0, pad_type = hidden_states_497_pad_type_0, strides = var_12484, weight = up_blocks_0_attentions_2_proj_out_weight_to_fp16, x = input_715_cast)[name = tensor("hidden_states_497_cast")]; + tensor input_717_cast = add(x = hidden_states_497_cast, y = hidden_states_431_cast)[name = tensor("input_717_cast")]; + tensor input_719_scale_factor_height_0 = const()[name = tensor("input_719_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_719_scale_factor_width_0 = const()[name = tensor("input_719_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_719_cast = upsample_nearest_neighbor(scale_factor_height = input_719_scale_factor_height_0, scale_factor_width = input_719_scale_factor_width_0, x = input_717_cast)[name = tensor("input_719_cast")]; + tensor var_12495 = const()[name = tensor("op_12495"), val = tensor([1, 1])]; + tensor var_12497 = const()[name = tensor("op_12497"), val = tensor([1, 1])]; + tensor hidden_states_499_pad_type_0 = const()[name = tensor("hidden_states_499_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_499_pad_0 = const()[name = tensor("hidden_states_499_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4870500096)))]; + tensor up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4899991360)))]; + tensor hidden_states_499_cast = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_12497, groups = var_6865, pad = hidden_states_499_pad_0, pad_type = hidden_states_499_pad_type_0, strides = var_12495, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_719_cast)[name = tensor("hidden_states_499_cast")]; + tensor var_12502 = const()[name = tensor("op_12502"), val = tensor(3)]; + tensor var_12513 = const()[name = tensor("op_12513"), val = tensor(true)]; + tensor var_12518 = const()[name = tensor("op_12518"), val = tensor(1)]; + tensor input_721_interleave_0 = const()[name = tensor("input_721_interleave_0"), val = tensor(false)]; + tensor input_721_cast = concat(axis = var_12518, interleave = input_721_interleave_0, values = (hidden_states_499_cast, input_113_cast))[name = tensor("input_721_cast")]; + tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([2, 32, 60, 64, 64])]; + tensor reshape_120_cast = reshape(shape = reshape_120_shape_0, x = input_721_cast)[name = tensor("reshape_120_cast")]; + tensor reduce_mean_90_axes_0 = const()[name = tensor("reduce_mean_90_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_90_keep_dims_0 = const()[name = tensor("reduce_mean_90_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_90_cast = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast)[name = tensor("reduce_mean_90_cast")]; + tensor sub_60_cast = sub(x = reshape_120_cast, y = reduce_mean_90_cast)[name = tensor("sub_60_cast")]; + tensor square_30_cast = square(x = sub_60_cast)[name = tensor("square_30_cast")]; + tensor reduce_mean_92_axes_0 = const()[name = tensor("reduce_mean_92_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_92_keep_dims_0 = const()[name = tensor("reduce_mean_92_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_92_cast = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast)[name = tensor("reduce_mean_92_cast")]; + tensor add_60_y_0_to_fp16 = const()[name = tensor("add_60_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_60_cast = add(x = reduce_mean_92_cast, y = add_60_y_0_to_fp16)[name = tensor("add_60_cast")]; + tensor sqrt_30_cast = sqrt(x = add_60_cast)[name = tensor("sqrt_30_cast")]; + tensor real_div_30_cast = real_div(x = sub_60_cast, y = sqrt_30_cast)[name = tensor("real_div_30_cast")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([2, 1920, 64, 64])]; + tensor reshape_121_cast = reshape(shape = reshape_121_shape_0, x = real_div_30_cast)[name = tensor("reshape_121_cast")]; + tensor add_61_gamma_0_to_fp16 = const()[name = tensor("add_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4899993984)))]; + tensor add_61_beta_0_to_fp16 = const()[name = tensor("add_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4899997888)))]; + tensor add_61_epsilon_0_to_fp16 = const()[name = tensor("add_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_61_cast = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_121_cast)[name = tensor("add_61_cast")]; + tensor input_725_cast = silu(x = add_61_cast)[name = tensor("input_725_cast")]; + tensor var_12547 = const()[name = tensor("op_12547"), val = tensor([1, 1])]; + tensor var_12549 = const()[name = tensor("op_12549"), val = tensor([1, 1])]; + tensor hidden_states_501_pad_type_0 = const()[name = tensor("hidden_states_501_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_501_pad_0 = const()[name = tensor("hidden_states_501_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4900001792)))]; + tensor up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4922120256)))]; + tensor hidden_states_501_cast = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_12549, groups = var_12518, pad = hidden_states_501_pad_0, pad_type = hidden_states_501_pad_type_0, strides = var_12547, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_725_cast)[name = tensor("hidden_states_501_cast")]; + tensor var_12555 = const()[name = tensor("op_12555"), val = tensor([1, 1])]; + tensor var_12557 = const()[name = tensor("op_12557"), val = tensor([1, 1])]; + tensor temb_23_pad_type_0 = const()[name = tensor("temb_23_pad_type_0"), val = tensor("custom")]; + tensor temb_23_pad_0 = const()[name = tensor("temb_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4922121600)))]; + tensor up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4923760064)))]; + tensor temb_23_cast = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_12557, groups = var_12518, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_12555, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_23_cast")]; + tensor input_729_cast = add(x = hidden_states_501_cast, y = temb_23_cast)[name = tensor("input_729_cast")]; + tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_124_cast = reshape(shape = reshape_124_shape_0, x = input_729_cast)[name = tensor("reshape_124_cast")]; + tensor reduce_mean_93_axes_0 = const()[name = tensor("reduce_mean_93_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_93_keep_dims_0 = const()[name = tensor("reduce_mean_93_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_93_cast = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast)[name = tensor("reduce_mean_93_cast")]; + tensor sub_62_cast = sub(x = reshape_124_cast, y = reduce_mean_93_cast)[name = tensor("sub_62_cast")]; + tensor square_31_cast = square(x = sub_62_cast)[name = tensor("square_31_cast")]; + tensor reduce_mean_95_axes_0 = const()[name = tensor("reduce_mean_95_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_95_keep_dims_0 = const()[name = tensor("reduce_mean_95_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_95_cast = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast)[name = tensor("reduce_mean_95_cast")]; + tensor add_62_y_0_to_fp16 = const()[name = tensor("add_62_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_62_cast = add(x = reduce_mean_95_cast, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast")]; + tensor sqrt_31_cast = sqrt(x = add_62_cast)[name = tensor("sqrt_31_cast")]; + tensor real_div_31_cast = real_div(x = sub_62_cast, y = sqrt_31_cast)[name = tensor("real_div_31_cast")]; + tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_125_cast = reshape(shape = reshape_125_shape_0, x = real_div_31_cast)[name = tensor("reshape_125_cast")]; + tensor add_63_gamma_0_to_fp16 = const()[name = tensor("add_63_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4923761408)))]; + tensor add_63_beta_0_to_fp16 = const()[name = tensor("add_63_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4923762752)))]; + tensor add_63_epsilon_0_to_fp16 = const()[name = tensor("add_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_63_cast = batch_norm(beta = add_63_beta_0_to_fp16, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_125_cast)[name = tensor("add_63_cast")]; + tensor input_733_cast = silu(x = add_63_cast)[name = tensor("input_733_cast")]; + tensor var_12567 = const()[name = tensor("op_12567"), val = tensor([1, 1])]; + tensor var_12569 = const()[name = tensor("op_12569"), val = tensor([1, 1])]; + tensor hidden_states_503_pad_type_0 = const()[name = tensor("hidden_states_503_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_503_pad_0 = const()[name = tensor("hidden_states_503_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4923764096)))]; + tensor up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4931136960)))]; + tensor hidden_states_503_cast = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_12569, groups = var_12518, pad = hidden_states_503_pad_0, pad_type = hidden_states_503_pad_type_0, strides = var_12567, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_733_cast)[name = tensor("hidden_states_503_cast")]; + tensor var_12574 = const()[name = tensor("op_12574"), val = tensor([1, 1])]; + tensor var_12576 = const()[name = tensor("op_12576"), val = tensor([1, 1])]; + tensor x_11_pad_type_0 = const()[name = tensor("x_11_pad_type_0"), val = tensor("custom")]; + tensor x_11_pad_0 = const()[name = tensor("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4931138304)))]; + tensor up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4933595968)))]; + tensor x_11_cast = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_12576, groups = var_12518, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_12574, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_721_cast)[name = tensor("x_11_cast")]; + tensor hidden_states_505_cast = add(x = x_11_cast, y = hidden_states_503_cast)[name = tensor("hidden_states_505_cast")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_128_cast = reshape(shape = reshape_128_shape_0, x = hidden_states_505_cast)[name = tensor("reshape_128_cast")]; + tensor reduce_mean_96_axes_0 = const()[name = tensor("reduce_mean_96_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_96_keep_dims_0 = const()[name = tensor("reduce_mean_96_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_96_cast = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast)[name = tensor("reduce_mean_96_cast")]; + tensor sub_64_cast = sub(x = reshape_128_cast, y = reduce_mean_96_cast)[name = tensor("sub_64_cast")]; + tensor square_32_cast = square(x = sub_64_cast)[name = tensor("square_32_cast")]; + tensor reduce_mean_98_axes_0 = const()[name = tensor("reduce_mean_98_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_98_keep_dims_0 = const()[name = tensor("reduce_mean_98_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_98_cast = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast)[name = tensor("reduce_mean_98_cast")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_64_cast = add(x = reduce_mean_98_cast, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast")]; + tensor sqrt_32_cast = sqrt(x = add_64_cast)[name = tensor("sqrt_32_cast")]; + tensor real_div_32_cast = real_div(x = sub_64_cast, y = sqrt_32_cast)[name = tensor("real_div_32_cast")]; + tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_129_cast = reshape(shape = reshape_129_shape_0, x = real_div_32_cast)[name = tensor("reshape_129_cast")]; + tensor add_65_gamma_0_to_fp16 = const()[name = tensor("add_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4933597312)))]; + tensor add_65_beta_0_to_fp16 = const()[name = tensor("add_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4933598656)))]; + tensor add_65_epsilon_0_to_fp16 = const()[name = tensor("add_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_65_cast = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_129_cast)[name = tensor("add_65_cast")]; + tensor var_12598 = const()[name = tensor("op_12598"), val = tensor([1, 1])]; + tensor var_12600 = const()[name = tensor("op_12600"), val = tensor([1, 1])]; + tensor hidden_states_507_pad_type_0 = const()[name = tensor("hidden_states_507_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_507_pad_0 = const()[name = tensor("hidden_states_507_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4933600000)))]; + tensor up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4934419264)))]; + tensor hidden_states_507_cast = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_12600, groups = var_12518, pad = hidden_states_507_pad_0, pad_type = hidden_states_507_pad_type_0, strides = var_12598, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_65_cast)[name = tensor("hidden_states_507_cast")]; + tensor var_12605 = const()[name = tensor("op_12605"), val = tensor([2, 640, 1, 4096])]; + tensor inputs_385_cast = reshape(shape = var_12605, x = hidden_states_507_cast)[name = tensor("inputs_385_cast")]; + tensor var_12615 = const()[name = tensor("op_12615"), val = tensor([1])]; + tensor channels_mean_385_cast = reduce_mean(axes = var_12615, keep_dims = var_12513, x = inputs_385_cast)[name = tensor("channels_mean_385_cast")]; + tensor zero_mean_385_cast = sub(x = inputs_385_cast, y = channels_mean_385_cast)[name = tensor("zero_mean_385_cast")]; + tensor zero_mean_sq_385_cast = mul(x = zero_mean_385_cast, y = zero_mean_385_cast)[name = tensor("zero_mean_sq_385_cast")]; + tensor var_12619 = const()[name = tensor("op_12619"), val = tensor([1])]; + tensor var_12620_cast = reduce_mean(axes = var_12619, keep_dims = var_12513, x = zero_mean_sq_385_cast)[name = tensor("op_12620_cast")]; + tensor var_12621_to_fp16 = const()[name = tensor("op_12621_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12622_cast = add(x = var_12620_cast, y = var_12621_to_fp16)[name = tensor("op_12622_cast")]; + tensor denom_385_epsilon_0_to_fp16 = const()[name = tensor("denom_385_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_385_cast = rsqrt(epsilon = denom_385_epsilon_0_to_fp16, x = var_12622_cast)[name = tensor("denom_385_cast")]; + tensor out_385_cast = mul(x = zero_mean_385_cast, y = denom_385_cast)[name = tensor("out_385_cast")]; + tensor var_12626_to_fp16 = const()[name = tensor("op_12626_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4934420608)))]; + tensor var_12627_cast = add(x = out_385_cast, y = var_12626_to_fp16)[name = tensor("op_12627_cast")]; + tensor var_12629_to_fp16 = const()[name = tensor("op_12629_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4934421952)))]; + tensor hidden_states_509_cast = mul(x = var_12627_cast, y = var_12629_to_fp16)[name = tensor("hidden_states_509_cast")]; + tensor var_12636 = const()[name = tensor("op_12636"), val = tensor([1, 1])]; + tensor var_12638 = const()[name = tensor("op_12638"), val = tensor([1, 1])]; + tensor q_257_pad_type_0 = const()[name = tensor("q_257_pad_type_0"), val = tensor("custom")]; + tensor q_257_pad_0 = const()[name = tensor("q_257_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4934423296)))]; + tensor q_257_cast = conv(dilations = var_12638, groups = var_12518, pad = q_257_pad_0, pad_type = q_257_pad_type_0, strides = var_12636, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_509_cast)[name = tensor("q_257_cast")]; + tensor var_12642 = const()[name = tensor("op_12642"), val = tensor([1, 1])]; + tensor var_12644 = const()[name = tensor("op_12644"), val = tensor([1, 1])]; + tensor k_257_pad_type_0 = const()[name = tensor("k_257_pad_type_0"), val = tensor("custom")]; + tensor k_257_pad_0 = const()[name = tensor("k_257_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4935242560)))]; + tensor k_257_cast = conv(dilations = var_12644, groups = var_12518, pad = k_257_pad_0, pad_type = k_257_pad_type_0, strides = var_12642, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_509_cast)[name = tensor("k_257_cast")]; + tensor var_12648 = const()[name = tensor("op_12648"), val = tensor([1, 1])]; + tensor var_12650 = const()[name = tensor("op_12650"), val = tensor([1, 1])]; + tensor v_257_pad_type_0 = const()[name = tensor("v_257_pad_type_0"), val = tensor("custom")]; + tensor v_257_pad_0 = const()[name = tensor("v_257_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4936061824)))]; + tensor v_257_cast = conv(dilations = var_12650, groups = var_12518, pad = v_257_pad_0, pad_type = v_257_pad_type_0, strides = var_12648, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_509_cast)[name = tensor("v_257_cast")]; + tensor var_12654 = const()[name = tensor("op_12654"), val = tensor([2, 10, 64, -1])]; + tensor var_12655_cast = reshape(shape = var_12654, x = q_257_cast)[name = tensor("op_12655_cast")]; + tensor var_12656 = const()[name = tensor("op_12656"), val = tensor([2, 10, 64, -1])]; + tensor var_12657_cast = reshape(shape = var_12656, x = k_257_cast)[name = tensor("op_12657_cast")]; + tensor var_12658 = const()[name = tensor("op_12658"), val = tensor([2, 10, 64, -1])]; + tensor var_12659_cast = reshape(shape = var_12658, x = v_257_cast)[name = tensor("op_12659_cast")]; + tensor attn_weights_513_transpose_x_0 = const()[name = tensor("attn_weights_513_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_513_transpose_y_0 = const()[name = tensor("attn_weights_513_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_513_cast = matmul(transpose_x = attn_weights_513_transpose_x_0, transpose_y = attn_weights_513_transpose_y_0, x = var_12655_cast, y = var_12657_cast)[name = tensor("attn_weights_513_cast")]; + tensor var_12509_to_fp16 = const()[name = tensor("op_12509_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_515_cast = mul(x = attn_weights_513_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_515_cast")]; + tensor var_12663_cast = softmax(axis = var_12502, x = attn_weights_515_cast)[name = tensor("op_12663_cast")]; + tensor attn_257_transpose_x_0 = const()[name = tensor("attn_257_transpose_x_0"), val = tensor(false)]; + tensor attn_257_transpose_y_0 = const()[name = tensor("attn_257_transpose_y_0"), val = tensor(true)]; + tensor attn_257_cast = matmul(transpose_x = attn_257_transpose_x_0, transpose_y = attn_257_transpose_y_0, x = var_12659_cast, y = var_12663_cast)[name = tensor("attn_257_cast")]; + tensor var_12667 = const()[name = tensor("op_12667"), val = tensor([2, 640, 1, -1])]; + tensor input_737_cast = reshape(shape = var_12667, x = attn_257_cast)[name = tensor("input_737_cast")]; + tensor var_12672 = const()[name = tensor("op_12672"), val = tensor([1, 1])]; + tensor var_12674 = const()[name = tensor("op_12674"), val = tensor([1, 1])]; + tensor var_12676_pad_type_0 = const()[name = tensor("op_12676_pad_type_0"), val = tensor("custom")]; + tensor var_12676_pad_0 = const()[name = tensor("op_12676_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4936881088)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4937700352)))]; + tensor var_12676_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_12674, groups = var_12518, pad = var_12676_pad_0, pad_type = var_12676_pad_type_0, strides = var_12672, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_737_cast)[name = tensor("op_12676_cast")]; + tensor inputs_387_cast = add(x = var_12676_cast, y = inputs_385_cast)[name = tensor("inputs_387_cast")]; + tensor var_12680 = const()[name = tensor("op_12680"), val = tensor([1])]; + tensor channels_mean_387_cast = reduce_mean(axes = var_12680, keep_dims = var_12513, x = inputs_387_cast)[name = tensor("channels_mean_387_cast")]; + tensor zero_mean_387_cast = sub(x = inputs_387_cast, y = channels_mean_387_cast)[name = tensor("zero_mean_387_cast")]; + tensor zero_mean_sq_387_cast = mul(x = zero_mean_387_cast, y = zero_mean_387_cast)[name = tensor("zero_mean_sq_387_cast")]; + tensor var_12684 = const()[name = tensor("op_12684"), val = tensor([1])]; + tensor var_12685_cast = reduce_mean(axes = var_12684, keep_dims = var_12513, x = zero_mean_sq_387_cast)[name = tensor("op_12685_cast")]; + tensor var_12686_to_fp16 = const()[name = tensor("op_12686_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12687_cast = add(x = var_12685_cast, y = var_12686_to_fp16)[name = tensor("op_12687_cast")]; + tensor denom_387_epsilon_0_to_fp16 = const()[name = tensor("denom_387_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_387_cast = rsqrt(epsilon = denom_387_epsilon_0_to_fp16, x = var_12687_cast)[name = tensor("denom_387_cast")]; + tensor out_387_cast = mul(x = zero_mean_387_cast, y = denom_387_cast)[name = tensor("out_387_cast")]; + tensor var_12691_to_fp16 = const()[name = tensor("op_12691_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4937701696)))]; + tensor var_12692_cast = add(x = out_387_cast, y = var_12691_to_fp16)[name = tensor("op_12692_cast")]; + tensor var_12694_to_fp16 = const()[name = tensor("op_12694_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4937703040)))]; + tensor hidden_states_511_cast = mul(x = var_12692_cast, y = var_12694_to_fp16)[name = tensor("hidden_states_511_cast")]; + tensor var_12701 = const()[name = tensor("op_12701"), val = tensor([1, 1])]; + tensor var_12703 = const()[name = tensor("op_12703"), val = tensor([1, 1])]; + tensor q_259_pad_type_0 = const()[name = tensor("q_259_pad_type_0"), val = tensor("custom")]; + tensor q_259_pad_0 = const()[name = tensor("q_259_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4937704384)))]; + tensor q_259_cast = conv(dilations = var_12703, groups = var_12518, pad = q_259_pad_0, pad_type = q_259_pad_type_0, strides = var_12701, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_511_cast)[name = tensor("q_259_cast")]; + tensor var_12707 = const()[name = tensor("op_12707"), val = tensor([1, 1])]; + tensor var_12709 = const()[name = tensor("op_12709"), val = tensor([1, 1])]; + tensor k_259_pad_type_0 = const()[name = tensor("k_259_pad_type_0"), val = tensor("custom")]; + tensor k_259_pad_0 = const()[name = tensor("k_259_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4938523648)))]; + tensor k_259_cast = conv(dilations = var_12709, groups = var_12518, pad = k_259_pad_0, pad_type = k_259_pad_type_0, strides = var_12707, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_259_cast")]; + tensor var_12713 = const()[name = tensor("op_12713"), val = tensor([1, 1])]; + tensor var_12715 = const()[name = tensor("op_12715"), val = tensor([1, 1])]; + tensor v_259_pad_type_0 = const()[name = tensor("v_259_pad_type_0"), val = tensor("custom")]; + tensor v_259_pad_0 = const()[name = tensor("v_259_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4941145152)))]; + tensor v_259_cast = conv(dilations = var_12715, groups = var_12518, pad = v_259_pad_0, pad_type = v_259_pad_type_0, strides = var_12713, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_259_cast")]; + tensor var_12719 = const()[name = tensor("op_12719"), val = tensor([2, 10, 64, -1])]; + tensor var_12720_cast = reshape(shape = var_12719, x = q_259_cast)[name = tensor("op_12720_cast")]; + tensor var_12721 = const()[name = tensor("op_12721"), val = tensor([2, 10, 64, -1])]; + tensor var_12722_cast = reshape(shape = var_12721, x = k_259_cast)[name = tensor("op_12722_cast")]; + tensor var_12723 = const()[name = tensor("op_12723"), val = tensor([2, 10, 64, -1])]; + tensor var_12724_cast = reshape(shape = var_12723, x = v_259_cast)[name = tensor("op_12724_cast")]; + tensor attn_weights_517_transpose_x_0 = const()[name = tensor("attn_weights_517_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_517_transpose_y_0 = const()[name = tensor("attn_weights_517_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_517_cast = matmul(transpose_x = attn_weights_517_transpose_x_0, transpose_y = attn_weights_517_transpose_y_0, x = var_12720_cast, y = var_12722_cast)[name = tensor("attn_weights_517_cast")]; + tensor attn_weights_519_cast = mul(x = attn_weights_517_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_519_cast")]; + tensor var_12728_cast = softmax(axis = var_12502, x = attn_weights_519_cast)[name = tensor("op_12728_cast")]; + tensor attn_259_transpose_x_0 = const()[name = tensor("attn_259_transpose_x_0"), val = tensor(false)]; + tensor attn_259_transpose_y_0 = const()[name = tensor("attn_259_transpose_y_0"), val = tensor(true)]; + tensor attn_259_cast = matmul(transpose_x = attn_259_transpose_x_0, transpose_y = attn_259_transpose_y_0, x = var_12724_cast, y = var_12728_cast)[name = tensor("attn_259_cast")]; + tensor var_12732 = const()[name = tensor("op_12732"), val = tensor([2, 640, 1, -1])]; + tensor input_739_cast = reshape(shape = var_12732, x = attn_259_cast)[name = tensor("input_739_cast")]; + tensor var_12737 = const()[name = tensor("op_12737"), val = tensor([1, 1])]; + tensor var_12739 = const()[name = tensor("op_12739"), val = tensor([1, 1])]; + tensor var_12741_pad_type_0 = const()[name = tensor("op_12741_pad_type_0"), val = tensor("custom")]; + tensor var_12741_pad_0 = const()[name = tensor("op_12741_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4943766656)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4944585920)))]; + tensor var_12741_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_12739, groups = var_12518, pad = var_12741_pad_0, pad_type = var_12741_pad_type_0, strides = var_12737, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_739_cast)[name = tensor("op_12741_cast")]; + tensor inputs_389_cast = add(x = var_12741_cast, y = inputs_387_cast)[name = tensor("inputs_389_cast")]; + tensor var_12745 = const()[name = tensor("op_12745"), val = tensor([1])]; + tensor channels_mean_389_cast = reduce_mean(axes = var_12745, keep_dims = var_12513, x = inputs_389_cast)[name = tensor("channels_mean_389_cast")]; + tensor zero_mean_389_cast = sub(x = inputs_389_cast, y = channels_mean_389_cast)[name = tensor("zero_mean_389_cast")]; + tensor zero_mean_sq_389_cast = mul(x = zero_mean_389_cast, y = zero_mean_389_cast)[name = tensor("zero_mean_sq_389_cast")]; + tensor var_12749 = const()[name = tensor("op_12749"), val = tensor([1])]; + tensor var_12750_cast = reduce_mean(axes = var_12749, keep_dims = var_12513, x = zero_mean_sq_389_cast)[name = tensor("op_12750_cast")]; + tensor var_12751_to_fp16 = const()[name = tensor("op_12751_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12752_cast = add(x = var_12750_cast, y = var_12751_to_fp16)[name = tensor("op_12752_cast")]; + tensor denom_389_epsilon_0_to_fp16 = const()[name = tensor("denom_389_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_389_cast = rsqrt(epsilon = denom_389_epsilon_0_to_fp16, x = var_12752_cast)[name = tensor("denom_389_cast")]; + tensor out_389_cast = mul(x = zero_mean_389_cast, y = denom_389_cast)[name = tensor("out_389_cast")]; + tensor var_12756_to_fp16 = const()[name = tensor("op_12756_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4944587264)))]; + tensor var_12757_cast = add(x = out_389_cast, y = var_12756_to_fp16)[name = tensor("op_12757_cast")]; + tensor var_12759_to_fp16 = const()[name = tensor("op_12759_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4944588608)))]; + tensor input_741_cast = mul(x = var_12757_cast, y = var_12759_to_fp16)[name = tensor("input_741_cast")]; + tensor var_12767 = const()[name = tensor("op_12767"), val = tensor([1, 1])]; + tensor var_12769 = const()[name = tensor("op_12769"), val = tensor([1, 1])]; + tensor var_12771_pad_type_0 = const()[name = tensor("op_12771_pad_type_0"), val = tensor("custom")]; + tensor var_12771_pad_0 = const()[name = tensor("op_12771_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4944589952)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4951143616)))]; + tensor var_12771_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_12769, groups = var_12518, pad = var_12771_pad_0, pad_type = var_12771_pad_type_0, strides = var_12767, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_741_cast)[name = tensor("op_12771_cast")]; + tensor var_12772_split_sizes_0 = const()[name = tensor("op_12772_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_12772_axis_0 = const()[name = tensor("op_12772_axis_0"), val = tensor(1)]; + tensor var_12772_cast_0, tensor var_12772_cast_1 = split(axis = var_12772_axis_0, split_sizes = var_12772_split_sizes_0, x = var_12771_cast)[name = tensor("op_12772_cast")]; + tensor var_12774_mode_0 = const()[name = tensor("op_12774_mode_0"), val = tensor("EXACT")]; + tensor var_12774_cast = gelu(mode = var_12774_mode_0, x = var_12772_cast_1)[name = tensor("op_12774_cast")]; + tensor input_743_cast = mul(x = var_12772_cast_0, y = var_12774_cast)[name = tensor("input_743_cast")]; + tensor var_12778 = const()[name = tensor("op_12778"), val = tensor([1, 1])]; + tensor var_12780 = const()[name = tensor("op_12780"), val = tensor([1, 1])]; + tensor var_12782_pad_type_0 = const()[name = tensor("op_12782_pad_type_0"), val = tensor("custom")]; + tensor var_12782_pad_0 = const()[name = tensor("op_12782_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4951153920)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4954430784)))]; + tensor var_12782_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_12780, groups = var_12518, pad = var_12782_pad_0, pad_type = var_12782_pad_type_0, strides = var_12778, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_743_cast)[name = tensor("op_12782_cast")]; + tensor inputs_391_cast = add(x = var_12782_cast, y = inputs_389_cast)[name = tensor("inputs_391_cast")]; + tensor var_12792 = const()[name = tensor("op_12792"), val = tensor([1])]; + tensor channels_mean_391_cast = reduce_mean(axes = var_12792, keep_dims = var_12513, x = inputs_391_cast)[name = tensor("channels_mean_391_cast")]; + tensor zero_mean_391_cast = sub(x = inputs_391_cast, y = channels_mean_391_cast)[name = tensor("zero_mean_391_cast")]; + tensor zero_mean_sq_391_cast = mul(x = zero_mean_391_cast, y = zero_mean_391_cast)[name = tensor("zero_mean_sq_391_cast")]; + tensor var_12796 = const()[name = tensor("op_12796"), val = tensor([1])]; + tensor var_12797_cast = reduce_mean(axes = var_12796, keep_dims = var_12513, x = zero_mean_sq_391_cast)[name = tensor("op_12797_cast")]; + tensor var_12798_to_fp16 = const()[name = tensor("op_12798_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12799_cast = add(x = var_12797_cast, y = var_12798_to_fp16)[name = tensor("op_12799_cast")]; + tensor denom_391_epsilon_0_to_fp16 = const()[name = tensor("denom_391_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_391_cast = rsqrt(epsilon = denom_391_epsilon_0_to_fp16, x = var_12799_cast)[name = tensor("denom_391_cast")]; + tensor out_391_cast = mul(x = zero_mean_391_cast, y = denom_391_cast)[name = tensor("out_391_cast")]; + tensor var_12803_to_fp16 = const()[name = tensor("op_12803_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4954432128)))]; + tensor var_12804_cast = add(x = out_391_cast, y = var_12803_to_fp16)[name = tensor("op_12804_cast")]; + tensor var_12806_to_fp16 = const()[name = tensor("op_12806_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4954433472)))]; + tensor hidden_states_515_cast = mul(x = var_12804_cast, y = var_12806_to_fp16)[name = tensor("hidden_states_515_cast")]; + tensor var_12813 = const()[name = tensor("op_12813"), val = tensor([1, 1])]; + tensor var_12815 = const()[name = tensor("op_12815"), val = tensor([1, 1])]; + tensor q_261_pad_type_0 = const()[name = tensor("q_261_pad_type_0"), val = tensor("custom")]; + tensor q_261_pad_0 = const()[name = tensor("q_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4954434816)))]; + tensor q_261_cast = conv(dilations = var_12815, groups = var_12518, pad = q_261_pad_0, pad_type = q_261_pad_type_0, strides = var_12813, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_515_cast)[name = tensor("q_261_cast")]; + tensor var_12819 = const()[name = tensor("op_12819"), val = tensor([1, 1])]; + tensor var_12821 = const()[name = tensor("op_12821"), val = tensor([1, 1])]; + tensor k_261_pad_type_0 = const()[name = tensor("k_261_pad_type_0"), val = tensor("custom")]; + tensor k_261_pad_0 = const()[name = tensor("k_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4955254080)))]; + tensor k_261_cast = conv(dilations = var_12821, groups = var_12518, pad = k_261_pad_0, pad_type = k_261_pad_type_0, strides = var_12819, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_515_cast)[name = tensor("k_261_cast")]; + tensor var_12825 = const()[name = tensor("op_12825"), val = tensor([1, 1])]; + tensor var_12827 = const()[name = tensor("op_12827"), val = tensor([1, 1])]; + tensor v_261_pad_type_0 = const()[name = tensor("v_261_pad_type_0"), val = tensor("custom")]; + tensor v_261_pad_0 = const()[name = tensor("v_261_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4956073344)))]; + tensor v_261_cast = conv(dilations = var_12827, groups = var_12518, pad = v_261_pad_0, pad_type = v_261_pad_type_0, strides = var_12825, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_515_cast)[name = tensor("v_261_cast")]; + tensor var_12831 = const()[name = tensor("op_12831"), val = tensor([2, 10, 64, -1])]; + tensor var_12832_cast = reshape(shape = var_12831, x = q_261_cast)[name = tensor("op_12832_cast")]; + tensor var_12833 = const()[name = tensor("op_12833"), val = tensor([2, 10, 64, -1])]; + tensor var_12834_cast = reshape(shape = var_12833, x = k_261_cast)[name = tensor("op_12834_cast")]; + tensor var_12835 = const()[name = tensor("op_12835"), val = tensor([2, 10, 64, -1])]; + tensor var_12836_cast = reshape(shape = var_12835, x = v_261_cast)[name = tensor("op_12836_cast")]; + tensor attn_weights_521_transpose_x_0 = const()[name = tensor("attn_weights_521_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_521_transpose_y_0 = const()[name = tensor("attn_weights_521_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_521_cast = matmul(transpose_x = attn_weights_521_transpose_x_0, transpose_y = attn_weights_521_transpose_y_0, x = var_12832_cast, y = var_12834_cast)[name = tensor("attn_weights_521_cast")]; + tensor attn_weights_523_cast = mul(x = attn_weights_521_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_523_cast")]; + tensor var_12840_cast = softmax(axis = var_12502, x = attn_weights_523_cast)[name = tensor("op_12840_cast")]; + tensor attn_261_transpose_x_0 = const()[name = tensor("attn_261_transpose_x_0"), val = tensor(false)]; + tensor attn_261_transpose_y_0 = const()[name = tensor("attn_261_transpose_y_0"), val = tensor(true)]; + tensor attn_261_cast = matmul(transpose_x = attn_261_transpose_x_0, transpose_y = attn_261_transpose_y_0, x = var_12836_cast, y = var_12840_cast)[name = tensor("attn_261_cast")]; + tensor var_12844 = const()[name = tensor("op_12844"), val = tensor([2, 640, 1, -1])]; + tensor input_745_cast = reshape(shape = var_12844, x = attn_261_cast)[name = tensor("input_745_cast")]; + tensor var_12849 = const()[name = tensor("op_12849"), val = tensor([1, 1])]; + tensor var_12851 = const()[name = tensor("op_12851"), val = tensor([1, 1])]; + tensor var_12853_pad_type_0 = const()[name = tensor("op_12853_pad_type_0"), val = tensor("custom")]; + tensor var_12853_pad_0 = const()[name = tensor("op_12853_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4956892608)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4957711872)))]; + tensor var_12853_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_12851, groups = var_12518, pad = var_12853_pad_0, pad_type = var_12853_pad_type_0, strides = var_12849, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_745_cast)[name = tensor("op_12853_cast")]; + tensor inputs_393_cast = add(x = var_12853_cast, y = inputs_391_cast)[name = tensor("inputs_393_cast")]; + tensor var_12857 = const()[name = tensor("op_12857"), val = tensor([1])]; + tensor channels_mean_393_cast = reduce_mean(axes = var_12857, keep_dims = var_12513, x = inputs_393_cast)[name = tensor("channels_mean_393_cast")]; + tensor zero_mean_393_cast = sub(x = inputs_393_cast, y = channels_mean_393_cast)[name = tensor("zero_mean_393_cast")]; + tensor zero_mean_sq_393_cast = mul(x = zero_mean_393_cast, y = zero_mean_393_cast)[name = tensor("zero_mean_sq_393_cast")]; + tensor var_12861 = const()[name = tensor("op_12861"), val = tensor([1])]; + tensor var_12862_cast = reduce_mean(axes = var_12861, keep_dims = var_12513, x = zero_mean_sq_393_cast)[name = tensor("op_12862_cast")]; + tensor var_12863_to_fp16 = const()[name = tensor("op_12863_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12864_cast = add(x = var_12862_cast, y = var_12863_to_fp16)[name = tensor("op_12864_cast")]; + tensor denom_393_epsilon_0_to_fp16 = const()[name = tensor("denom_393_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_393_cast = rsqrt(epsilon = denom_393_epsilon_0_to_fp16, x = var_12864_cast)[name = tensor("denom_393_cast")]; + tensor out_393_cast = mul(x = zero_mean_393_cast, y = denom_393_cast)[name = tensor("out_393_cast")]; + tensor var_12868_to_fp16 = const()[name = tensor("op_12868_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4957713216)))]; + tensor var_12869_cast = add(x = out_393_cast, y = var_12868_to_fp16)[name = tensor("op_12869_cast")]; + tensor var_12871_to_fp16 = const()[name = tensor("op_12871_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4957714560)))]; + tensor hidden_states_517_cast = mul(x = var_12869_cast, y = var_12871_to_fp16)[name = tensor("hidden_states_517_cast")]; + tensor var_12878 = const()[name = tensor("op_12878"), val = tensor([1, 1])]; + tensor var_12880 = const()[name = tensor("op_12880"), val = tensor([1, 1])]; + tensor q_263_pad_type_0 = const()[name = tensor("q_263_pad_type_0"), val = tensor("custom")]; + tensor q_263_pad_0 = const()[name = tensor("q_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4957715904)))]; + tensor q_263_cast = conv(dilations = var_12880, groups = var_12518, pad = q_263_pad_0, pad_type = q_263_pad_type_0, strides = var_12878, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_517_cast)[name = tensor("q_263_cast")]; + tensor var_12884 = const()[name = tensor("op_12884"), val = tensor([1, 1])]; + tensor var_12886 = const()[name = tensor("op_12886"), val = tensor([1, 1])]; + tensor k_263_pad_type_0 = const()[name = tensor("k_263_pad_type_0"), val = tensor("custom")]; + tensor k_263_pad_0 = const()[name = tensor("k_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4958535168)))]; + tensor k_263_cast = conv(dilations = var_12886, groups = var_12518, pad = k_263_pad_0, pad_type = k_263_pad_type_0, strides = var_12884, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_263_cast")]; + tensor var_12890 = const()[name = tensor("op_12890"), val = tensor([1, 1])]; + tensor var_12892 = const()[name = tensor("op_12892"), val = tensor([1, 1])]; + tensor v_263_pad_type_0 = const()[name = tensor("v_263_pad_type_0"), val = tensor("custom")]; + tensor v_263_pad_0 = const()[name = tensor("v_263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4961156672)))]; + tensor v_263_cast = conv(dilations = var_12892, groups = var_12518, pad = v_263_pad_0, pad_type = v_263_pad_type_0, strides = var_12890, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_263_cast")]; + tensor var_12896 = const()[name = tensor("op_12896"), val = tensor([2, 10, 64, -1])]; + tensor var_12897_cast = reshape(shape = var_12896, x = q_263_cast)[name = tensor("op_12897_cast")]; + tensor var_12898 = const()[name = tensor("op_12898"), val = tensor([2, 10, 64, -1])]; + tensor var_12899_cast = reshape(shape = var_12898, x = k_263_cast)[name = tensor("op_12899_cast")]; + tensor var_12900 = const()[name = tensor("op_12900"), val = tensor([2, 10, 64, -1])]; + tensor var_12901_cast = reshape(shape = var_12900, x = v_263_cast)[name = tensor("op_12901_cast")]; + tensor attn_weights_525_transpose_x_0 = const()[name = tensor("attn_weights_525_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_525_transpose_y_0 = const()[name = tensor("attn_weights_525_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_525_cast = matmul(transpose_x = attn_weights_525_transpose_x_0, transpose_y = attn_weights_525_transpose_y_0, x = var_12897_cast, y = var_12899_cast)[name = tensor("attn_weights_525_cast")]; + tensor attn_weights_527_cast = mul(x = attn_weights_525_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_527_cast")]; + tensor var_12905_cast = softmax(axis = var_12502, x = attn_weights_527_cast)[name = tensor("op_12905_cast")]; + tensor attn_263_transpose_x_0 = const()[name = tensor("attn_263_transpose_x_0"), val = tensor(false)]; + tensor attn_263_transpose_y_0 = const()[name = tensor("attn_263_transpose_y_0"), val = tensor(true)]; + tensor attn_263_cast = matmul(transpose_x = attn_263_transpose_x_0, transpose_y = attn_263_transpose_y_0, x = var_12901_cast, y = var_12905_cast)[name = tensor("attn_263_cast")]; + tensor var_12909 = const()[name = tensor("op_12909"), val = tensor([2, 640, 1, -1])]; + tensor input_747_cast = reshape(shape = var_12909, x = attn_263_cast)[name = tensor("input_747_cast")]; + tensor var_12914 = const()[name = tensor("op_12914"), val = tensor([1, 1])]; + tensor var_12916 = const()[name = tensor("op_12916"), val = tensor([1, 1])]; + tensor var_12918_pad_type_0 = const()[name = tensor("op_12918_pad_type_0"), val = tensor("custom")]; + tensor var_12918_pad_0 = const()[name = tensor("op_12918_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4963778176)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4964597440)))]; + tensor var_12918_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_12916, groups = var_12518, pad = var_12918_pad_0, pad_type = var_12918_pad_type_0, strides = var_12914, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_747_cast)[name = tensor("op_12918_cast")]; + tensor inputs_395_cast = add(x = var_12918_cast, y = inputs_393_cast)[name = tensor("inputs_395_cast")]; + tensor var_12922 = const()[name = tensor("op_12922"), val = tensor([1])]; + tensor channels_mean_395_cast = reduce_mean(axes = var_12922, keep_dims = var_12513, x = inputs_395_cast)[name = tensor("channels_mean_395_cast")]; + tensor zero_mean_395_cast = sub(x = inputs_395_cast, y = channels_mean_395_cast)[name = tensor("zero_mean_395_cast")]; + tensor zero_mean_sq_395_cast = mul(x = zero_mean_395_cast, y = zero_mean_395_cast)[name = tensor("zero_mean_sq_395_cast")]; + tensor var_12926 = const()[name = tensor("op_12926"), val = tensor([1])]; + tensor var_12927_cast = reduce_mean(axes = var_12926, keep_dims = var_12513, x = zero_mean_sq_395_cast)[name = tensor("op_12927_cast")]; + tensor var_12928_to_fp16 = const()[name = tensor("op_12928_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12929_cast = add(x = var_12927_cast, y = var_12928_to_fp16)[name = tensor("op_12929_cast")]; + tensor denom_395_epsilon_0_to_fp16 = const()[name = tensor("denom_395_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_395_cast = rsqrt(epsilon = denom_395_epsilon_0_to_fp16, x = var_12929_cast)[name = tensor("denom_395_cast")]; + tensor out_395_cast = mul(x = zero_mean_395_cast, y = denom_395_cast)[name = tensor("out_395_cast")]; + tensor var_12933_to_fp16 = const()[name = tensor("op_12933_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4964598784)))]; + tensor var_12934_cast = add(x = out_395_cast, y = var_12933_to_fp16)[name = tensor("op_12934_cast")]; + tensor var_12936_to_fp16 = const()[name = tensor("op_12936_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4964600128)))]; + tensor input_749_cast = mul(x = var_12934_cast, y = var_12936_to_fp16)[name = tensor("input_749_cast")]; + tensor var_12944 = const()[name = tensor("op_12944"), val = tensor([1, 1])]; + tensor var_12946 = const()[name = tensor("op_12946"), val = tensor([1, 1])]; + tensor var_12948_pad_type_0 = const()[name = tensor("op_12948_pad_type_0"), val = tensor("custom")]; + tensor var_12948_pad_0 = const()[name = tensor("op_12948_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4964601472)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4971155136)))]; + tensor var_12948_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_12946, groups = var_12518, pad = var_12948_pad_0, pad_type = var_12948_pad_type_0, strides = var_12944, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_749_cast)[name = tensor("op_12948_cast")]; + tensor var_12949_split_sizes_0 = const()[name = tensor("op_12949_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_12949_axis_0 = const()[name = tensor("op_12949_axis_0"), val = tensor(1)]; + tensor var_12949_cast_0, tensor var_12949_cast_1 = split(axis = var_12949_axis_0, split_sizes = var_12949_split_sizes_0, x = var_12948_cast)[name = tensor("op_12949_cast")]; + tensor var_12951_mode_0 = const()[name = tensor("op_12951_mode_0"), val = tensor("EXACT")]; + tensor var_12951_cast = gelu(mode = var_12951_mode_0, x = var_12949_cast_1)[name = tensor("op_12951_cast")]; + tensor input_751_cast = mul(x = var_12949_cast_0, y = var_12951_cast)[name = tensor("input_751_cast")]; + tensor var_12955 = const()[name = tensor("op_12955"), val = tensor([1, 1])]; + tensor var_12957 = const()[name = tensor("op_12957"), val = tensor([1, 1])]; + tensor var_12959_pad_type_0 = const()[name = tensor("op_12959_pad_type_0"), val = tensor("custom")]; + tensor var_12959_pad_0 = const()[name = tensor("op_12959_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4971165440)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4974442304)))]; + tensor var_12959_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_12957, groups = var_12518, pad = var_12959_pad_0, pad_type = var_12959_pad_type_0, strides = var_12955, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_751_cast)[name = tensor("op_12959_cast")]; + tensor hidden_states_521_cast = add(x = var_12959_cast, y = inputs_395_cast)[name = tensor("hidden_states_521_cast")]; + tensor var_12961 = const()[name = tensor("op_12961"), val = tensor([2, 640, 64, 64])]; + tensor input_753_cast = reshape(shape = var_12961, x = hidden_states_521_cast)[name = tensor("input_753_cast")]; + tensor var_12965 = const()[name = tensor("op_12965"), val = tensor([1, 1])]; + tensor var_12967 = const()[name = tensor("op_12967"), val = tensor([1, 1])]; + tensor hidden_states_523_pad_type_0 = const()[name = tensor("hidden_states_523_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_523_pad_0 = const()[name = tensor("hidden_states_523_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4974443648)))]; + tensor up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4975262912)))]; + tensor hidden_states_523_cast = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_12967, groups = var_12518, pad = hidden_states_523_pad_0, pad_type = hidden_states_523_pad_type_0, strides = var_12965, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_753_cast)[name = tensor("hidden_states_523_cast")]; + tensor hidden_states_525_cast = add(x = hidden_states_523_cast, y = hidden_states_505_cast)[name = tensor("hidden_states_525_cast")]; + tensor input_755_interleave_0 = const()[name = tensor("input_755_interleave_0"), val = tensor(false)]; + tensor input_755_cast = concat(axis = var_12518, interleave = input_755_interleave_0, values = (hidden_states_525_cast, input_79_cast))[name = tensor("input_755_cast")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([2, 32, 40, 64, 64])]; + tensor reshape_132_cast = reshape(shape = reshape_132_shape_0, x = input_755_cast)[name = tensor("reshape_132_cast")]; + tensor reduce_mean_99_axes_0 = const()[name = tensor("reduce_mean_99_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_99_keep_dims_0 = const()[name = tensor("reduce_mean_99_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_99_cast = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast)[name = tensor("reduce_mean_99_cast")]; + tensor sub_66_cast = sub(x = reshape_132_cast, y = reduce_mean_99_cast)[name = tensor("sub_66_cast")]; + tensor square_33_cast = square(x = sub_66_cast)[name = tensor("square_33_cast")]; + tensor reduce_mean_101_axes_0 = const()[name = tensor("reduce_mean_101_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_101_keep_dims_0 = const()[name = tensor("reduce_mean_101_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_101_cast = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast)[name = tensor("reduce_mean_101_cast")]; + tensor add_66_y_0_to_fp16 = const()[name = tensor("add_66_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_66_cast = add(x = reduce_mean_101_cast, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast")]; + tensor sqrt_33_cast = sqrt(x = add_66_cast)[name = tensor("sqrt_33_cast")]; + tensor real_div_33_cast = real_div(x = sub_66_cast, y = sqrt_33_cast)[name = tensor("real_div_33_cast")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([2, 1280, 64, 64])]; + tensor reshape_133_cast = reshape(shape = reshape_133_shape_0, x = real_div_33_cast)[name = tensor("reshape_133_cast")]; + tensor add_67_gamma_0_to_fp16 = const()[name = tensor("add_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4975264256)))]; + tensor add_67_beta_0_to_fp16 = const()[name = tensor("add_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4975266880)))]; + tensor add_67_epsilon_0_to_fp16 = const()[name = tensor("add_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_67_cast = batch_norm(beta = add_67_beta_0_to_fp16, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_133_cast)[name = tensor("add_67_cast")]; + tensor input_759_cast = silu(x = add_67_cast)[name = tensor("input_759_cast")]; + tensor var_12985 = const()[name = tensor("op_12985"), val = tensor([1, 1])]; + tensor var_12987 = const()[name = tensor("op_12987"), val = tensor([1, 1])]; + tensor hidden_states_527_pad_type_0 = const()[name = tensor("hidden_states_527_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_527_pad_0 = const()[name = tensor("hidden_states_527_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4975269504)))]; + tensor up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4990015168)))]; + tensor hidden_states_527_cast = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_12987, groups = var_12518, pad = hidden_states_527_pad_0, pad_type = hidden_states_527_pad_type_0, strides = var_12985, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_759_cast)[name = tensor("hidden_states_527_cast")]; + tensor var_12993 = const()[name = tensor("op_12993"), val = tensor([1, 1])]; + tensor var_12995 = const()[name = tensor("op_12995"), val = tensor([1, 1])]; + tensor temb_25_pad_type_0 = const()[name = tensor("temb_25_pad_type_0"), val = tensor("custom")]; + tensor temb_25_pad_0 = const()[name = tensor("temb_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4990016512)))]; + tensor up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4991654976)))]; + tensor temb_25_cast = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_12995, groups = var_12518, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_12993, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_25_cast")]; + tensor input_763_cast = add(x = hidden_states_527_cast, y = temb_25_cast)[name = tensor("input_763_cast")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_136_cast = reshape(shape = reshape_136_shape_0, x = input_763_cast)[name = tensor("reshape_136_cast")]; + tensor reduce_mean_102_axes_0 = const()[name = tensor("reduce_mean_102_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_102_keep_dims_0 = const()[name = tensor("reduce_mean_102_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_102_cast = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast)[name = tensor("reduce_mean_102_cast")]; + tensor sub_68_cast = sub(x = reshape_136_cast, y = reduce_mean_102_cast)[name = tensor("sub_68_cast")]; + tensor square_34_cast = square(x = sub_68_cast)[name = tensor("square_34_cast")]; + tensor reduce_mean_104_axes_0 = const()[name = tensor("reduce_mean_104_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_104_keep_dims_0 = const()[name = tensor("reduce_mean_104_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_104_cast = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast)[name = tensor("reduce_mean_104_cast")]; + tensor add_68_y_0_to_fp16 = const()[name = tensor("add_68_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_68_cast = add(x = reduce_mean_104_cast, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast")]; + tensor sqrt_34_cast = sqrt(x = add_68_cast)[name = tensor("sqrt_34_cast")]; + tensor real_div_34_cast = real_div(x = sub_68_cast, y = sqrt_34_cast)[name = tensor("real_div_34_cast")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_137_cast = reshape(shape = reshape_137_shape_0, x = real_div_34_cast)[name = tensor("reshape_137_cast")]; + tensor add_69_gamma_0_to_fp16 = const()[name = tensor("add_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4991656320)))]; + tensor add_69_beta_0_to_fp16 = const()[name = tensor("add_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4991657664)))]; + tensor add_69_epsilon_0_to_fp16 = const()[name = tensor("add_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_69_cast = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_137_cast)[name = tensor("add_69_cast")]; + tensor input_767_cast = silu(x = add_69_cast)[name = tensor("input_767_cast")]; + tensor var_13005 = const()[name = tensor("op_13005"), val = tensor([1, 1])]; + tensor var_13007 = const()[name = tensor("op_13007"), val = tensor([1, 1])]; + tensor hidden_states_529_pad_type_0 = const()[name = tensor("hidden_states_529_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_529_pad_0 = const()[name = tensor("hidden_states_529_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4991659008)))]; + tensor up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4999031872)))]; + tensor hidden_states_529_cast = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_13007, groups = var_12518, pad = hidden_states_529_pad_0, pad_type = hidden_states_529_pad_type_0, strides = var_13005, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_767_cast)[name = tensor("hidden_states_529_cast")]; + tensor var_13012 = const()[name = tensor("op_13012"), val = tensor([1, 1])]; + tensor var_13014 = const()[name = tensor("op_13014"), val = tensor([1, 1])]; + tensor x_13_pad_type_0 = const()[name = tensor("x_13_pad_type_0"), val = tensor("custom")]; + tensor x_13_pad_0 = const()[name = tensor("x_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4999033216)))]; + tensor up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5000671680)))]; + tensor x_13_cast = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_13014, groups = var_12518, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_13012, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16, x = input_755_cast)[name = tensor("x_13_cast")]; + tensor hidden_states_531_cast = add(x = x_13_cast, y = hidden_states_529_cast)[name = tensor("hidden_states_531_cast")]; + tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_140_cast = reshape(shape = reshape_140_shape_0, x = hidden_states_531_cast)[name = tensor("reshape_140_cast")]; + tensor reduce_mean_105_axes_0 = const()[name = tensor("reduce_mean_105_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_105_keep_dims_0 = const()[name = tensor("reduce_mean_105_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_105_cast = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast)[name = tensor("reduce_mean_105_cast")]; + tensor sub_70_cast = sub(x = reshape_140_cast, y = reduce_mean_105_cast)[name = tensor("sub_70_cast")]; + tensor square_35_cast = square(x = sub_70_cast)[name = tensor("square_35_cast")]; + tensor reduce_mean_107_axes_0 = const()[name = tensor("reduce_mean_107_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_107_keep_dims_0 = const()[name = tensor("reduce_mean_107_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_107_cast = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast)[name = tensor("reduce_mean_107_cast")]; + tensor add_70_y_0_to_fp16 = const()[name = tensor("add_70_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_70_cast = add(x = reduce_mean_107_cast, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast")]; + tensor sqrt_35_cast = sqrt(x = add_70_cast)[name = tensor("sqrt_35_cast")]; + tensor real_div_35_cast = real_div(x = sub_70_cast, y = sqrt_35_cast)[name = tensor("real_div_35_cast")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_141_cast = reshape(shape = reshape_141_shape_0, x = real_div_35_cast)[name = tensor("reshape_141_cast")]; + tensor add_71_gamma_0_to_fp16 = const()[name = tensor("add_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5000673024)))]; + tensor add_71_beta_0_to_fp16 = const()[name = tensor("add_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5000674368)))]; + tensor add_71_epsilon_0_to_fp16 = const()[name = tensor("add_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_71_cast = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_141_cast)[name = tensor("add_71_cast")]; + tensor var_13036 = const()[name = tensor("op_13036"), val = tensor([1, 1])]; + tensor var_13038 = const()[name = tensor("op_13038"), val = tensor([1, 1])]; + tensor hidden_states_533_pad_type_0 = const()[name = tensor("hidden_states_533_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_533_pad_0 = const()[name = tensor("hidden_states_533_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5000675712)))]; + tensor up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001494976)))]; + tensor hidden_states_533_cast = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_13038, groups = var_12518, pad = hidden_states_533_pad_0, pad_type = hidden_states_533_pad_type_0, strides = var_13036, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_71_cast)[name = tensor("hidden_states_533_cast")]; + tensor var_13043 = const()[name = tensor("op_13043"), val = tensor([2, 640, 1, 4096])]; + tensor inputs_397_cast = reshape(shape = var_13043, x = hidden_states_533_cast)[name = tensor("inputs_397_cast")]; + tensor var_13053 = const()[name = tensor("op_13053"), val = tensor([1])]; + tensor channels_mean_397_cast = reduce_mean(axes = var_13053, keep_dims = var_12513, x = inputs_397_cast)[name = tensor("channels_mean_397_cast")]; + tensor zero_mean_397_cast = sub(x = inputs_397_cast, y = channels_mean_397_cast)[name = tensor("zero_mean_397_cast")]; + tensor zero_mean_sq_397_cast = mul(x = zero_mean_397_cast, y = zero_mean_397_cast)[name = tensor("zero_mean_sq_397_cast")]; + tensor var_13057 = const()[name = tensor("op_13057"), val = tensor([1])]; + tensor var_13058_cast = reduce_mean(axes = var_13057, keep_dims = var_12513, x = zero_mean_sq_397_cast)[name = tensor("op_13058_cast")]; + tensor var_13059_to_fp16 = const()[name = tensor("op_13059_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13060_cast = add(x = var_13058_cast, y = var_13059_to_fp16)[name = tensor("op_13060_cast")]; + tensor denom_397_epsilon_0_to_fp16 = const()[name = tensor("denom_397_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_397_cast = rsqrt(epsilon = denom_397_epsilon_0_to_fp16, x = var_13060_cast)[name = tensor("denom_397_cast")]; + tensor out_397_cast = mul(x = zero_mean_397_cast, y = denom_397_cast)[name = tensor("out_397_cast")]; + tensor var_13064_to_fp16 = const()[name = tensor("op_13064_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001496320)))]; + tensor var_13065_cast = add(x = out_397_cast, y = var_13064_to_fp16)[name = tensor("op_13065_cast")]; + tensor var_13067_to_fp16 = const()[name = tensor("op_13067_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001497664)))]; + tensor hidden_states_535_cast = mul(x = var_13065_cast, y = var_13067_to_fp16)[name = tensor("hidden_states_535_cast")]; + tensor var_13074 = const()[name = tensor("op_13074"), val = tensor([1, 1])]; + tensor var_13076 = const()[name = tensor("op_13076"), val = tensor([1, 1])]; + tensor q_265_pad_type_0 = const()[name = tensor("q_265_pad_type_0"), val = tensor("custom")]; + tensor q_265_pad_0 = const()[name = tensor("q_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001499008)))]; + tensor q_265_cast = conv(dilations = var_13076, groups = var_12518, pad = q_265_pad_0, pad_type = q_265_pad_type_0, strides = var_13074, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_535_cast)[name = tensor("q_265_cast")]; + tensor var_13080 = const()[name = tensor("op_13080"), val = tensor([1, 1])]; + tensor var_13082 = const()[name = tensor("op_13082"), val = tensor([1, 1])]; + tensor k_265_pad_type_0 = const()[name = tensor("k_265_pad_type_0"), val = tensor("custom")]; + tensor k_265_pad_0 = const()[name = tensor("k_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5002318272)))]; + tensor k_265_cast = conv(dilations = var_13082, groups = var_12518, pad = k_265_pad_0, pad_type = k_265_pad_type_0, strides = var_13080, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_535_cast)[name = tensor("k_265_cast")]; + tensor var_13086 = const()[name = tensor("op_13086"), val = tensor([1, 1])]; + tensor var_13088 = const()[name = tensor("op_13088"), val = tensor([1, 1])]; + tensor v_265_pad_type_0 = const()[name = tensor("v_265_pad_type_0"), val = tensor("custom")]; + tensor v_265_pad_0 = const()[name = tensor("v_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5003137536)))]; + tensor v_265_cast = conv(dilations = var_13088, groups = var_12518, pad = v_265_pad_0, pad_type = v_265_pad_type_0, strides = var_13086, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_535_cast)[name = tensor("v_265_cast")]; + tensor var_13092 = const()[name = tensor("op_13092"), val = tensor([2, 10, 64, -1])]; + tensor var_13093_cast = reshape(shape = var_13092, x = q_265_cast)[name = tensor("op_13093_cast")]; + tensor var_13094 = const()[name = tensor("op_13094"), val = tensor([2, 10, 64, -1])]; + tensor var_13095_cast = reshape(shape = var_13094, x = k_265_cast)[name = tensor("op_13095_cast")]; + tensor var_13096 = const()[name = tensor("op_13096"), val = tensor([2, 10, 64, -1])]; + tensor var_13097_cast = reshape(shape = var_13096, x = v_265_cast)[name = tensor("op_13097_cast")]; + tensor attn_weights_529_transpose_x_0 = const()[name = tensor("attn_weights_529_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_529_transpose_y_0 = const()[name = tensor("attn_weights_529_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_529_cast = matmul(transpose_x = attn_weights_529_transpose_x_0, transpose_y = attn_weights_529_transpose_y_0, x = var_13093_cast, y = var_13095_cast)[name = tensor("attn_weights_529_cast")]; + tensor attn_weights_531_cast = mul(x = attn_weights_529_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_531_cast")]; + tensor var_13101_cast = softmax(axis = var_12502, x = attn_weights_531_cast)[name = tensor("op_13101_cast")]; + tensor attn_265_transpose_x_0 = const()[name = tensor("attn_265_transpose_x_0"), val = tensor(false)]; + tensor attn_265_transpose_y_0 = const()[name = tensor("attn_265_transpose_y_0"), val = tensor(true)]; + tensor attn_265_cast = matmul(transpose_x = attn_265_transpose_x_0, transpose_y = attn_265_transpose_y_0, x = var_13097_cast, y = var_13101_cast)[name = tensor("attn_265_cast")]; + tensor var_13105 = const()[name = tensor("op_13105"), val = tensor([2, 640, 1, -1])]; + tensor input_771_cast = reshape(shape = var_13105, x = attn_265_cast)[name = tensor("input_771_cast")]; + tensor var_13110 = const()[name = tensor("op_13110"), val = tensor([1, 1])]; + tensor var_13112 = const()[name = tensor("op_13112"), val = tensor([1, 1])]; + tensor var_13114_pad_type_0 = const()[name = tensor("op_13114_pad_type_0"), val = tensor("custom")]; + tensor var_13114_pad_0 = const()[name = tensor("op_13114_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5003956800)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5004776064)))]; + tensor var_13114_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_13112, groups = var_12518, pad = var_13114_pad_0, pad_type = var_13114_pad_type_0, strides = var_13110, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_771_cast)[name = tensor("op_13114_cast")]; + tensor inputs_399_cast = add(x = var_13114_cast, y = inputs_397_cast)[name = tensor("inputs_399_cast")]; + tensor var_13118 = const()[name = tensor("op_13118"), val = tensor([1])]; + tensor channels_mean_399_cast = reduce_mean(axes = var_13118, keep_dims = var_12513, x = inputs_399_cast)[name = tensor("channels_mean_399_cast")]; + tensor zero_mean_399_cast = sub(x = inputs_399_cast, y = channels_mean_399_cast)[name = tensor("zero_mean_399_cast")]; + tensor zero_mean_sq_399_cast = mul(x = zero_mean_399_cast, y = zero_mean_399_cast)[name = tensor("zero_mean_sq_399_cast")]; + tensor var_13122 = const()[name = tensor("op_13122"), val = tensor([1])]; + tensor var_13123_cast = reduce_mean(axes = var_13122, keep_dims = var_12513, x = zero_mean_sq_399_cast)[name = tensor("op_13123_cast")]; + tensor var_13124_to_fp16 = const()[name = tensor("op_13124_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13125_cast = add(x = var_13123_cast, y = var_13124_to_fp16)[name = tensor("op_13125_cast")]; + tensor denom_399_epsilon_0_to_fp16 = const()[name = tensor("denom_399_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_399_cast = rsqrt(epsilon = denom_399_epsilon_0_to_fp16, x = var_13125_cast)[name = tensor("denom_399_cast")]; + tensor out_399_cast = mul(x = zero_mean_399_cast, y = denom_399_cast)[name = tensor("out_399_cast")]; + tensor var_13129_to_fp16 = const()[name = tensor("op_13129_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5004777408)))]; + tensor var_13130_cast = add(x = out_399_cast, y = var_13129_to_fp16)[name = tensor("op_13130_cast")]; + tensor var_13132_to_fp16 = const()[name = tensor("op_13132_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5004778752)))]; + tensor hidden_states_537_cast = mul(x = var_13130_cast, y = var_13132_to_fp16)[name = tensor("hidden_states_537_cast")]; + tensor var_13139 = const()[name = tensor("op_13139"), val = tensor([1, 1])]; + tensor var_13141 = const()[name = tensor("op_13141"), val = tensor([1, 1])]; + tensor q_267_pad_type_0 = const()[name = tensor("q_267_pad_type_0"), val = tensor("custom")]; + tensor q_267_pad_0 = const()[name = tensor("q_267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5004780096)))]; + tensor q_267_cast = conv(dilations = var_13141, groups = var_12518, pad = q_267_pad_0, pad_type = q_267_pad_type_0, strides = var_13139, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_537_cast)[name = tensor("q_267_cast")]; + tensor var_13145 = const()[name = tensor("op_13145"), val = tensor([1, 1])]; + tensor var_13147 = const()[name = tensor("op_13147"), val = tensor([1, 1])]; + tensor k_267_pad_type_0 = const()[name = tensor("k_267_pad_type_0"), val = tensor("custom")]; + tensor k_267_pad_0 = const()[name = tensor("k_267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5005599360)))]; + tensor k_267_cast = conv(dilations = var_13147, groups = var_12518, pad = k_267_pad_0, pad_type = k_267_pad_type_0, strides = var_13145, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_267_cast")]; + tensor var_13151 = const()[name = tensor("op_13151"), val = tensor([1, 1])]; + tensor var_13153 = const()[name = tensor("op_13153"), val = tensor([1, 1])]; + tensor v_267_pad_type_0 = const()[name = tensor("v_267_pad_type_0"), val = tensor("custom")]; + tensor v_267_pad_0 = const()[name = tensor("v_267_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5008220864)))]; + tensor v_267_cast = conv(dilations = var_13153, groups = var_12518, pad = v_267_pad_0, pad_type = v_267_pad_type_0, strides = var_13151, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_267_cast")]; + tensor var_13157 = const()[name = tensor("op_13157"), val = tensor([2, 10, 64, -1])]; + tensor var_13158_cast = reshape(shape = var_13157, x = q_267_cast)[name = tensor("op_13158_cast")]; + tensor var_13159 = const()[name = tensor("op_13159"), val = tensor([2, 10, 64, -1])]; + tensor var_13160_cast = reshape(shape = var_13159, x = k_267_cast)[name = tensor("op_13160_cast")]; + tensor var_13161 = const()[name = tensor("op_13161"), val = tensor([2, 10, 64, -1])]; + tensor var_13162_cast = reshape(shape = var_13161, x = v_267_cast)[name = tensor("op_13162_cast")]; + tensor attn_weights_533_transpose_x_0 = const()[name = tensor("attn_weights_533_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_533_transpose_y_0 = const()[name = tensor("attn_weights_533_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_533_cast = matmul(transpose_x = attn_weights_533_transpose_x_0, transpose_y = attn_weights_533_transpose_y_0, x = var_13158_cast, y = var_13160_cast)[name = tensor("attn_weights_533_cast")]; + tensor attn_weights_535_cast = mul(x = attn_weights_533_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_535_cast")]; + tensor var_13166_cast = softmax(axis = var_12502, x = attn_weights_535_cast)[name = tensor("op_13166_cast")]; + tensor attn_267_transpose_x_0 = const()[name = tensor("attn_267_transpose_x_0"), val = tensor(false)]; + tensor attn_267_transpose_y_0 = const()[name = tensor("attn_267_transpose_y_0"), val = tensor(true)]; + tensor attn_267_cast = matmul(transpose_x = attn_267_transpose_x_0, transpose_y = attn_267_transpose_y_0, x = var_13162_cast, y = var_13166_cast)[name = tensor("attn_267_cast")]; + tensor var_13170 = const()[name = tensor("op_13170"), val = tensor([2, 640, 1, -1])]; + tensor input_773_cast = reshape(shape = var_13170, x = attn_267_cast)[name = tensor("input_773_cast")]; + tensor var_13175 = const()[name = tensor("op_13175"), val = tensor([1, 1])]; + tensor var_13177 = const()[name = tensor("op_13177"), val = tensor([1, 1])]; + tensor var_13179_pad_type_0 = const()[name = tensor("op_13179_pad_type_0"), val = tensor("custom")]; + tensor var_13179_pad_0 = const()[name = tensor("op_13179_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5010842368)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5011661632)))]; + tensor var_13179_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_13177, groups = var_12518, pad = var_13179_pad_0, pad_type = var_13179_pad_type_0, strides = var_13175, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_773_cast)[name = tensor("op_13179_cast")]; + tensor inputs_401_cast = add(x = var_13179_cast, y = inputs_399_cast)[name = tensor("inputs_401_cast")]; + tensor var_13183 = const()[name = tensor("op_13183"), val = tensor([1])]; + tensor channels_mean_401_cast = reduce_mean(axes = var_13183, keep_dims = var_12513, x = inputs_401_cast)[name = tensor("channels_mean_401_cast")]; + tensor zero_mean_401_cast = sub(x = inputs_401_cast, y = channels_mean_401_cast)[name = tensor("zero_mean_401_cast")]; + tensor zero_mean_sq_401_cast = mul(x = zero_mean_401_cast, y = zero_mean_401_cast)[name = tensor("zero_mean_sq_401_cast")]; + tensor var_13187 = const()[name = tensor("op_13187"), val = tensor([1])]; + tensor var_13188_cast = reduce_mean(axes = var_13187, keep_dims = var_12513, x = zero_mean_sq_401_cast)[name = tensor("op_13188_cast")]; + tensor var_13189_to_fp16 = const()[name = tensor("op_13189_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13190_cast = add(x = var_13188_cast, y = var_13189_to_fp16)[name = tensor("op_13190_cast")]; + tensor denom_401_epsilon_0_to_fp16 = const()[name = tensor("denom_401_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_401_cast = rsqrt(epsilon = denom_401_epsilon_0_to_fp16, x = var_13190_cast)[name = tensor("denom_401_cast")]; + tensor out_401_cast = mul(x = zero_mean_401_cast, y = denom_401_cast)[name = tensor("out_401_cast")]; + tensor var_13194_to_fp16 = const()[name = tensor("op_13194_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5011662976)))]; + tensor var_13195_cast = add(x = out_401_cast, y = var_13194_to_fp16)[name = tensor("op_13195_cast")]; + tensor var_13197_to_fp16 = const()[name = tensor("op_13197_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5011664320)))]; + tensor input_775_cast = mul(x = var_13195_cast, y = var_13197_to_fp16)[name = tensor("input_775_cast")]; + tensor var_13205 = const()[name = tensor("op_13205"), val = tensor([1, 1])]; + tensor var_13207 = const()[name = tensor("op_13207"), val = tensor([1, 1])]; + tensor var_13209_pad_type_0 = const()[name = tensor("op_13209_pad_type_0"), val = tensor("custom")]; + tensor var_13209_pad_0 = const()[name = tensor("op_13209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5011665664)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5018219328)))]; + tensor var_13209_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_13207, groups = var_12518, pad = var_13209_pad_0, pad_type = var_13209_pad_type_0, strides = var_13205, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_775_cast)[name = tensor("op_13209_cast")]; + tensor var_13210_split_sizes_0 = const()[name = tensor("op_13210_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13210_axis_0 = const()[name = tensor("op_13210_axis_0"), val = tensor(1)]; + tensor var_13210_cast_0, tensor var_13210_cast_1 = split(axis = var_13210_axis_0, split_sizes = var_13210_split_sizes_0, x = var_13209_cast)[name = tensor("op_13210_cast")]; + tensor var_13212_mode_0 = const()[name = tensor("op_13212_mode_0"), val = tensor("EXACT")]; + tensor var_13212_cast = gelu(mode = var_13212_mode_0, x = var_13210_cast_1)[name = tensor("op_13212_cast")]; + tensor input_777_cast = mul(x = var_13210_cast_0, y = var_13212_cast)[name = tensor("input_777_cast")]; + tensor var_13216 = const()[name = tensor("op_13216"), val = tensor([1, 1])]; + tensor var_13218 = const()[name = tensor("op_13218"), val = tensor([1, 1])]; + tensor var_13220_pad_type_0 = const()[name = tensor("op_13220_pad_type_0"), val = tensor("custom")]; + tensor var_13220_pad_0 = const()[name = tensor("op_13220_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5018229632)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5021506496)))]; + tensor var_13220_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_13218, groups = var_12518, pad = var_13220_pad_0, pad_type = var_13220_pad_type_0, strides = var_13216, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_777_cast)[name = tensor("op_13220_cast")]; + tensor inputs_403_cast = add(x = var_13220_cast, y = inputs_401_cast)[name = tensor("inputs_403_cast")]; + tensor var_13230 = const()[name = tensor("op_13230"), val = tensor([1])]; + tensor channels_mean_403_cast = reduce_mean(axes = var_13230, keep_dims = var_12513, x = inputs_403_cast)[name = tensor("channels_mean_403_cast")]; + tensor zero_mean_403_cast = sub(x = inputs_403_cast, y = channels_mean_403_cast)[name = tensor("zero_mean_403_cast")]; + tensor zero_mean_sq_403_cast = mul(x = zero_mean_403_cast, y = zero_mean_403_cast)[name = tensor("zero_mean_sq_403_cast")]; + tensor var_13234 = const()[name = tensor("op_13234"), val = tensor([1])]; + tensor var_13235_cast = reduce_mean(axes = var_13234, keep_dims = var_12513, x = zero_mean_sq_403_cast)[name = tensor("op_13235_cast")]; + tensor var_13236_to_fp16 = const()[name = tensor("op_13236_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13237_cast = add(x = var_13235_cast, y = var_13236_to_fp16)[name = tensor("op_13237_cast")]; + tensor denom_403_epsilon_0_to_fp16 = const()[name = tensor("denom_403_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_403_cast = rsqrt(epsilon = denom_403_epsilon_0_to_fp16, x = var_13237_cast)[name = tensor("denom_403_cast")]; + tensor out_403_cast = mul(x = zero_mean_403_cast, y = denom_403_cast)[name = tensor("out_403_cast")]; + tensor var_13241_to_fp16 = const()[name = tensor("op_13241_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5021507840)))]; + tensor var_13242_cast = add(x = out_403_cast, y = var_13241_to_fp16)[name = tensor("op_13242_cast")]; + tensor var_13244_to_fp16 = const()[name = tensor("op_13244_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5021509184)))]; + tensor hidden_states_541_cast = mul(x = var_13242_cast, y = var_13244_to_fp16)[name = tensor("hidden_states_541_cast")]; + tensor var_13251 = const()[name = tensor("op_13251"), val = tensor([1, 1])]; + tensor var_13253 = const()[name = tensor("op_13253"), val = tensor([1, 1])]; + tensor q_269_pad_type_0 = const()[name = tensor("q_269_pad_type_0"), val = tensor("custom")]; + tensor q_269_pad_0 = const()[name = tensor("q_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5021510528)))]; + tensor q_269_cast = conv(dilations = var_13253, groups = var_12518, pad = q_269_pad_0, pad_type = q_269_pad_type_0, strides = var_13251, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_541_cast)[name = tensor("q_269_cast")]; + tensor var_13257 = const()[name = tensor("op_13257"), val = tensor([1, 1])]; + tensor var_13259 = const()[name = tensor("op_13259"), val = tensor([1, 1])]; + tensor k_269_pad_type_0 = const()[name = tensor("k_269_pad_type_0"), val = tensor("custom")]; + tensor k_269_pad_0 = const()[name = tensor("k_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5022329792)))]; + tensor k_269_cast = conv(dilations = var_13259, groups = var_12518, pad = k_269_pad_0, pad_type = k_269_pad_type_0, strides = var_13257, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_541_cast)[name = tensor("k_269_cast")]; + tensor var_13263 = const()[name = tensor("op_13263"), val = tensor([1, 1])]; + tensor var_13265 = const()[name = tensor("op_13265"), val = tensor([1, 1])]; + tensor v_269_pad_type_0 = const()[name = tensor("v_269_pad_type_0"), val = tensor("custom")]; + tensor v_269_pad_0 = const()[name = tensor("v_269_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5023149056)))]; + tensor v_269_cast = conv(dilations = var_13265, groups = var_12518, pad = v_269_pad_0, pad_type = v_269_pad_type_0, strides = var_13263, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_541_cast)[name = tensor("v_269_cast")]; + tensor var_13269 = const()[name = tensor("op_13269"), val = tensor([2, 10, 64, -1])]; + tensor var_13270_cast = reshape(shape = var_13269, x = q_269_cast)[name = tensor("op_13270_cast")]; + tensor var_13271 = const()[name = tensor("op_13271"), val = tensor([2, 10, 64, -1])]; + tensor var_13272_cast = reshape(shape = var_13271, x = k_269_cast)[name = tensor("op_13272_cast")]; + tensor var_13273 = const()[name = tensor("op_13273"), val = tensor([2, 10, 64, -1])]; + tensor var_13274_cast = reshape(shape = var_13273, x = v_269_cast)[name = tensor("op_13274_cast")]; + tensor attn_weights_537_transpose_x_0 = const()[name = tensor("attn_weights_537_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_537_transpose_y_0 = const()[name = tensor("attn_weights_537_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_537_cast = matmul(transpose_x = attn_weights_537_transpose_x_0, transpose_y = attn_weights_537_transpose_y_0, x = var_13270_cast, y = var_13272_cast)[name = tensor("attn_weights_537_cast")]; + tensor attn_weights_539_cast = mul(x = attn_weights_537_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_539_cast")]; + tensor var_13278_cast = softmax(axis = var_12502, x = attn_weights_539_cast)[name = tensor("op_13278_cast")]; + tensor attn_269_transpose_x_0 = const()[name = tensor("attn_269_transpose_x_0"), val = tensor(false)]; + tensor attn_269_transpose_y_0 = const()[name = tensor("attn_269_transpose_y_0"), val = tensor(true)]; + tensor attn_269_cast = matmul(transpose_x = attn_269_transpose_x_0, transpose_y = attn_269_transpose_y_0, x = var_13274_cast, y = var_13278_cast)[name = tensor("attn_269_cast")]; + tensor var_13282 = const()[name = tensor("op_13282"), val = tensor([2, 640, 1, -1])]; + tensor input_779_cast = reshape(shape = var_13282, x = attn_269_cast)[name = tensor("input_779_cast")]; + tensor var_13287 = const()[name = tensor("op_13287"), val = tensor([1, 1])]; + tensor var_13289 = const()[name = tensor("op_13289"), val = tensor([1, 1])]; + tensor var_13291_pad_type_0 = const()[name = tensor("op_13291_pad_type_0"), val = tensor("custom")]; + tensor var_13291_pad_0 = const()[name = tensor("op_13291_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5023968320)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5024787584)))]; + tensor var_13291_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_13289, groups = var_12518, pad = var_13291_pad_0, pad_type = var_13291_pad_type_0, strides = var_13287, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_779_cast)[name = tensor("op_13291_cast")]; + tensor inputs_405_cast = add(x = var_13291_cast, y = inputs_403_cast)[name = tensor("inputs_405_cast")]; + tensor var_13295 = const()[name = tensor("op_13295"), val = tensor([1])]; + tensor channels_mean_405_cast = reduce_mean(axes = var_13295, keep_dims = var_12513, x = inputs_405_cast)[name = tensor("channels_mean_405_cast")]; + tensor zero_mean_405_cast = sub(x = inputs_405_cast, y = channels_mean_405_cast)[name = tensor("zero_mean_405_cast")]; + tensor zero_mean_sq_405_cast = mul(x = zero_mean_405_cast, y = zero_mean_405_cast)[name = tensor("zero_mean_sq_405_cast")]; + tensor var_13299 = const()[name = tensor("op_13299"), val = tensor([1])]; + tensor var_13300_cast = reduce_mean(axes = var_13299, keep_dims = var_12513, x = zero_mean_sq_405_cast)[name = tensor("op_13300_cast")]; + tensor var_13301_to_fp16 = const()[name = tensor("op_13301_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13302_cast = add(x = var_13300_cast, y = var_13301_to_fp16)[name = tensor("op_13302_cast")]; + tensor denom_405_epsilon_0_to_fp16 = const()[name = tensor("denom_405_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_405_cast = rsqrt(epsilon = denom_405_epsilon_0_to_fp16, x = var_13302_cast)[name = tensor("denom_405_cast")]; + tensor out_405_cast = mul(x = zero_mean_405_cast, y = denom_405_cast)[name = tensor("out_405_cast")]; + tensor var_13306_to_fp16 = const()[name = tensor("op_13306_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5024788928)))]; + tensor var_13307_cast = add(x = out_405_cast, y = var_13306_to_fp16)[name = tensor("op_13307_cast")]; + tensor var_13309_to_fp16 = const()[name = tensor("op_13309_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5024790272)))]; + tensor hidden_states_543_cast = mul(x = var_13307_cast, y = var_13309_to_fp16)[name = tensor("hidden_states_543_cast")]; + tensor var_13316 = const()[name = tensor("op_13316"), val = tensor([1, 1])]; + tensor var_13318 = const()[name = tensor("op_13318"), val = tensor([1, 1])]; + tensor q_271_pad_type_0 = const()[name = tensor("q_271_pad_type_0"), val = tensor("custom")]; + tensor q_271_pad_0 = const()[name = tensor("q_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5024791616)))]; + tensor q_271_cast = conv(dilations = var_13318, groups = var_12518, pad = q_271_pad_0, pad_type = q_271_pad_type_0, strides = var_13316, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_543_cast)[name = tensor("q_271_cast")]; + tensor var_13322 = const()[name = tensor("op_13322"), val = tensor([1, 1])]; + tensor var_13324 = const()[name = tensor("op_13324"), val = tensor([1, 1])]; + tensor k_271_pad_type_0 = const()[name = tensor("k_271_pad_type_0"), val = tensor("custom")]; + tensor k_271_pad_0 = const()[name = tensor("k_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5025610880)))]; + tensor k_271_cast = conv(dilations = var_13324, groups = var_12518, pad = k_271_pad_0, pad_type = k_271_pad_type_0, strides = var_13322, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_271_cast")]; + tensor var_13328 = const()[name = tensor("op_13328"), val = tensor([1, 1])]; + tensor var_13330 = const()[name = tensor("op_13330"), val = tensor([1, 1])]; + tensor v_271_pad_type_0 = const()[name = tensor("v_271_pad_type_0"), val = tensor("custom")]; + tensor v_271_pad_0 = const()[name = tensor("v_271_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5028232384)))]; + tensor v_271_cast = conv(dilations = var_13330, groups = var_12518, pad = v_271_pad_0, pad_type = v_271_pad_type_0, strides = var_13328, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_271_cast")]; + tensor var_13334 = const()[name = tensor("op_13334"), val = tensor([2, 10, 64, -1])]; + tensor var_13335_cast = reshape(shape = var_13334, x = q_271_cast)[name = tensor("op_13335_cast")]; + tensor var_13336 = const()[name = tensor("op_13336"), val = tensor([2, 10, 64, -1])]; + tensor var_13337_cast = reshape(shape = var_13336, x = k_271_cast)[name = tensor("op_13337_cast")]; + tensor var_13338 = const()[name = tensor("op_13338"), val = tensor([2, 10, 64, -1])]; + tensor var_13339_cast = reshape(shape = var_13338, x = v_271_cast)[name = tensor("op_13339_cast")]; + tensor attn_weights_541_transpose_x_0 = const()[name = tensor("attn_weights_541_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_541_transpose_y_0 = const()[name = tensor("attn_weights_541_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_541_cast = matmul(transpose_x = attn_weights_541_transpose_x_0, transpose_y = attn_weights_541_transpose_y_0, x = var_13335_cast, y = var_13337_cast)[name = tensor("attn_weights_541_cast")]; + tensor attn_weights_543_cast = mul(x = attn_weights_541_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_543_cast")]; + tensor var_13343_cast = softmax(axis = var_12502, x = attn_weights_543_cast)[name = tensor("op_13343_cast")]; + tensor attn_271_transpose_x_0 = const()[name = tensor("attn_271_transpose_x_0"), val = tensor(false)]; + tensor attn_271_transpose_y_0 = const()[name = tensor("attn_271_transpose_y_0"), val = tensor(true)]; + tensor attn_271_cast = matmul(transpose_x = attn_271_transpose_x_0, transpose_y = attn_271_transpose_y_0, x = var_13339_cast, y = var_13343_cast)[name = tensor("attn_271_cast")]; + tensor var_13347 = const()[name = tensor("op_13347"), val = tensor([2, 640, 1, -1])]; + tensor input_781_cast = reshape(shape = var_13347, x = attn_271_cast)[name = tensor("input_781_cast")]; + tensor var_13352 = const()[name = tensor("op_13352"), val = tensor([1, 1])]; + tensor var_13354 = const()[name = tensor("op_13354"), val = tensor([1, 1])]; + tensor var_13356_pad_type_0 = const()[name = tensor("op_13356_pad_type_0"), val = tensor("custom")]; + tensor var_13356_pad_0 = const()[name = tensor("op_13356_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5030853888)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5031673152)))]; + tensor var_13356_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_13354, groups = var_12518, pad = var_13356_pad_0, pad_type = var_13356_pad_type_0, strides = var_13352, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_781_cast)[name = tensor("op_13356_cast")]; + tensor inputs_407_cast = add(x = var_13356_cast, y = inputs_405_cast)[name = tensor("inputs_407_cast")]; + tensor var_13360 = const()[name = tensor("op_13360"), val = tensor([1])]; + tensor channels_mean_407_cast = reduce_mean(axes = var_13360, keep_dims = var_12513, x = inputs_407_cast)[name = tensor("channels_mean_407_cast")]; + tensor zero_mean_407_cast = sub(x = inputs_407_cast, y = channels_mean_407_cast)[name = tensor("zero_mean_407_cast")]; + tensor zero_mean_sq_407_cast = mul(x = zero_mean_407_cast, y = zero_mean_407_cast)[name = tensor("zero_mean_sq_407_cast")]; + tensor var_13364 = const()[name = tensor("op_13364"), val = tensor([1])]; + tensor var_13365_cast = reduce_mean(axes = var_13364, keep_dims = var_12513, x = zero_mean_sq_407_cast)[name = tensor("op_13365_cast")]; + tensor var_13366_to_fp16 = const()[name = tensor("op_13366_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13367_cast = add(x = var_13365_cast, y = var_13366_to_fp16)[name = tensor("op_13367_cast")]; + tensor denom_407_epsilon_0_to_fp16 = const()[name = tensor("denom_407_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_407_cast = rsqrt(epsilon = denom_407_epsilon_0_to_fp16, x = var_13367_cast)[name = tensor("denom_407_cast")]; + tensor out_407_cast = mul(x = zero_mean_407_cast, y = denom_407_cast)[name = tensor("out_407_cast")]; + tensor var_13371_to_fp16 = const()[name = tensor("op_13371_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5031674496)))]; + tensor var_13372_cast = add(x = out_407_cast, y = var_13371_to_fp16)[name = tensor("op_13372_cast")]; + tensor var_13374_to_fp16 = const()[name = tensor("op_13374_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5031675840)))]; + tensor input_783_cast = mul(x = var_13372_cast, y = var_13374_to_fp16)[name = tensor("input_783_cast")]; + tensor var_13382 = const()[name = tensor("op_13382"), val = tensor([1, 1])]; + tensor var_13384 = const()[name = tensor("op_13384"), val = tensor([1, 1])]; + tensor var_13386_pad_type_0 = const()[name = tensor("op_13386_pad_type_0"), val = tensor("custom")]; + tensor var_13386_pad_0 = const()[name = tensor("op_13386_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5031677184)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5038230848)))]; + tensor var_13386_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_13384, groups = var_12518, pad = var_13386_pad_0, pad_type = var_13386_pad_type_0, strides = var_13382, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_783_cast)[name = tensor("op_13386_cast")]; + tensor var_13387_split_sizes_0 = const()[name = tensor("op_13387_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13387_axis_0 = const()[name = tensor("op_13387_axis_0"), val = tensor(1)]; + tensor var_13387_cast_0, tensor var_13387_cast_1 = split(axis = var_13387_axis_0, split_sizes = var_13387_split_sizes_0, x = var_13386_cast)[name = tensor("op_13387_cast")]; + tensor var_13389_mode_0 = const()[name = tensor("op_13389_mode_0"), val = tensor("EXACT")]; + tensor var_13389_cast = gelu(mode = var_13389_mode_0, x = var_13387_cast_1)[name = tensor("op_13389_cast")]; + tensor input_785_cast = mul(x = var_13387_cast_0, y = var_13389_cast)[name = tensor("input_785_cast")]; + tensor var_13393 = const()[name = tensor("op_13393"), val = tensor([1, 1])]; + tensor var_13395 = const()[name = tensor("op_13395"), val = tensor([1, 1])]; + tensor var_13397_pad_type_0 = const()[name = tensor("op_13397_pad_type_0"), val = tensor("custom")]; + tensor var_13397_pad_0 = const()[name = tensor("op_13397_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5038241152)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5041518016)))]; + tensor var_13397_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_13395, groups = var_12518, pad = var_13397_pad_0, pad_type = var_13397_pad_type_0, strides = var_13393, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_785_cast)[name = tensor("op_13397_cast")]; + tensor hidden_states_547_cast = add(x = var_13397_cast, y = inputs_407_cast)[name = tensor("hidden_states_547_cast")]; + tensor var_13399 = const()[name = tensor("op_13399"), val = tensor([2, 640, 64, 64])]; + tensor input_787_cast = reshape(shape = var_13399, x = hidden_states_547_cast)[name = tensor("input_787_cast")]; + tensor var_13403 = const()[name = tensor("op_13403"), val = tensor([1, 1])]; + tensor var_13405 = const()[name = tensor("op_13405"), val = tensor([1, 1])]; + tensor hidden_states_549_pad_type_0 = const()[name = tensor("hidden_states_549_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_549_pad_0 = const()[name = tensor("hidden_states_549_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5041519360)))]; + tensor up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042338624)))]; + tensor hidden_states_549_cast = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_13405, groups = var_12518, pad = hidden_states_549_pad_0, pad_type = hidden_states_549_pad_type_0, strides = var_13403, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_787_cast)[name = tensor("hidden_states_549_cast")]; + tensor hidden_states_551_cast = add(x = hidden_states_549_cast, y = hidden_states_531_cast)[name = tensor("hidden_states_551_cast")]; + tensor input_789_interleave_0 = const()[name = tensor("input_789_interleave_0"), val = tensor(false)]; + tensor input_789_cast = concat(axis = var_12518, interleave = input_789_interleave_0, values = (hidden_states_551_cast, input_45_cast))[name = tensor("input_789_cast")]; + tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([2, 32, 30, 64, 64])]; + tensor reshape_144_cast = reshape(shape = reshape_144_shape_0, x = input_789_cast)[name = tensor("reshape_144_cast")]; + tensor reduce_mean_108_axes_0 = const()[name = tensor("reduce_mean_108_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_108_keep_dims_0 = const()[name = tensor("reduce_mean_108_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_108_cast = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast)[name = tensor("reduce_mean_108_cast")]; + tensor sub_72_cast = sub(x = reshape_144_cast, y = reduce_mean_108_cast)[name = tensor("sub_72_cast")]; + tensor square_36_cast = square(x = sub_72_cast)[name = tensor("square_36_cast")]; + tensor reduce_mean_110_axes_0 = const()[name = tensor("reduce_mean_110_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_110_keep_dims_0 = const()[name = tensor("reduce_mean_110_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_110_cast = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast)[name = tensor("reduce_mean_110_cast")]; + tensor add_72_y_0_to_fp16 = const()[name = tensor("add_72_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_72_cast = add(x = reduce_mean_110_cast, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast")]; + tensor sqrt_36_cast = sqrt(x = add_72_cast)[name = tensor("sqrt_36_cast")]; + tensor real_div_36_cast = real_div(x = sub_72_cast, y = sqrt_36_cast)[name = tensor("real_div_36_cast")]; + tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([2, 960, 64, 64])]; + tensor reshape_145_cast = reshape(shape = reshape_145_shape_0, x = real_div_36_cast)[name = tensor("reshape_145_cast")]; + tensor add_73_mean_0_to_fp16 = const()[name = tensor("add_73_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042339968)))]; + tensor add_73_variance_0_to_fp16 = const()[name = tensor("add_73_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042341952)))]; + tensor add_73_gamma_0_to_fp16 = const()[name = tensor("add_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042343936)))]; + tensor add_73_beta_0_to_fp16 = const()[name = tensor("add_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042345920)))]; + tensor add_73_epsilon_0_to_fp16 = const()[name = tensor("add_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_73_cast = batch_norm(beta = add_73_beta_0_to_fp16, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16, mean = add_73_mean_0_to_fp16, variance = add_73_variance_0_to_fp16, x = reshape_145_cast)[name = tensor("add_73_cast")]; + tensor input_793_cast = silu(x = add_73_cast)[name = tensor("input_793_cast")]; + tensor var_13423 = const()[name = tensor("op_13423"), val = tensor([1, 1])]; + tensor var_13425 = const()[name = tensor("op_13425"), val = tensor([1, 1])]; + tensor hidden_states_553_pad_type_0 = const()[name = tensor("hidden_states_553_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_553_pad_0 = const()[name = tensor("hidden_states_553_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042347904)))]; + tensor up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5053407168)))]; + tensor hidden_states_553_cast = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_13425, groups = var_12518, pad = hidden_states_553_pad_0, pad_type = hidden_states_553_pad_type_0, strides = var_13423, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_793_cast)[name = tensor("hidden_states_553_cast")]; + tensor var_13431 = const()[name = tensor("op_13431"), val = tensor([1, 1])]; + tensor var_13433 = const()[name = tensor("op_13433"), val = tensor([1, 1])]; + tensor temb_27_pad_type_0 = const()[name = tensor("temb_27_pad_type_0"), val = tensor("custom")]; + tensor temb_27_pad_0 = const()[name = tensor("temb_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5053408512)))]; + tensor up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5055046976)))]; + tensor temb_27_cast = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_13433, groups = var_12518, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_13431, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_27_cast")]; + tensor input_797_cast = add(x = hidden_states_553_cast, y = temb_27_cast)[name = tensor("input_797_cast")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_148_cast = reshape(shape = reshape_148_shape_0, x = input_797_cast)[name = tensor("reshape_148_cast")]; + tensor reduce_mean_111_axes_0 = const()[name = tensor("reduce_mean_111_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_111_keep_dims_0 = const()[name = tensor("reduce_mean_111_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_111_cast = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast)[name = tensor("reduce_mean_111_cast")]; + tensor sub_74_cast = sub(x = reshape_148_cast, y = reduce_mean_111_cast)[name = tensor("sub_74_cast")]; + tensor square_37_cast = square(x = sub_74_cast)[name = tensor("square_37_cast")]; + tensor reduce_mean_113_axes_0 = const()[name = tensor("reduce_mean_113_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_113_keep_dims_0 = const()[name = tensor("reduce_mean_113_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_113_cast = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast)[name = tensor("reduce_mean_113_cast")]; + tensor add_74_y_0_to_fp16 = const()[name = tensor("add_74_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_74_cast = add(x = reduce_mean_113_cast, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast")]; + tensor sqrt_37_cast = sqrt(x = add_74_cast)[name = tensor("sqrt_37_cast")]; + tensor real_div_37_cast = real_div(x = sub_74_cast, y = sqrt_37_cast)[name = tensor("real_div_37_cast")]; + tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_149_cast = reshape(shape = reshape_149_shape_0, x = real_div_37_cast)[name = tensor("reshape_149_cast")]; + tensor add_75_gamma_0_to_fp16 = const()[name = tensor("add_75_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5055048320)))]; + tensor add_75_beta_0_to_fp16 = const()[name = tensor("add_75_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5055049664)))]; + tensor add_75_epsilon_0_to_fp16 = const()[name = tensor("add_75_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_75_cast = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_149_cast)[name = tensor("add_75_cast")]; + tensor input_801_cast = silu(x = add_75_cast)[name = tensor("input_801_cast")]; + tensor var_13443 = const()[name = tensor("op_13443"), val = tensor([1, 1])]; + tensor var_13445 = const()[name = tensor("op_13445"), val = tensor([1, 1])]; + tensor hidden_states_555_pad_type_0 = const()[name = tensor("hidden_states_555_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_555_pad_0 = const()[name = tensor("hidden_states_555_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5055051008)))]; + tensor up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5062423872)))]; + tensor hidden_states_555_cast = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_13445, groups = var_12518, pad = hidden_states_555_pad_0, pad_type = hidden_states_555_pad_type_0, strides = var_13443, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_801_cast)[name = tensor("hidden_states_555_cast")]; + tensor var_13450 = const()[name = tensor("op_13450"), val = tensor([1, 1])]; + tensor var_13452 = const()[name = tensor("op_13452"), val = tensor([1, 1])]; + tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("custom")]; + tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5062425216)))]; + tensor up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5063654080)))]; + tensor x_15_cast = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_13452, groups = var_12518, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_13450, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16, x = input_789_cast)[name = tensor("x_15_cast")]; + tensor hidden_states_557_cast = add(x = x_15_cast, y = hidden_states_555_cast)[name = tensor("hidden_states_557_cast")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_152_cast = reshape(shape = reshape_152_shape_0, x = hidden_states_557_cast)[name = tensor("reshape_152_cast")]; + tensor reduce_mean_114_axes_0 = const()[name = tensor("reduce_mean_114_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_114_keep_dims_0 = const()[name = tensor("reduce_mean_114_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_114_cast = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast)[name = tensor("reduce_mean_114_cast")]; + tensor sub_76_cast = sub(x = reshape_152_cast, y = reduce_mean_114_cast)[name = tensor("sub_76_cast")]; + tensor square_38_cast = square(x = sub_76_cast)[name = tensor("square_38_cast")]; + tensor reduce_mean_116_axes_0 = const()[name = tensor("reduce_mean_116_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_116_keep_dims_0 = const()[name = tensor("reduce_mean_116_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_116_cast = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast)[name = tensor("reduce_mean_116_cast")]; + tensor add_76_y_0_to_fp16 = const()[name = tensor("add_76_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_76_cast = add(x = reduce_mean_116_cast, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast")]; + tensor sqrt_38_cast = sqrt(x = add_76_cast)[name = tensor("sqrt_38_cast")]; + tensor real_div_38_cast = real_div(x = sub_76_cast, y = sqrt_38_cast)[name = tensor("real_div_38_cast")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_153_cast = reshape(shape = reshape_153_shape_0, x = real_div_38_cast)[name = tensor("reshape_153_cast")]; + tensor add_77_gamma_0_to_fp16 = const()[name = tensor("add_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5063655424)))]; + tensor add_77_beta_0_to_fp16 = const()[name = tensor("add_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5063656768)))]; + tensor add_77_epsilon_0_to_fp16 = const()[name = tensor("add_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_77_cast = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_153_cast)[name = tensor("add_77_cast")]; + tensor var_13474 = const()[name = tensor("op_13474"), val = tensor([1, 1])]; + tensor var_13476 = const()[name = tensor("op_13476"), val = tensor([1, 1])]; + tensor hidden_states_559_pad_type_0 = const()[name = tensor("hidden_states_559_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_559_pad_0 = const()[name = tensor("hidden_states_559_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5063658112)))]; + tensor up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5064477376)))]; + tensor hidden_states_559_cast = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_13476, groups = var_12518, pad = hidden_states_559_pad_0, pad_type = hidden_states_559_pad_type_0, strides = var_13474, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16, x = add_77_cast)[name = tensor("hidden_states_559_cast")]; + tensor var_13481 = const()[name = tensor("op_13481"), val = tensor([2, 640, 1, 4096])]; + tensor inputs_409_cast = reshape(shape = var_13481, x = hidden_states_559_cast)[name = tensor("inputs_409_cast")]; + tensor var_13491 = const()[name = tensor("op_13491"), val = tensor([1])]; + tensor channels_mean_409_cast = reduce_mean(axes = var_13491, keep_dims = var_12513, x = inputs_409_cast)[name = tensor("channels_mean_409_cast")]; + tensor zero_mean_409_cast = sub(x = inputs_409_cast, y = channels_mean_409_cast)[name = tensor("zero_mean_409_cast")]; + tensor zero_mean_sq_409_cast = mul(x = zero_mean_409_cast, y = zero_mean_409_cast)[name = tensor("zero_mean_sq_409_cast")]; + tensor var_13495 = const()[name = tensor("op_13495"), val = tensor([1])]; + tensor var_13496_cast = reduce_mean(axes = var_13495, keep_dims = var_12513, x = zero_mean_sq_409_cast)[name = tensor("op_13496_cast")]; + tensor var_13497_to_fp16 = const()[name = tensor("op_13497_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13498_cast = add(x = var_13496_cast, y = var_13497_to_fp16)[name = tensor("op_13498_cast")]; + tensor denom_409_epsilon_0_to_fp16 = const()[name = tensor("denom_409_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_409_cast = rsqrt(epsilon = denom_409_epsilon_0_to_fp16, x = var_13498_cast)[name = tensor("denom_409_cast")]; + tensor out_409_cast = mul(x = zero_mean_409_cast, y = denom_409_cast)[name = tensor("out_409_cast")]; + tensor var_13502_to_fp16 = const()[name = tensor("op_13502_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5064478720)))]; + tensor var_13503_cast = add(x = out_409_cast, y = var_13502_to_fp16)[name = tensor("op_13503_cast")]; + tensor var_13505_to_fp16 = const()[name = tensor("op_13505_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5064480064)))]; + tensor hidden_states_561_cast = mul(x = var_13503_cast, y = var_13505_to_fp16)[name = tensor("hidden_states_561_cast")]; + tensor var_13512 = const()[name = tensor("op_13512"), val = tensor([1, 1])]; + tensor var_13514 = const()[name = tensor("op_13514"), val = tensor([1, 1])]; + tensor q_273_pad_type_0 = const()[name = tensor("q_273_pad_type_0"), val = tensor("custom")]; + tensor q_273_pad_0 = const()[name = tensor("q_273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5064481408)))]; + tensor q_273_cast = conv(dilations = var_13514, groups = var_12518, pad = q_273_pad_0, pad_type = q_273_pad_type_0, strides = var_13512, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_561_cast)[name = tensor("q_273_cast")]; + tensor var_13518 = const()[name = tensor("op_13518"), val = tensor([1, 1])]; + tensor var_13520 = const()[name = tensor("op_13520"), val = tensor([1, 1])]; + tensor k_273_pad_type_0 = const()[name = tensor("k_273_pad_type_0"), val = tensor("custom")]; + tensor k_273_pad_0 = const()[name = tensor("k_273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5065300672)))]; + tensor k_273_cast = conv(dilations = var_13520, groups = var_12518, pad = k_273_pad_0, pad_type = k_273_pad_type_0, strides = var_13518, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_561_cast)[name = tensor("k_273_cast")]; + tensor var_13524 = const()[name = tensor("op_13524"), val = tensor([1, 1])]; + tensor var_13526 = const()[name = tensor("op_13526"), val = tensor([1, 1])]; + tensor v_273_pad_type_0 = const()[name = tensor("v_273_pad_type_0"), val = tensor("custom")]; + tensor v_273_pad_0 = const()[name = tensor("v_273_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5066119936)))]; + tensor v_273_cast = conv(dilations = var_13526, groups = var_12518, pad = v_273_pad_0, pad_type = v_273_pad_type_0, strides = var_13524, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_561_cast)[name = tensor("v_273_cast")]; + tensor var_13530 = const()[name = tensor("op_13530"), val = tensor([2, 10, 64, -1])]; + tensor var_13531_cast = reshape(shape = var_13530, x = q_273_cast)[name = tensor("op_13531_cast")]; + tensor var_13532 = const()[name = tensor("op_13532"), val = tensor([2, 10, 64, -1])]; + tensor var_13533_cast = reshape(shape = var_13532, x = k_273_cast)[name = tensor("op_13533_cast")]; + tensor var_13534 = const()[name = tensor("op_13534"), val = tensor([2, 10, 64, -1])]; + tensor var_13535_cast = reshape(shape = var_13534, x = v_273_cast)[name = tensor("op_13535_cast")]; + tensor attn_weights_545_transpose_x_0 = const()[name = tensor("attn_weights_545_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_545_transpose_y_0 = const()[name = tensor("attn_weights_545_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_545_cast = matmul(transpose_x = attn_weights_545_transpose_x_0, transpose_y = attn_weights_545_transpose_y_0, x = var_13531_cast, y = var_13533_cast)[name = tensor("attn_weights_545_cast")]; + tensor attn_weights_547_cast = mul(x = attn_weights_545_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_547_cast")]; + tensor var_13539_cast = softmax(axis = var_12502, x = attn_weights_547_cast)[name = tensor("op_13539_cast")]; + tensor attn_273_transpose_x_0 = const()[name = tensor("attn_273_transpose_x_0"), val = tensor(false)]; + tensor attn_273_transpose_y_0 = const()[name = tensor("attn_273_transpose_y_0"), val = tensor(true)]; + tensor attn_273_cast = matmul(transpose_x = attn_273_transpose_x_0, transpose_y = attn_273_transpose_y_0, x = var_13535_cast, y = var_13539_cast)[name = tensor("attn_273_cast")]; + tensor var_13543 = const()[name = tensor("op_13543"), val = tensor([2, 640, 1, -1])]; + tensor input_805_cast = reshape(shape = var_13543, x = attn_273_cast)[name = tensor("input_805_cast")]; + tensor var_13548 = const()[name = tensor("op_13548"), val = tensor([1, 1])]; + tensor var_13550 = const()[name = tensor("op_13550"), val = tensor([1, 1])]; + tensor var_13552_pad_type_0 = const()[name = tensor("op_13552_pad_type_0"), val = tensor("custom")]; + tensor var_13552_pad_0 = const()[name = tensor("op_13552_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5066939200)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5067758464)))]; + tensor var_13552_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_13550, groups = var_12518, pad = var_13552_pad_0, pad_type = var_13552_pad_type_0, strides = var_13548, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_805_cast)[name = tensor("op_13552_cast")]; + tensor inputs_411_cast = add(x = var_13552_cast, y = inputs_409_cast)[name = tensor("inputs_411_cast")]; + tensor var_13556 = const()[name = tensor("op_13556"), val = tensor([1])]; + tensor channels_mean_411_cast = reduce_mean(axes = var_13556, keep_dims = var_12513, x = inputs_411_cast)[name = tensor("channels_mean_411_cast")]; + tensor zero_mean_411_cast = sub(x = inputs_411_cast, y = channels_mean_411_cast)[name = tensor("zero_mean_411_cast")]; + tensor zero_mean_sq_411_cast = mul(x = zero_mean_411_cast, y = zero_mean_411_cast)[name = tensor("zero_mean_sq_411_cast")]; + tensor var_13560 = const()[name = tensor("op_13560"), val = tensor([1])]; + tensor var_13561_cast = reduce_mean(axes = var_13560, keep_dims = var_12513, x = zero_mean_sq_411_cast)[name = tensor("op_13561_cast")]; + tensor var_13562_to_fp16 = const()[name = tensor("op_13562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13563_cast = add(x = var_13561_cast, y = var_13562_to_fp16)[name = tensor("op_13563_cast")]; + tensor denom_411_epsilon_0_to_fp16 = const()[name = tensor("denom_411_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_411_cast = rsqrt(epsilon = denom_411_epsilon_0_to_fp16, x = var_13563_cast)[name = tensor("denom_411_cast")]; + tensor out_411_cast = mul(x = zero_mean_411_cast, y = denom_411_cast)[name = tensor("out_411_cast")]; + tensor var_13567_to_fp16 = const()[name = tensor("op_13567_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5067759808)))]; + tensor var_13568_cast = add(x = out_411_cast, y = var_13567_to_fp16)[name = tensor("op_13568_cast")]; + tensor var_13570_to_fp16 = const()[name = tensor("op_13570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5067761152)))]; + tensor hidden_states_563_cast = mul(x = var_13568_cast, y = var_13570_to_fp16)[name = tensor("hidden_states_563_cast")]; + tensor var_13577 = const()[name = tensor("op_13577"), val = tensor([1, 1])]; + tensor var_13579 = const()[name = tensor("op_13579"), val = tensor([1, 1])]; + tensor q_275_pad_type_0 = const()[name = tensor("q_275_pad_type_0"), val = tensor("custom")]; + tensor q_275_pad_0 = const()[name = tensor("q_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5067762496)))]; + tensor q_275_cast = conv(dilations = var_13579, groups = var_12518, pad = q_275_pad_0, pad_type = q_275_pad_type_0, strides = var_13577, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_563_cast)[name = tensor("q_275_cast")]; + tensor var_13583 = const()[name = tensor("op_13583"), val = tensor([1, 1])]; + tensor var_13585 = const()[name = tensor("op_13585"), val = tensor([1, 1])]; + tensor k_275_pad_type_0 = const()[name = tensor("k_275_pad_type_0"), val = tensor("custom")]; + tensor k_275_pad_0 = const()[name = tensor("k_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5068581760)))]; + tensor k_275_cast = conv(dilations = var_13585, groups = var_12518, pad = k_275_pad_0, pad_type = k_275_pad_type_0, strides = var_13583, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_275_cast")]; + tensor var_13589 = const()[name = tensor("op_13589"), val = tensor([1, 1])]; + tensor var_13591 = const()[name = tensor("op_13591"), val = tensor([1, 1])]; + tensor v_275_pad_type_0 = const()[name = tensor("v_275_pad_type_0"), val = tensor("custom")]; + tensor v_275_pad_0 = const()[name = tensor("v_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5071203264)))]; + tensor v_275_cast = conv(dilations = var_13591, groups = var_12518, pad = v_275_pad_0, pad_type = v_275_pad_type_0, strides = var_13589, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_275_cast")]; + tensor var_13595 = const()[name = tensor("op_13595"), val = tensor([2, 10, 64, -1])]; + tensor var_13596_cast = reshape(shape = var_13595, x = q_275_cast)[name = tensor("op_13596_cast")]; + tensor var_13597 = const()[name = tensor("op_13597"), val = tensor([2, 10, 64, -1])]; + tensor var_13598_cast = reshape(shape = var_13597, x = k_275_cast)[name = tensor("op_13598_cast")]; + tensor var_13599 = const()[name = tensor("op_13599"), val = tensor([2, 10, 64, -1])]; + tensor var_13600_cast = reshape(shape = var_13599, x = v_275_cast)[name = tensor("op_13600_cast")]; + tensor attn_weights_549_transpose_x_0 = const()[name = tensor("attn_weights_549_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_549_transpose_y_0 = const()[name = tensor("attn_weights_549_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_549_cast = matmul(transpose_x = attn_weights_549_transpose_x_0, transpose_y = attn_weights_549_transpose_y_0, x = var_13596_cast, y = var_13598_cast)[name = tensor("attn_weights_549_cast")]; + tensor attn_weights_551_cast = mul(x = attn_weights_549_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_551_cast")]; + tensor var_13604_cast = softmax(axis = var_12502, x = attn_weights_551_cast)[name = tensor("op_13604_cast")]; + tensor attn_275_transpose_x_0 = const()[name = tensor("attn_275_transpose_x_0"), val = tensor(false)]; + tensor attn_275_transpose_y_0 = const()[name = tensor("attn_275_transpose_y_0"), val = tensor(true)]; + tensor attn_275_cast = matmul(transpose_x = attn_275_transpose_x_0, transpose_y = attn_275_transpose_y_0, x = var_13600_cast, y = var_13604_cast)[name = tensor("attn_275_cast")]; + tensor var_13608 = const()[name = tensor("op_13608"), val = tensor([2, 640, 1, -1])]; + tensor input_807_cast = reshape(shape = var_13608, x = attn_275_cast)[name = tensor("input_807_cast")]; + tensor var_13613 = const()[name = tensor("op_13613"), val = tensor([1, 1])]; + tensor var_13615 = const()[name = tensor("op_13615"), val = tensor([1, 1])]; + tensor var_13617_pad_type_0 = const()[name = tensor("op_13617_pad_type_0"), val = tensor("custom")]; + tensor var_13617_pad_0 = const()[name = tensor("op_13617_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5073824768)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5074644032)))]; + tensor var_13617_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_13615, groups = var_12518, pad = var_13617_pad_0, pad_type = var_13617_pad_type_0, strides = var_13613, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_807_cast)[name = tensor("op_13617_cast")]; + tensor inputs_413_cast = add(x = var_13617_cast, y = inputs_411_cast)[name = tensor("inputs_413_cast")]; + tensor var_13621 = const()[name = tensor("op_13621"), val = tensor([1])]; + tensor channels_mean_413_cast = reduce_mean(axes = var_13621, keep_dims = var_12513, x = inputs_413_cast)[name = tensor("channels_mean_413_cast")]; + tensor zero_mean_413_cast = sub(x = inputs_413_cast, y = channels_mean_413_cast)[name = tensor("zero_mean_413_cast")]; + tensor zero_mean_sq_413_cast = mul(x = zero_mean_413_cast, y = zero_mean_413_cast)[name = tensor("zero_mean_sq_413_cast")]; + tensor var_13625 = const()[name = tensor("op_13625"), val = tensor([1])]; + tensor var_13626_cast = reduce_mean(axes = var_13625, keep_dims = var_12513, x = zero_mean_sq_413_cast)[name = tensor("op_13626_cast")]; + tensor var_13627_to_fp16 = const()[name = tensor("op_13627_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13628_cast = add(x = var_13626_cast, y = var_13627_to_fp16)[name = tensor("op_13628_cast")]; + tensor denom_413_epsilon_0_to_fp16 = const()[name = tensor("denom_413_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_413_cast = rsqrt(epsilon = denom_413_epsilon_0_to_fp16, x = var_13628_cast)[name = tensor("denom_413_cast")]; + tensor out_413_cast = mul(x = zero_mean_413_cast, y = denom_413_cast)[name = tensor("out_413_cast")]; + tensor var_13632_to_fp16 = const()[name = tensor("op_13632_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5074645376)))]; + tensor var_13633_cast = add(x = out_413_cast, y = var_13632_to_fp16)[name = tensor("op_13633_cast")]; + tensor var_13635_to_fp16 = const()[name = tensor("op_13635_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5074646720)))]; + tensor input_809_cast = mul(x = var_13633_cast, y = var_13635_to_fp16)[name = tensor("input_809_cast")]; + tensor var_13643 = const()[name = tensor("op_13643"), val = tensor([1, 1])]; + tensor var_13645 = const()[name = tensor("op_13645"), val = tensor([1, 1])]; + tensor var_13647_pad_type_0 = const()[name = tensor("op_13647_pad_type_0"), val = tensor("custom")]; + tensor var_13647_pad_0 = const()[name = tensor("op_13647_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5074648064)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5081201728)))]; + tensor var_13647_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_13645, groups = var_12518, pad = var_13647_pad_0, pad_type = var_13647_pad_type_0, strides = var_13643, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_809_cast)[name = tensor("op_13647_cast")]; + tensor var_13648_split_sizes_0 = const()[name = tensor("op_13648_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13648_axis_0 = const()[name = tensor("op_13648_axis_0"), val = tensor(1)]; + tensor var_13648_cast_0, tensor var_13648_cast_1 = split(axis = var_13648_axis_0, split_sizes = var_13648_split_sizes_0, x = var_13647_cast)[name = tensor("op_13648_cast")]; + tensor var_13650_mode_0 = const()[name = tensor("op_13650_mode_0"), val = tensor("EXACT")]; + tensor var_13650_cast = gelu(mode = var_13650_mode_0, x = var_13648_cast_1)[name = tensor("op_13650_cast")]; + tensor input_811_cast = mul(x = var_13648_cast_0, y = var_13650_cast)[name = tensor("input_811_cast")]; + tensor var_13654 = const()[name = tensor("op_13654"), val = tensor([1, 1])]; + tensor var_13656 = const()[name = tensor("op_13656"), val = tensor([1, 1])]; + tensor var_13658_pad_type_0 = const()[name = tensor("op_13658_pad_type_0"), val = tensor("custom")]; + tensor var_13658_pad_0 = const()[name = tensor("op_13658_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5081212032)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5084488896)))]; + tensor var_13658_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_13656, groups = var_12518, pad = var_13658_pad_0, pad_type = var_13658_pad_type_0, strides = var_13654, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_811_cast)[name = tensor("op_13658_cast")]; + tensor inputs_415_cast = add(x = var_13658_cast, y = inputs_413_cast)[name = tensor("inputs_415_cast")]; + tensor var_13668 = const()[name = tensor("op_13668"), val = tensor([1])]; + tensor channels_mean_415_cast = reduce_mean(axes = var_13668, keep_dims = var_12513, x = inputs_415_cast)[name = tensor("channels_mean_415_cast")]; + tensor zero_mean_415_cast = sub(x = inputs_415_cast, y = channels_mean_415_cast)[name = tensor("zero_mean_415_cast")]; + tensor zero_mean_sq_415_cast = mul(x = zero_mean_415_cast, y = zero_mean_415_cast)[name = tensor("zero_mean_sq_415_cast")]; + tensor var_13672 = const()[name = tensor("op_13672"), val = tensor([1])]; + tensor var_13673_cast = reduce_mean(axes = var_13672, keep_dims = var_12513, x = zero_mean_sq_415_cast)[name = tensor("op_13673_cast")]; + tensor var_13674_to_fp16 = const()[name = tensor("op_13674_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13675_cast = add(x = var_13673_cast, y = var_13674_to_fp16)[name = tensor("op_13675_cast")]; + tensor denom_415_epsilon_0_to_fp16 = const()[name = tensor("denom_415_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_415_cast = rsqrt(epsilon = denom_415_epsilon_0_to_fp16, x = var_13675_cast)[name = tensor("denom_415_cast")]; + tensor out_415_cast = mul(x = zero_mean_415_cast, y = denom_415_cast)[name = tensor("out_415_cast")]; + tensor var_13679_to_fp16 = const()[name = tensor("op_13679_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5084490240)))]; + tensor var_13680_cast = add(x = out_415_cast, y = var_13679_to_fp16)[name = tensor("op_13680_cast")]; + tensor var_13682_to_fp16 = const()[name = tensor("op_13682_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5084491584)))]; + tensor hidden_states_567_cast = mul(x = var_13680_cast, y = var_13682_to_fp16)[name = tensor("hidden_states_567_cast")]; + tensor var_13689 = const()[name = tensor("op_13689"), val = tensor([1, 1])]; + tensor var_13691 = const()[name = tensor("op_13691"), val = tensor([1, 1])]; + tensor q_277_pad_type_0 = const()[name = tensor("q_277_pad_type_0"), val = tensor("custom")]; + tensor q_277_pad_0 = const()[name = tensor("q_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5084492928)))]; + tensor q_277_cast = conv(dilations = var_13691, groups = var_12518, pad = q_277_pad_0, pad_type = q_277_pad_type_0, strides = var_13689, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_567_cast)[name = tensor("q_277_cast")]; + tensor var_13695 = const()[name = tensor("op_13695"), val = tensor([1, 1])]; + tensor var_13697 = const()[name = tensor("op_13697"), val = tensor([1, 1])]; + tensor k_277_pad_type_0 = const()[name = tensor("k_277_pad_type_0"), val = tensor("custom")]; + tensor k_277_pad_0 = const()[name = tensor("k_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5085312192)))]; + tensor k_277_cast = conv(dilations = var_13697, groups = var_12518, pad = k_277_pad_0, pad_type = k_277_pad_type_0, strides = var_13695, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_567_cast)[name = tensor("k_277_cast")]; + tensor var_13701 = const()[name = tensor("op_13701"), val = tensor([1, 1])]; + tensor var_13703 = const()[name = tensor("op_13703"), val = tensor([1, 1])]; + tensor v_277_pad_type_0 = const()[name = tensor("v_277_pad_type_0"), val = tensor("custom")]; + tensor v_277_pad_0 = const()[name = tensor("v_277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5086131456)))]; + tensor v_277_cast = conv(dilations = var_13703, groups = var_12518, pad = v_277_pad_0, pad_type = v_277_pad_type_0, strides = var_13701, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_567_cast)[name = tensor("v_277_cast")]; + tensor var_13707 = const()[name = tensor("op_13707"), val = tensor([2, 10, 64, -1])]; + tensor var_13708_cast = reshape(shape = var_13707, x = q_277_cast)[name = tensor("op_13708_cast")]; + tensor var_13709 = const()[name = tensor("op_13709"), val = tensor([2, 10, 64, -1])]; + tensor var_13710_cast = reshape(shape = var_13709, x = k_277_cast)[name = tensor("op_13710_cast")]; + tensor var_13711 = const()[name = tensor("op_13711"), val = tensor([2, 10, 64, -1])]; + tensor var_13712_cast = reshape(shape = var_13711, x = v_277_cast)[name = tensor("op_13712_cast")]; + tensor attn_weights_553_transpose_x_0 = const()[name = tensor("attn_weights_553_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_553_transpose_y_0 = const()[name = tensor("attn_weights_553_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_553_cast = matmul(transpose_x = attn_weights_553_transpose_x_0, transpose_y = attn_weights_553_transpose_y_0, x = var_13708_cast, y = var_13710_cast)[name = tensor("attn_weights_553_cast")]; + tensor attn_weights_555_cast = mul(x = attn_weights_553_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_555_cast")]; + tensor var_13716_cast = softmax(axis = var_12502, x = attn_weights_555_cast)[name = tensor("op_13716_cast")]; + tensor attn_277_transpose_x_0 = const()[name = tensor("attn_277_transpose_x_0"), val = tensor(false)]; + tensor attn_277_transpose_y_0 = const()[name = tensor("attn_277_transpose_y_0"), val = tensor(true)]; + tensor attn_277_cast = matmul(transpose_x = attn_277_transpose_x_0, transpose_y = attn_277_transpose_y_0, x = var_13712_cast, y = var_13716_cast)[name = tensor("attn_277_cast")]; + tensor var_13720 = const()[name = tensor("op_13720"), val = tensor([2, 640, 1, -1])]; + tensor input_813_cast = reshape(shape = var_13720, x = attn_277_cast)[name = tensor("input_813_cast")]; + tensor var_13725 = const()[name = tensor("op_13725"), val = tensor([1, 1])]; + tensor var_13727 = const()[name = tensor("op_13727"), val = tensor([1, 1])]; + tensor var_13729_pad_type_0 = const()[name = tensor("op_13729_pad_type_0"), val = tensor("custom")]; + tensor var_13729_pad_0 = const()[name = tensor("op_13729_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5086950720)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5087769984)))]; + tensor var_13729_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_13727, groups = var_12518, pad = var_13729_pad_0, pad_type = var_13729_pad_type_0, strides = var_13725, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_813_cast)[name = tensor("op_13729_cast")]; + tensor inputs_417_cast = add(x = var_13729_cast, y = inputs_415_cast)[name = tensor("inputs_417_cast")]; + tensor var_13733 = const()[name = tensor("op_13733"), val = tensor([1])]; + tensor channels_mean_417_cast = reduce_mean(axes = var_13733, keep_dims = var_12513, x = inputs_417_cast)[name = tensor("channels_mean_417_cast")]; + tensor zero_mean_417_cast = sub(x = inputs_417_cast, y = channels_mean_417_cast)[name = tensor("zero_mean_417_cast")]; + tensor zero_mean_sq_417_cast = mul(x = zero_mean_417_cast, y = zero_mean_417_cast)[name = tensor("zero_mean_sq_417_cast")]; + tensor var_13737 = const()[name = tensor("op_13737"), val = tensor([1])]; + tensor var_13738_cast = reduce_mean(axes = var_13737, keep_dims = var_12513, x = zero_mean_sq_417_cast)[name = tensor("op_13738_cast")]; + tensor var_13739_to_fp16 = const()[name = tensor("op_13739_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13740_cast = add(x = var_13738_cast, y = var_13739_to_fp16)[name = tensor("op_13740_cast")]; + tensor denom_417_epsilon_0_to_fp16 = const()[name = tensor("denom_417_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_417_cast = rsqrt(epsilon = denom_417_epsilon_0_to_fp16, x = var_13740_cast)[name = tensor("denom_417_cast")]; + tensor out_417_cast = mul(x = zero_mean_417_cast, y = denom_417_cast)[name = tensor("out_417_cast")]; + tensor var_13744_to_fp16 = const()[name = tensor("op_13744_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5087771328)))]; + tensor var_13745_cast = add(x = out_417_cast, y = var_13744_to_fp16)[name = tensor("op_13745_cast")]; + tensor var_13747_to_fp16 = const()[name = tensor("op_13747_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5087772672)))]; + tensor hidden_states_569_cast = mul(x = var_13745_cast, y = var_13747_to_fp16)[name = tensor("hidden_states_569_cast")]; + tensor var_13754 = const()[name = tensor("op_13754"), val = tensor([1, 1])]; + tensor var_13756 = const()[name = tensor("op_13756"), val = tensor([1, 1])]; + tensor q_pad_type_0 = const()[name = tensor("q_pad_type_0"), val = tensor("custom")]; + tensor q_pad_0 = const()[name = tensor("q_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5087774016)))]; + tensor q_cast = conv(dilations = var_13756, groups = var_12518, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_13754, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_569_cast)[name = tensor("q_cast")]; + tensor var_13760 = const()[name = tensor("op_13760"), val = tensor([1, 1])]; + tensor var_13762 = const()[name = tensor("op_13762"), val = tensor([1, 1])]; + tensor k_pad_type_0 = const()[name = tensor("k_pad_type_0"), val = tensor("custom")]; + tensor k_pad_0 = const()[name = tensor("k_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5088593280)))]; + tensor k_cast = conv(dilations = var_13762, groups = var_12518, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_13760, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_cast")]; + tensor var_13766 = const()[name = tensor("op_13766"), val = tensor([1, 1])]; + tensor var_13768 = const()[name = tensor("op_13768"), val = tensor([1, 1])]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("custom")]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5091214784)))]; + tensor v_cast = conv(dilations = var_13768, groups = var_12518, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_13766, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_cast")]; + tensor var_13772 = const()[name = tensor("op_13772"), val = tensor([2, 10, 64, -1])]; + tensor var_13773_cast = reshape(shape = var_13772, x = q_cast)[name = tensor("op_13773_cast")]; + tensor var_13774 = const()[name = tensor("op_13774"), val = tensor([2, 10, 64, -1])]; + tensor var_13775_cast = reshape(shape = var_13774, x = k_cast)[name = tensor("op_13775_cast")]; + tensor var_13776 = const()[name = tensor("op_13776"), val = tensor([2, 10, 64, -1])]; + tensor var_13777_cast = reshape(shape = var_13776, x = v_cast)[name = tensor("op_13777_cast")]; + tensor attn_weights_557_transpose_x_0 = const()[name = tensor("attn_weights_557_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_557_transpose_y_0 = const()[name = tensor("attn_weights_557_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_557_cast = matmul(transpose_x = attn_weights_557_transpose_x_0, transpose_y = attn_weights_557_transpose_y_0, x = var_13773_cast, y = var_13775_cast)[name = tensor("attn_weights_557_cast")]; + tensor attn_weights_cast = mul(x = attn_weights_557_cast, y = var_12509_to_fp16)[name = tensor("attn_weights_cast")]; + tensor var_13781_cast = softmax(axis = var_12502, x = attn_weights_cast)[name = tensor("op_13781_cast")]; + tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; + tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; + tensor attn_cast = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_13777_cast, y = var_13781_cast)[name = tensor("attn_cast")]; + tensor var_13785 = const()[name = tensor("op_13785"), val = tensor([2, 640, 1, -1])]; + tensor input_815_cast = reshape(shape = var_13785, x = attn_cast)[name = tensor("input_815_cast")]; + tensor var_13790 = const()[name = tensor("op_13790"), val = tensor([1, 1])]; + tensor var_13792 = const()[name = tensor("op_13792"), val = tensor([1, 1])]; + tensor var_13794_pad_type_0 = const()[name = tensor("op_13794_pad_type_0"), val = tensor("custom")]; + tensor var_13794_pad_0 = const()[name = tensor("op_13794_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5093836288)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5094655552)))]; + tensor var_13794_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_13792, groups = var_12518, pad = var_13794_pad_0, pad_type = var_13794_pad_type_0, strides = var_13790, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_815_cast)[name = tensor("op_13794_cast")]; + tensor inputs_cast = add(x = var_13794_cast, y = inputs_417_cast)[name = tensor("inputs_cast")]; + tensor var_13798 = const()[name = tensor("op_13798"), val = tensor([1])]; + tensor channels_mean_cast = reduce_mean(axes = var_13798, keep_dims = var_12513, x = inputs_cast)[name = tensor("channels_mean_cast")]; + tensor zero_mean_cast = sub(x = inputs_cast, y = channels_mean_cast)[name = tensor("zero_mean_cast")]; + tensor zero_mean_sq_cast = mul(x = zero_mean_cast, y = zero_mean_cast)[name = tensor("zero_mean_sq_cast")]; + tensor var_13802 = const()[name = tensor("op_13802"), val = tensor([1])]; + tensor var_13803_cast = reduce_mean(axes = var_13802, keep_dims = var_12513, x = zero_mean_sq_cast)[name = tensor("op_13803_cast")]; + tensor var_13804_to_fp16 = const()[name = tensor("op_13804_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13805_cast = add(x = var_13803_cast, y = var_13804_to_fp16)[name = tensor("op_13805_cast")]; + tensor denom_epsilon_0_to_fp16 = const()[name = tensor("denom_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_cast = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_13805_cast)[name = tensor("denom_cast")]; + tensor out_cast = mul(x = zero_mean_cast, y = denom_cast)[name = tensor("out_cast")]; + tensor var_13809_to_fp16 = const()[name = tensor("op_13809_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5094656896)))]; + tensor var_13810_cast = add(x = out_cast, y = var_13809_to_fp16)[name = tensor("op_13810_cast")]; + tensor var_13812_to_fp16 = const()[name = tensor("op_13812_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5094658240)))]; + tensor input_817_cast = mul(x = var_13810_cast, y = var_13812_to_fp16)[name = tensor("input_817_cast")]; + tensor var_13820 = const()[name = tensor("op_13820"), val = tensor([1, 1])]; + tensor var_13822 = const()[name = tensor("op_13822"), val = tensor([1, 1])]; + tensor var_13824_pad_type_0 = const()[name = tensor("op_13824_pad_type_0"), val = tensor("custom")]; + tensor var_13824_pad_0 = const()[name = tensor("op_13824_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5094659584)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5101213248)))]; + tensor var_13824_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_13822, groups = var_12518, pad = var_13824_pad_0, pad_type = var_13824_pad_type_0, strides = var_13820, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_817_cast)[name = tensor("op_13824_cast")]; + tensor var_13825_split_sizes_0 = const()[name = tensor("op_13825_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13825_axis_0 = const()[name = tensor("op_13825_axis_0"), val = tensor(1)]; + tensor var_13825_cast_0, tensor var_13825_cast_1 = split(axis = var_13825_axis_0, split_sizes = var_13825_split_sizes_0, x = var_13824_cast)[name = tensor("op_13825_cast")]; + tensor var_13827_mode_0 = const()[name = tensor("op_13827_mode_0"), val = tensor("EXACT")]; + tensor var_13827_cast = gelu(mode = var_13827_mode_0, x = var_13825_cast_1)[name = tensor("op_13827_cast")]; + tensor input_819_cast = mul(x = var_13825_cast_0, y = var_13827_cast)[name = tensor("input_819_cast")]; + tensor var_13831 = const()[name = tensor("op_13831"), val = tensor([1, 1])]; + tensor var_13833 = const()[name = tensor("op_13833"), val = tensor([1, 1])]; + tensor var_13835_pad_type_0 = const()[name = tensor("op_13835_pad_type_0"), val = tensor("custom")]; + tensor var_13835_pad_0 = const()[name = tensor("op_13835_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5101223552)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5104500416)))]; + tensor var_13835_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_13833, groups = var_12518, pad = var_13835_pad_0, pad_type = var_13835_pad_type_0, strides = var_13831, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_819_cast)[name = tensor("op_13835_cast")]; + tensor hidden_states_573_cast = add(x = var_13835_cast, y = inputs_cast)[name = tensor("hidden_states_573_cast")]; + tensor var_13837 = const()[name = tensor("op_13837"), val = tensor([2, 640, 64, 64])]; + tensor input_821_cast = reshape(shape = var_13837, x = hidden_states_573_cast)[name = tensor("input_821_cast")]; + tensor var_13841 = const()[name = tensor("op_13841"), val = tensor([1, 1])]; + tensor var_13843 = const()[name = tensor("op_13843"), val = tensor([1, 1])]; + tensor hidden_states_575_pad_type_0 = const()[name = tensor("hidden_states_575_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_575_pad_0 = const()[name = tensor("hidden_states_575_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5104501760)))]; + tensor up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5105321024)))]; + tensor hidden_states_575_cast = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_13843, groups = var_12518, pad = hidden_states_575_pad_0, pad_type = hidden_states_575_pad_type_0, strides = var_13841, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16, x = input_821_cast)[name = tensor("hidden_states_575_cast")]; + tensor input_823_cast = add(x = hidden_states_575_cast, y = hidden_states_557_cast)[name = tensor("input_823_cast")]; + tensor input_825_scale_factor_height_0 = const()[name = tensor("input_825_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_825_scale_factor_width_0 = const()[name = tensor("input_825_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_825_cast = upsample_nearest_neighbor(scale_factor_height = input_825_scale_factor_height_0, scale_factor_width = input_825_scale_factor_width_0, x = input_823_cast)[name = tensor("input_825_cast")]; + tensor var_13852 = const()[name = tensor("op_13852"), val = tensor([1, 1])]; + tensor var_13854 = const()[name = tensor("op_13854"), val = tensor([1, 1])]; + tensor hidden_states_577_pad_type_0 = const()[name = tensor("hidden_states_577_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_577_pad_0 = const()[name = tensor("hidden_states_577_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5105322368)))]; + tensor up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5112695232)))]; + tensor hidden_states_577_cast = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_13854, groups = var_12518, pad = hidden_states_577_pad_0, pad_type = hidden_states_577_pad_type_0, strides = var_13852, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_825_cast)[name = tensor("hidden_states_577_cast")]; + tensor var_13862 = const()[name = tensor("op_13862"), val = tensor(1)]; + tensor input_827_interleave_0 = const()[name = tensor("input_827_interleave_0"), val = tensor(false)]; + tensor input_827_cast = concat(axis = var_13862, interleave = input_827_interleave_0, values = (hidden_states_577_cast, input_43_cast))[name = tensor("input_827_cast")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([2, 32, 30, 128, 128])]; + tensor reshape_156_cast = reshape(shape = reshape_156_shape_0, x = input_827_cast)[name = tensor("reshape_156_cast")]; + tensor reduce_mean_117_axes_0 = const()[name = tensor("reduce_mean_117_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_117_keep_dims_0 = const()[name = tensor("reduce_mean_117_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_117_cast = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast)[name = tensor("reduce_mean_117_cast")]; + tensor sub_78_cast = sub(x = reshape_156_cast, y = reduce_mean_117_cast)[name = tensor("sub_78_cast")]; + tensor square_39_cast = square(x = sub_78_cast)[name = tensor("square_39_cast")]; + tensor reduce_mean_119_axes_0 = const()[name = tensor("reduce_mean_119_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_119_keep_dims_0 = const()[name = tensor("reduce_mean_119_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_119_cast = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast)[name = tensor("reduce_mean_119_cast")]; + tensor add_78_y_0_to_fp16 = const()[name = tensor("add_78_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_78_cast = add(x = reduce_mean_119_cast, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast")]; + tensor sqrt_39_cast = sqrt(x = add_78_cast)[name = tensor("sqrt_39_cast")]; + tensor real_div_39_cast = real_div(x = sub_78_cast, y = sqrt_39_cast)[name = tensor("real_div_39_cast")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([2, 960, 128, 128])]; + tensor reshape_157_cast = reshape(shape = reshape_157_shape_0, x = real_div_39_cast)[name = tensor("reshape_157_cast")]; + tensor add_79_gamma_0_to_fp16 = const()[name = tensor("add_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5112696576)))]; + tensor add_79_beta_0_to_fp16 = const()[name = tensor("add_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5112698560)))]; + tensor add_79_epsilon_0_to_fp16 = const()[name = tensor("add_79_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_79_cast = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_73_mean_0_to_fp16, variance = add_73_variance_0_to_fp16, x = reshape_157_cast)[name = tensor("add_79_cast")]; + tensor input_831_cast = silu(x = add_79_cast)[name = tensor("input_831_cast")]; + tensor var_13883 = const()[name = tensor("op_13883"), val = tensor([1, 1])]; + tensor var_13885 = const()[name = tensor("op_13885"), val = tensor([1, 1])]; + tensor hidden_states_579_pad_type_0 = const()[name = tensor("hidden_states_579_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_579_pad_0 = const()[name = tensor("hidden_states_579_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5112700544)))]; + tensor up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5118230208)))]; + tensor hidden_states_579_cast = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_13885, groups = var_13862, pad = hidden_states_579_pad_0, pad_type = hidden_states_579_pad_type_0, strides = var_13883, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_831_cast)[name = tensor("hidden_states_579_cast")]; + tensor var_13891 = const()[name = tensor("op_13891"), val = tensor([1, 1])]; + tensor var_13893 = const()[name = tensor("op_13893"), val = tensor([1, 1])]; + tensor temb_29_pad_type_0 = const()[name = tensor("temb_29_pad_type_0"), val = tensor("custom")]; + tensor temb_29_pad_0 = const()[name = tensor("temb_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5118230912)))]; + tensor up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5119050176)))]; + tensor temb_29_cast = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_13893, groups = var_13862, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_13891, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_29_cast")]; + tensor input_835_cast = add(x = hidden_states_579_cast, y = temb_29_cast)[name = tensor("input_835_cast")]; + tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([2, 32, 10, 128, 128])]; + tensor reshape_160_cast = reshape(shape = reshape_160_shape_0, x = input_835_cast)[name = tensor("reshape_160_cast")]; + tensor reduce_mean_120_axes_0 = const()[name = tensor("reduce_mean_120_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_120_keep_dims_0 = const()[name = tensor("reduce_mean_120_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_120_cast = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast)[name = tensor("reduce_mean_120_cast")]; + tensor sub_80_cast = sub(x = reshape_160_cast, y = reduce_mean_120_cast)[name = tensor("sub_80_cast")]; + tensor square_40_cast = square(x = sub_80_cast)[name = tensor("square_40_cast")]; + tensor reduce_mean_122_axes_0 = const()[name = tensor("reduce_mean_122_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_122_keep_dims_0 = const()[name = tensor("reduce_mean_122_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_122_cast = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast)[name = tensor("reduce_mean_122_cast")]; + tensor add_80_y_0_to_fp16 = const()[name = tensor("add_80_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_80_cast = add(x = reduce_mean_122_cast, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast")]; + tensor sqrt_40_cast = sqrt(x = add_80_cast)[name = tensor("sqrt_40_cast")]; + tensor real_div_40_cast = real_div(x = sub_80_cast, y = sqrt_40_cast)[name = tensor("real_div_40_cast")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([2, 320, 128, 128])]; + tensor reshape_161_cast = reshape(shape = reshape_161_shape_0, x = real_div_40_cast)[name = tensor("reshape_161_cast")]; + tensor add_81_gamma_0_to_fp16 = const()[name = tensor("add_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5119050880)))]; + tensor add_81_beta_0_to_fp16 = const()[name = tensor("add_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5119051584)))]; + tensor add_81_epsilon_0_to_fp16 = const()[name = tensor("add_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_81_cast = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_161_cast)[name = tensor("add_81_cast")]; + tensor input_839_cast = silu(x = add_81_cast)[name = tensor("input_839_cast")]; + tensor var_13903 = const()[name = tensor("op_13903"), val = tensor([1, 1])]; + tensor var_13905 = const()[name = tensor("op_13905"), val = tensor([1, 1])]; + tensor hidden_states_581_pad_type_0 = const()[name = tensor("hidden_states_581_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_581_pad_0 = const()[name = tensor("hidden_states_581_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5119052288)))]; + tensor up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5120895552)))]; + tensor hidden_states_581_cast = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_13905, groups = var_13862, pad = hidden_states_581_pad_0, pad_type = hidden_states_581_pad_type_0, strides = var_13903, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_839_cast)[name = tensor("hidden_states_581_cast")]; + tensor var_13910 = const()[name = tensor("op_13910"), val = tensor([1, 1])]; + tensor var_13912 = const()[name = tensor("op_13912"), val = tensor([1, 1])]; + tensor x_17_pad_type_0 = const()[name = tensor("x_17_pad_type_0"), val = tensor("custom")]; + tensor x_17_pad_0 = const()[name = tensor("x_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5120896256)))]; + tensor up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5121510720)))]; + tensor x_17_cast = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_13912, groups = var_13862, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_13910, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_827_cast)[name = tensor("x_17_cast")]; + tensor hidden_states_583_cast = add(x = x_17_cast, y = hidden_states_581_cast)[name = tensor("hidden_states_583_cast")]; + tensor input_841_interleave_0 = const()[name = tensor("input_841_interleave_0"), val = tensor(false)]; + tensor input_841_cast = concat(axis = var_13862, interleave = input_841_interleave_0, values = (hidden_states_583_cast, input_29_cast))[name = tensor("input_841_cast")]; + tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([2, 32, 20, 128, 128])]; + tensor reshape_164_cast = reshape(shape = reshape_164_shape_0, x = input_841_cast)[name = tensor("reshape_164_cast")]; + tensor reduce_mean_123_axes_0 = const()[name = tensor("reduce_mean_123_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_123_keep_dims_0 = const()[name = tensor("reduce_mean_123_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_123_cast = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast)[name = tensor("reduce_mean_123_cast")]; + tensor sub_82_cast = sub(x = reshape_164_cast, y = reduce_mean_123_cast)[name = tensor("sub_82_cast")]; + tensor square_41_cast = square(x = sub_82_cast)[name = tensor("square_41_cast")]; + tensor reduce_mean_125_axes_0 = const()[name = tensor("reduce_mean_125_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_125_keep_dims_0 = const()[name = tensor("reduce_mean_125_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_125_cast = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast)[name = tensor("reduce_mean_125_cast")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_82_cast = add(x = reduce_mean_125_cast, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast")]; + tensor sqrt_41_cast = sqrt(x = add_82_cast)[name = tensor("sqrt_41_cast")]; + tensor real_div_41_cast = real_div(x = sub_82_cast, y = sqrt_41_cast)[name = tensor("real_div_41_cast")]; + tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([2, 640, 128, 128])]; + tensor reshape_165_cast = reshape(shape = reshape_165_shape_0, x = real_div_41_cast)[name = tensor("reshape_165_cast")]; + tensor add_83_gamma_0_to_fp16 = const()[name = tensor("add_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5121511424)))]; + tensor add_83_beta_0_to_fp16 = const()[name = tensor("add_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5121512768)))]; + tensor add_83_epsilon_0_to_fp16 = const()[name = tensor("add_83_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_83_cast = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_165_cast)[name = tensor("add_83_cast")]; + tensor input_845_cast = silu(x = add_83_cast)[name = tensor("input_845_cast")]; + tensor var_13930 = const()[name = tensor("op_13930"), val = tensor([1, 1])]; + tensor var_13932 = const()[name = tensor("op_13932"), val = tensor([1, 1])]; + tensor hidden_states_585_pad_type_0 = const()[name = tensor("hidden_states_585_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_585_pad_0 = const()[name = tensor("hidden_states_585_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5121514112)))]; + tensor up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5125200576)))]; + tensor hidden_states_585_cast = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_13932, groups = var_13862, pad = hidden_states_585_pad_0, pad_type = hidden_states_585_pad_type_0, strides = var_13930, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_845_cast)[name = tensor("hidden_states_585_cast")]; + tensor var_13938 = const()[name = tensor("op_13938"), val = tensor([1, 1])]; + tensor var_13940 = const()[name = tensor("op_13940"), val = tensor([1, 1])]; + tensor temb_31_pad_type_0 = const()[name = tensor("temb_31_pad_type_0"), val = tensor("custom")]; + tensor temb_31_pad_0 = const()[name = tensor("temb_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5125201280)))]; + tensor up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5126020544)))]; + tensor temb_31_cast = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_13940, groups = var_13862, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_13938, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_31_cast")]; + tensor input_849_cast = add(x = hidden_states_585_cast, y = temb_31_cast)[name = tensor("input_849_cast")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([2, 32, 10, 128, 128])]; + tensor reshape_168_cast = reshape(shape = reshape_168_shape_0, x = input_849_cast)[name = tensor("reshape_168_cast")]; + tensor reduce_mean_126_axes_0 = const()[name = tensor("reduce_mean_126_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_126_keep_dims_0 = const()[name = tensor("reduce_mean_126_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_126_cast = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast)[name = tensor("reduce_mean_126_cast")]; + tensor sub_84_cast = sub(x = reshape_168_cast, y = reduce_mean_126_cast)[name = tensor("sub_84_cast")]; + tensor square_42_cast = square(x = sub_84_cast)[name = tensor("square_42_cast")]; + tensor reduce_mean_128_axes_0 = const()[name = tensor("reduce_mean_128_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_128_keep_dims_0 = const()[name = tensor("reduce_mean_128_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_128_cast = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast)[name = tensor("reduce_mean_128_cast")]; + tensor add_84_y_0_to_fp16 = const()[name = tensor("add_84_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_84_cast = add(x = reduce_mean_128_cast, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast")]; + tensor sqrt_42_cast = sqrt(x = add_84_cast)[name = tensor("sqrt_42_cast")]; + tensor real_div_42_cast = real_div(x = sub_84_cast, y = sqrt_42_cast)[name = tensor("real_div_42_cast")]; + tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([2, 320, 128, 128])]; + tensor reshape_169_cast = reshape(shape = reshape_169_shape_0, x = real_div_42_cast)[name = tensor("reshape_169_cast")]; + tensor add_85_gamma_0_to_fp16 = const()[name = tensor("add_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5126021248)))]; + tensor add_85_beta_0_to_fp16 = const()[name = tensor("add_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5126021952)))]; + tensor add_85_epsilon_0_to_fp16 = const()[name = tensor("add_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_85_cast = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_169_cast)[name = tensor("add_85_cast")]; + tensor input_853_cast = silu(x = add_85_cast)[name = tensor("input_853_cast")]; + tensor var_13950 = const()[name = tensor("op_13950"), val = tensor([1, 1])]; + tensor var_13952 = const()[name = tensor("op_13952"), val = tensor([1, 1])]; + tensor hidden_states_587_pad_type_0 = const()[name = tensor("hidden_states_587_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_587_pad_0 = const()[name = tensor("hidden_states_587_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5126022656)))]; + tensor up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5127865920)))]; + tensor hidden_states_587_cast = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_13952, groups = var_13862, pad = hidden_states_587_pad_0, pad_type = hidden_states_587_pad_type_0, strides = var_13950, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_853_cast)[name = tensor("hidden_states_587_cast")]; + tensor var_13957 = const()[name = tensor("op_13957"), val = tensor([1, 1])]; + tensor var_13959 = const()[name = tensor("op_13959"), val = tensor([1, 1])]; + tensor x_19_pad_type_0 = const()[name = tensor("x_19_pad_type_0"), val = tensor("custom")]; + tensor x_19_pad_0 = const()[name = tensor("x_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5127866624)))]; + tensor up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5128276288)))]; + tensor x_19_cast = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_13959, groups = var_13862, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_13957, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16, x = input_841_cast)[name = tensor("x_19_cast")]; + tensor hidden_states_589_cast = add(x = x_19_cast, y = hidden_states_587_cast)[name = tensor("hidden_states_589_cast")]; + tensor input_855_interleave_0 = const()[name = tensor("input_855_interleave_0"), val = tensor(false)]; + tensor input_855_cast = concat(axis = var_13862, interleave = input_855_interleave_0, values = (hidden_states_589_cast, input_13_cast))[name = tensor("input_855_cast")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([2, 32, 20, 128, 128])]; + tensor reshape_172_cast = reshape(shape = reshape_172_shape_0, x = input_855_cast)[name = tensor("reshape_172_cast")]; + tensor reduce_mean_129_axes_0 = const()[name = tensor("reduce_mean_129_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_129_keep_dims_0 = const()[name = tensor("reduce_mean_129_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_129_cast = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast)[name = tensor("reduce_mean_129_cast")]; + tensor sub_86_cast = sub(x = reshape_172_cast, y = reduce_mean_129_cast)[name = tensor("sub_86_cast")]; + tensor square_43_cast = square(x = sub_86_cast)[name = tensor("square_43_cast")]; + tensor reduce_mean_131_axes_0 = const()[name = tensor("reduce_mean_131_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_131_keep_dims_0 = const()[name = tensor("reduce_mean_131_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_131_cast = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast)[name = tensor("reduce_mean_131_cast")]; + tensor add_86_y_0_to_fp16 = const()[name = tensor("add_86_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_86_cast = add(x = reduce_mean_131_cast, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast")]; + tensor sqrt_43_cast = sqrt(x = add_86_cast)[name = tensor("sqrt_43_cast")]; + tensor real_div_43_cast = real_div(x = sub_86_cast, y = sqrt_43_cast)[name = tensor("real_div_43_cast")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([2, 640, 128, 128])]; + tensor reshape_173_cast = reshape(shape = reshape_173_shape_0, x = real_div_43_cast)[name = tensor("reshape_173_cast")]; + tensor add_87_gamma_0_to_fp16 = const()[name = tensor("add_87_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5128276992)))]; + tensor add_87_beta_0_to_fp16 = const()[name = tensor("add_87_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5128278336)))]; + tensor add_87_epsilon_0_to_fp16 = const()[name = tensor("add_87_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_87_cast = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_173_cast)[name = tensor("add_87_cast")]; + tensor input_859_cast = silu(x = add_87_cast)[name = tensor("input_859_cast")]; + tensor var_13977 = const()[name = tensor("op_13977"), val = tensor([1, 1])]; + tensor var_13979 = const()[name = tensor("op_13979"), val = tensor([1, 1])]; + tensor hidden_states_591_pad_type_0 = const()[name = tensor("hidden_states_591_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_591_pad_0 = const()[name = tensor("hidden_states_591_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5128279680)))]; + tensor up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5131966144)))]; + tensor hidden_states_591_cast = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_13979, groups = var_13862, pad = hidden_states_591_pad_0, pad_type = hidden_states_591_pad_type_0, strides = var_13977, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_859_cast)[name = tensor("hidden_states_591_cast")]; + tensor var_13985 = const()[name = tensor("op_13985"), val = tensor([1, 1])]; + tensor var_13987 = const()[name = tensor("op_13987"), val = tensor([1, 1])]; + tensor temb_pad_type_0 = const()[name = tensor("temb_pad_type_0"), val = tensor("custom")]; + tensor temb_pad_0 = const()[name = tensor("temb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5131966848)))]; + tensor up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5132786112)))]; + tensor temb_cast = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_13987, groups = var_13862, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_13985, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_cast")]; + tensor input_863_cast = add(x = hidden_states_591_cast, y = temb_cast)[name = tensor("input_863_cast")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([2, 32, 10, 128, 128])]; + tensor reshape_176_cast = reshape(shape = reshape_176_shape_0, x = input_863_cast)[name = tensor("reshape_176_cast")]; + tensor reduce_mean_132_axes_0 = const()[name = tensor("reduce_mean_132_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_132_keep_dims_0 = const()[name = tensor("reduce_mean_132_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_132_cast = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast)[name = tensor("reduce_mean_132_cast")]; + tensor sub_88_cast = sub(x = reshape_176_cast, y = reduce_mean_132_cast)[name = tensor("sub_88_cast")]; + tensor square_44_cast = square(x = sub_88_cast)[name = tensor("square_44_cast")]; + tensor reduce_mean_134_axes_0 = const()[name = tensor("reduce_mean_134_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_134_keep_dims_0 = const()[name = tensor("reduce_mean_134_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_134_cast = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast)[name = tensor("reduce_mean_134_cast")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_88_cast = add(x = reduce_mean_134_cast, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast")]; + tensor sqrt_44_cast = sqrt(x = add_88_cast)[name = tensor("sqrt_44_cast")]; + tensor real_div_44_cast = real_div(x = sub_88_cast, y = sqrt_44_cast)[name = tensor("real_div_44_cast")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([2, 320, 128, 128])]; + tensor reshape_177_cast = reshape(shape = reshape_177_shape_0, x = real_div_44_cast)[name = tensor("reshape_177_cast")]; + tensor add_89_gamma_0_to_fp16 = const()[name = tensor("add_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5132786816)))]; + tensor add_89_beta_0_to_fp16 = const()[name = tensor("add_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5132787520)))]; + tensor add_89_epsilon_0_to_fp16 = const()[name = tensor("add_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_89_cast = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_177_cast)[name = tensor("add_89_cast")]; + tensor input_867_cast = silu(x = add_89_cast)[name = tensor("input_867_cast")]; + tensor var_13997 = const()[name = tensor("op_13997"), val = tensor([1, 1])]; + tensor var_13999 = const()[name = tensor("op_13999"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5132788224)))]; + tensor up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5134631488)))]; + tensor hidden_states_cast = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_13999, groups = var_13862, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_13997, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_867_cast)[name = tensor("hidden_states_cast")]; + tensor var_14004 = const()[name = tensor("op_14004"), val = tensor([1, 1])]; + tensor var_14006 = const()[name = tensor("op_14006"), val = tensor([1, 1])]; + tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5134632192)))]; + tensor up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5135041856)))]; + tensor x_cast = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_14006, groups = var_13862, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_14004, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16, x = input_855_cast)[name = tensor("x_cast")]; + tensor input_869_cast = add(x = x_cast, y = hidden_states_cast)[name = tensor("input_869_cast")]; + tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([2, 32, 10, 128, 128])]; + tensor reshape_180_cast = reshape(shape = reshape_180_shape_0, x = input_869_cast)[name = tensor("reshape_180_cast")]; + tensor reduce_mean_135_axes_0 = const()[name = tensor("reduce_mean_135_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_135_keep_dims_0 = const()[name = tensor("reduce_mean_135_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_135_cast = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast)[name = tensor("reduce_mean_135_cast")]; + tensor sub_90_cast = sub(x = reshape_180_cast, y = reduce_mean_135_cast)[name = tensor("sub_90_cast")]; + tensor square_45_cast = square(x = sub_90_cast)[name = tensor("square_45_cast")]; + tensor reduce_mean_137_axes_0 = const()[name = tensor("reduce_mean_137_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_137_keep_dims_0 = const()[name = tensor("reduce_mean_137_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_137_cast = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast)[name = tensor("reduce_mean_137_cast")]; + tensor add_90_y_0_to_fp16 = const()[name = tensor("add_90_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_90_cast = add(x = reduce_mean_137_cast, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast")]; + tensor sqrt_45_cast = sqrt(x = add_90_cast)[name = tensor("sqrt_45_cast")]; + tensor real_div_45_cast = real_div(x = sub_90_cast, y = sqrt_45_cast)[name = tensor("real_div_45_cast")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([2, 320, 128, 128])]; + tensor reshape_181_cast = reshape(shape = reshape_181_shape_0, x = real_div_45_cast)[name = tensor("reshape_181_cast")]; + tensor add_91_gamma_0_to_fp16 = const()[name = tensor("add_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5135042560)))]; + tensor add_91_beta_0_to_fp16 = const()[name = tensor("add_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5135043264)))]; + tensor add_91_epsilon_0_to_fp16 = const()[name = tensor("add_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_91_cast = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_181_cast)[name = tensor("add_91_cast")]; + tensor input_cast = silu(x = add_91_cast)[name = tensor("input_cast")]; + tensor var_14020 = const()[name = tensor("op_14020"), val = tensor(1)]; + tensor var_14023 = const()[name = tensor("op_14023"), val = tensor([1, 1])]; + tensor var_14025 = const()[name = tensor("op_14025"), val = tensor([1, 1])]; + tensor var_14027_pad_type_0 = const()[name = tensor("op_14027_pad_type_0"), val = tensor("custom")]; + tensor var_14027_pad_0 = const()[name = tensor("op_14027_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_out_weight_to_fp16 = const()[name = tensor("conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5135043968)))]; + tensor conv_out_bias_to_fp16 = const()[name = tensor("conv_out_bias_to_fp16"), val = tensor([0x1.664p-9, -0x1.72p-10, 0x1.06p-9, -0x1.9b8p-9])]; + tensor var_14027_cast = conv(bias = conv_out_bias_to_fp16, dilations = var_14025, groups = var_14020, pad = var_14027_pad_0, pad_type = var_14027_pad_type_0, strides = var_14023, weight = conv_out_weight_to_fp16, x = input_cast)[name = tensor("op_14027_cast")]; + tensor var_14027_cast_to_fp32_dtype_0 = const()[name = tensor("op_14027_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_14027_cast_to_fp32_dtype_0, x = var_14027_cast)[name = tensor("cast_1013")]; + } -> (noise_pred); +} \ No newline at end of file diff --git a/compiled/Unet.mlmodelc/weights/weight.bin b/compiled/Unet.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..4b055dd377acf5d78fd6b9085faf2d137cc224a4 --- /dev/null +++ b/compiled/Unet.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d34f8b7b5b0b62880733514f5fc39cb79f243a3a4bdb13eff5f8855cf2c82854 +size 5135067072 diff --git a/compiled/VAEDecoder.mlmodelc/analytics/coremldata.bin b/compiled/VAEDecoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..5e5dc49b64e54b805e99f3ddf883fa95d3b9391f --- /dev/null +++ b/compiled/VAEDecoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bac2854504d7f6bfdc5645982dd965cca9cc8c12b9fdd2493cf50cd583684cc2 +size 207 diff --git a/compiled/VAEDecoder.mlmodelc/coremldata.bin b/compiled/VAEDecoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..191ad6c2c088a6da76dd03ecfef4ff2d24ed1f6f --- /dev/null +++ b/compiled/VAEDecoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:766fce2585587fe93a1d32e09bc4d63ad45335ea62239f11b193e481b5888258 +size 773 diff --git a/compiled/VAEDecoder.mlmodelc/metadata.json b/compiled/VAEDecoder.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..74234c580fbd56e743ae1ade72208e39a2d1197b --- /dev/null +++ b/compiled/VAEDecoder.mlmodelc/metadata.json @@ -0,0 +1,74 @@ +[ + { + "shortDescription" : "Stable Diffusion generates images conditioned on text and\/or other images as input through the diffusion process. Please refer to https:\/\/arxiv.org\/abs\/2112.10752 for details.", + "metadataOutputVersion" : "3.0", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32)", + "shortDescription" : "Generated image normalized to range [-1, 1]", + "shape" : "[]", + "name" : "image", + "type" : "MultiArray" + } + ], + "version" : "diffusers\/stable-diffusion-xl-base-1.0", + "modelParameters" : [ + + ], + "author" : "Please refer to the Model Card available at huggingface.co\/diffusers\/stable-diffusion-xl-base-1.0", + "specificationVersion" : 7, + "storagePrecision" : "Float32", + "license" : "OpenRAIL (https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license)", + "mlProgramOperationTypeHistogram" : { + "Ios16.mul" : 2, + "Ios16.sqrt" : 30, + "Ios16.sub" : 30, + "Transpose" : 6, + "UpsampleNearestNeighbor" : 3, + "Ios16.conv" : 36, + "Ios16.add" : 46, + "Ios16.linear" : 4, + "Ios16.matmul" : 2, + "Ios16.realDiv" : 30, + "Ios16.reduceMean" : 60, + "Ios16.softmax" : 1, + "Ios16.batchNorm" : 29, + "Ios16.square" : 30, + "Ios16.reshape" : 65, + "Ios16.silu" : 29 + }, + "computePrecision" : "Mixed (Float32, Int32)", + "isUpdatable" : "0", + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32 1 × 4 × 128 × 128)", + "shortDescription" : "The denoised latent embeddings from the unet model after the last step of reverse diffusion", + "shape" : "[1, 4, 128, 128]", + "name" : "z", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.version" : "7.0b1", + "com.github.apple.coremltools.source" : "torch==2.0.1" + }, + "generatedClassName" : "Stable_Diffusion_version_diffusers_stable_diffusion_xl_base_1_0_vae_decoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/compiled/VAEDecoder.mlmodelc/model.mil b/compiled/VAEDecoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..1934b801c86ffe3e71435c98defa3d7058d8498a --- /dev/null +++ b/compiled/VAEDecoder.mlmodelc/model.mil @@ -0,0 +1,963 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.33.4"}, {"coremlc-version", "1839.0.0"}, {"coremltools-component-torch", "2.0.1"}, {"coremltools-version", "7.0b1"}})] +{ + func main(tensor z) { + tensor post_quant_conv_bias = const()[name = tensor("post_quant_conv_bias"), val = tensor([-0x1.dbcp-5, 0x1.cf4p-3, -0x1.c7cp-4, 0x1.adp-3])]; + tensor post_quant_conv_weight = const()[name = tensor("post_quant_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor decoder_conv_in_bias = const()[name = tensor("decoder_conv_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192)))]; + tensor decoder_conv_in_weight = const()[name = tensor("decoder_conv_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2304)))]; + tensor decoder_mid_block_resnets_0_conv1_bias = const()[name = tensor("decoder_mid_block_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76096)))]; + tensor decoder_mid_block_resnets_0_conv1_weight = const()[name = tensor("decoder_mid_block_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78208)))]; + tensor decoder_mid_block_resnets_0_conv2_bias = const()[name = tensor("decoder_mid_block_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9515456)))]; + tensor decoder_mid_block_resnets_0_conv2_weight = const()[name = tensor("decoder_mid_block_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9517568)))]; + tensor decoder_mid_block_attentions_0_to_q_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_q_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18954816)))]; + tensor decoder_mid_block_attentions_0_to_q_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18956928)))]; + tensor decoder_mid_block_attentions_0_to_k_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_k_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20005568)))]; + tensor decoder_mid_block_attentions_0_to_k_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20007680)))]; + tensor decoder_mid_block_attentions_0_to_v_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21056320)))]; + tensor decoder_mid_block_attentions_0_to_v_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21058432)))]; + tensor decoder_mid_block_attentions_0_to_out_0_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22107072)))]; + tensor decoder_mid_block_attentions_0_to_out_0_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22109184)))]; + tensor decoder_mid_block_resnets_1_conv1_bias = const()[name = tensor("decoder_mid_block_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23157824)))]; + tensor decoder_mid_block_resnets_1_conv1_weight = const()[name = tensor("decoder_mid_block_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23159936)))]; + tensor decoder_mid_block_resnets_1_conv2_bias = const()[name = tensor("decoder_mid_block_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32597184)))]; + tensor decoder_mid_block_resnets_1_conv2_weight = const()[name = tensor("decoder_mid_block_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32599296)))]; + tensor decoder_up_blocks_0_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42036544)))]; + tensor decoder_up_blocks_0_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42038656)))]; + tensor decoder_up_blocks_0_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51475904)))]; + tensor decoder_up_blocks_0_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51478016)))]; + tensor decoder_up_blocks_0_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60915264)))]; + tensor decoder_up_blocks_0_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60917376)))]; + tensor decoder_up_blocks_0_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70354624)))]; + tensor decoder_up_blocks_0_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70356736)))]; + tensor decoder_up_blocks_0_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79793984)))]; + tensor decoder_up_blocks_0_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79796096)))]; + tensor decoder_up_blocks_0_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89233344)))]; + tensor decoder_up_blocks_0_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89235456)))]; + tensor decoder_up_blocks_0_upsamplers_0_conv_bias = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98672704)))]; + tensor decoder_up_blocks_0_upsamplers_0_conv_weight = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98674816)))]; + tensor decoder_up_blocks_1_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108112064)))]; + tensor decoder_up_blocks_1_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108114176)))]; + tensor decoder_up_blocks_1_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117551424)))]; + tensor decoder_up_blocks_1_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117553536)))]; + tensor decoder_up_blocks_1_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126990784)))]; + tensor decoder_up_blocks_1_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126992896)))]; + tensor decoder_up_blocks_1_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136430144)))]; + tensor decoder_up_blocks_1_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136432256)))]; + tensor decoder_up_blocks_1_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145869504)))]; + tensor decoder_up_blocks_1_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145871616)))]; + tensor decoder_up_blocks_1_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155308864)))]; + tensor decoder_up_blocks_1_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155310976)))]; + tensor decoder_up_blocks_1_upsamplers_0_conv_bias = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164748224)))]; + tensor decoder_up_blocks_1_upsamplers_0_conv_weight = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164750336)))]; + tensor decoder_up_blocks_2_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174187584)))]; + tensor decoder_up_blocks_2_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174188672)))]; + tensor decoder_up_blocks_2_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178907328)))]; + tensor decoder_up_blocks_2_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178908416)))]; + tensor decoder_up_blocks_2_resnets_0_conv_shortcut_bias = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181267776)))]; + tensor decoder_up_blocks_2_resnets_0_conv_shortcut_weight = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181268864)))]; + tensor decoder_up_blocks_2_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181793216)))]; + tensor decoder_up_blocks_2_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181794304)))]; + tensor decoder_up_blocks_2_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184153664)))]; + tensor decoder_up_blocks_2_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184154752)))]; + tensor decoder_up_blocks_2_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186514112)))]; + tensor decoder_up_blocks_2_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186515200)))]; + tensor decoder_up_blocks_2_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188874560)))]; + tensor decoder_up_blocks_2_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188875648)))]; + tensor decoder_up_blocks_2_upsamplers_0_conv_bias = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191235008)))]; + tensor decoder_up_blocks_2_upsamplers_0_conv_weight = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191236096)))]; + tensor decoder_up_blocks_3_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193595456)))]; + tensor decoder_up_blocks_3_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193596032)))]; + tensor decoder_up_blocks_3_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194775744)))]; + tensor decoder_up_blocks_3_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194776320)))]; + tensor decoder_up_blocks_3_resnets_0_conv_shortcut_bias = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195366208)))]; + tensor decoder_up_blocks_3_resnets_0_conv_shortcut_weight = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195366784)))]; + tensor decoder_up_blocks_3_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195497920)))]; + tensor decoder_up_blocks_3_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195498496)))]; + tensor decoder_up_blocks_3_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196088384)))]; + tensor decoder_up_blocks_3_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196088960)))]; + tensor decoder_up_blocks_3_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196678848)))]; + tensor decoder_up_blocks_3_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196679424)))]; + tensor decoder_up_blocks_3_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197269312)))]; + tensor decoder_up_blocks_3_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197269888)))]; + tensor decoder_conv_out_bias = const()[name = tensor("decoder_conv_out_bias"), val = tensor([0x1.fd4p-4, 0x1.4c8p-4, 0x1.93p-5])]; + tensor decoder_conv_out_weight = const()[name = tensor("decoder_conv_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197859776)))]; + tensor var_7 = const()[name = tensor("op_7"), val = tensor(1)]; + tensor var_10 = const()[name = tensor("op_10"), val = tensor([1, 1])]; + tensor var_12 = const()[name = tensor("op_12"), val = tensor([1, 1])]; + tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("custom")]; + tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_1 = conv(bias = post_quant_conv_bias, dilations = var_12, groups = var_7, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_10, weight = post_quant_conv_weight, x = z)[name = tensor("input_1")]; + tensor var_26 = const()[name = tensor("op_26"), val = tensor(1)]; + tensor var_44 = const()[name = tensor("op_44"), val = tensor([1, 1])]; + tensor var_46 = const()[name = tensor("op_46"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3 = conv(bias = decoder_conv_in_bias, dilations = var_46, groups = var_26, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_44, weight = decoder_conv_in_weight, x = input_1)[name = tensor("input_3")]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_0 = reshape(shape = reshape_0_shape_0, x = input_3)[name = tensor("reshape_0")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0)[name = tensor("reduce_mean_0")]; + tensor sub_0 = sub(x = reshape_0, y = reduce_mean_0)[name = tensor("sub_0")]; + tensor square_0 = square(x = sub_0)[name = tensor("square_0")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0)[name = tensor("reduce_mean_2")]; + tensor add_0_y_0 = const()[name = tensor("add_0_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_0 = add(x = reduce_mean_2, y = add_0_y_0)[name = tensor("add_0")]; + tensor sqrt_0 = sqrt(x = add_0)[name = tensor("sqrt_0")]; + tensor real_div_0 = real_div(x = sub_0, y = sqrt_0)[name = tensor("real_div_0")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_1 = reshape(shape = reshape_1_shape_0, x = real_div_0)[name = tensor("reshape_1")]; + tensor add_1_mean_0 = const()[name = tensor("add_1_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197873664)))]; + tensor add_1_variance_0 = const()[name = tensor("add_1_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197875776)))]; + tensor add_1_gamma_0 = const()[name = tensor("add_1_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197877888)))]; + tensor add_1_beta_0 = const()[name = tensor("add_1_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197880000)))]; + tensor add_1_epsilon_0 = const()[name = tensor("add_1_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_1 = batch_norm(beta = add_1_beta_0, epsilon = add_1_epsilon_0, gamma = add_1_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_1)[name = tensor("add_1")]; + tensor input_7 = silu(x = add_1)[name = tensor("input_7")]; + tensor var_65 = const()[name = tensor("op_65"), val = tensor([1, 1])]; + tensor var_67 = const()[name = tensor("op_67"), val = tensor([1, 1])]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_9 = conv(bias = decoder_mid_block_resnets_0_conv1_bias, dilations = var_67, groups = var_26, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_65, weight = decoder_mid_block_resnets_0_conv1_weight, x = input_7)[name = tensor("input_9")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_4 = reshape(shape = reshape_4_shape_0, x = input_9)[name = tensor("reshape_4")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4)[name = tensor("reduce_mean_3")]; + tensor sub_2 = sub(x = reshape_4, y = reduce_mean_3)[name = tensor("sub_2")]; + tensor square_1 = square(x = sub_2)[name = tensor("square_1")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1)[name = tensor("reduce_mean_5")]; + tensor add_2_y_0 = const()[name = tensor("add_2_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_2 = add(x = reduce_mean_5, y = add_2_y_0)[name = tensor("add_2")]; + tensor sqrt_1 = sqrt(x = add_2)[name = tensor("sqrt_1")]; + tensor real_div_1 = real_div(x = sub_2, y = sqrt_1)[name = tensor("real_div_1")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_5 = reshape(shape = reshape_5_shape_0, x = real_div_1)[name = tensor("reshape_5")]; + tensor add_3_gamma_0 = const()[name = tensor("add_3_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197882112)))]; + tensor add_3_beta_0 = const()[name = tensor("add_3_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197884224)))]; + tensor add_3_epsilon_0 = const()[name = tensor("add_3_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_3 = batch_norm(beta = add_3_beta_0, epsilon = add_3_epsilon_0, gamma = add_3_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_5)[name = tensor("add_3")]; + tensor input_13 = silu(x = add_3)[name = tensor("input_13")]; + tensor var_77 = const()[name = tensor("op_77"), val = tensor([1, 1])]; + tensor var_79 = const()[name = tensor("op_79"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_1 = conv(bias = decoder_mid_block_resnets_0_conv2_bias, dilations = var_79, groups = var_26, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_77, weight = decoder_mid_block_resnets_0_conv2_weight, x = input_13)[name = tensor("hidden_states_1")]; + tensor var_82 = add(x = input_3, y = hidden_states_1)[name = tensor("op_82")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 16, 16384])]; + tensor reshape_8 = reshape(shape = reshape_8_shape_0, x = var_82)[name = tensor("reshape_8")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8)[name = tensor("reduce_mean_6")]; + tensor sub_4 = sub(x = reshape_8, y = reduce_mean_6)[name = tensor("sub_4")]; + tensor square_2 = square(x = sub_4)[name = tensor("square_2")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2)[name = tensor("reduce_mean_8")]; + tensor add_4_y_0 = const()[name = tensor("add_4_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_4 = add(x = reduce_mean_8, y = add_4_y_0)[name = tensor("add_4")]; + tensor sqrt_2 = sqrt(x = add_4)[name = tensor("sqrt_2")]; + tensor real_div_2 = real_div(x = sub_4, y = sqrt_2)[name = tensor("real_div_2")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 512, 16384])]; + tensor reshape_9 = reshape(shape = reshape_9_shape_0, x = real_div_2)[name = tensor("reshape_9")]; + tensor reshape_10 = const()[name = tensor("reshape_10"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197886336)))]; + tensor mul_2 = mul(x = reshape_9, y = reshape_10)[name = tensor("mul_2")]; + tensor reshape_11 = const()[name = tensor("reshape_11"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197888448)))]; + tensor add_5 = add(x = mul_2, y = reshape_11)[name = tensor("add_5")]; + tensor input_19_perm_0 = const()[name = tensor("input_19_perm_0"), val = tensor([0, 2, 1])]; + tensor transpose_11 = transpose(perm = input_19_perm_0, x = add_5)[name = tensor("transpose_11")]; + tensor query_1 = linear(bias = decoder_mid_block_attentions_0_to_q_bias, weight = decoder_mid_block_attentions_0_to_q_weight, x = transpose_11)[name = tensor("query_1")]; + tensor key_1 = linear(bias = decoder_mid_block_attentions_0_to_k_bias, weight = decoder_mid_block_attentions_0_to_k_weight, x = transpose_11)[name = tensor("key_1")]; + tensor value_1 = linear(bias = decoder_mid_block_attentions_0_to_v_bias, weight = decoder_mid_block_attentions_0_to_v_weight, x = transpose_11)[name = tensor("value_1")]; + tensor var_123 = const()[name = tensor("op_123"), val = tensor([1, -1, 1, 512])]; + tensor var_124 = reshape(shape = var_123, x = query_1)[name = tensor("op_124")]; + tensor var_126 = const()[name = tensor("op_126"), val = tensor([1, -1, 1, 512])]; + tensor var_127 = reshape(shape = var_126, x = key_1)[name = tensor("op_127")]; + tensor var_129 = const()[name = tensor("op_129"), val = tensor([1, -1, 1, 512])]; + tensor var_130 = reshape(shape = var_129, x = value_1)[name = tensor("op_130")]; + tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor mul_3_y_0 = const()[name = tensor("mul_3_y_0"), val = tensor(0x1.6a09e6p-5)]; + tensor mul_3 = mul(x = var_124, y = mul_3_y_0)[name = tensor("mul_3")]; + tensor matmul_0_transpose_y_0 = const()[name = tensor("matmul_0_transpose_y_0"), val = tensor(true)]; + tensor matmul_0_transpose_x_0 = const()[name = tensor("matmul_0_transpose_x_0"), val = tensor(false)]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_5_perm_0 = const()[name = tensor("transpose_5_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_8 = transpose(perm = transpose_5_perm_0, x = var_127)[name = tensor("transpose_8")]; + tensor transpose_9 = transpose(perm = transpose_4_perm_0, x = mul_3)[name = tensor("transpose_9")]; + tensor matmul_0 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_9, y = transpose_8)[name = tensor("matmul_0")]; + tensor softmax_0_axis_0 = const()[name = tensor("softmax_0_axis_0"), val = tensor(-1)]; + tensor softmax_0 = softmax(axis = softmax_0_axis_0, x = matmul_0)[name = tensor("softmax_0")]; + tensor hidden_states_7_transpose_x_0 = const()[name = tensor("hidden_states_7_transpose_x_0"), val = tensor(false)]; + tensor hidden_states_7_transpose_y_0 = const()[name = tensor("hidden_states_7_transpose_y_0"), val = tensor(false)]; + tensor transpose_10 = transpose(perm = value_perm_0, x = var_130)[name = tensor("transpose_10")]; + tensor hidden_states_7 = matmul(transpose_x = hidden_states_7_transpose_x_0, transpose_y = hidden_states_7_transpose_y_0, x = softmax_0, y = transpose_10)[name = tensor("hidden_states_7")]; + tensor var_133_perm_0 = const()[name = tensor("op_133_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_137 = const()[name = tensor("op_137"), val = tensor([1, -1, 512])]; + tensor transpose_7 = transpose(perm = var_133_perm_0, x = hidden_states_7)[name = tensor("transpose_7")]; + tensor hidden_states_9 = reshape(shape = var_137, x = transpose_7)[name = tensor("hidden_states_9")]; + tensor input_23 = linear(bias = decoder_mid_block_attentions_0_to_out_0_bias, weight = decoder_mid_block_attentions_0_to_out_0_weight, x = hidden_states_9)[name = tensor("input_23")]; + tensor var_144_perm_0 = const()[name = tensor("op_144_perm_0"), val = tensor([0, -1, -2])]; + tensor var_145 = const()[name = tensor("op_145"), val = tensor([1, 512, 128, 128])]; + tensor transpose_6 = transpose(perm = var_144_perm_0, x = input_23)[name = tensor("transpose_6")]; + tensor hidden_states_13 = reshape(shape = var_145, x = transpose_6)[name = tensor("hidden_states_13")]; + tensor hidden_states_15 = add(x = hidden_states_13, y = var_82)[name = tensor("hidden_states_15")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_12 = reshape(shape = reshape_12_shape_0, x = hidden_states_15)[name = tensor("reshape_12")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12)[name = tensor("reduce_mean_9")]; + tensor sub_6 = sub(x = reshape_12, y = reduce_mean_9)[name = tensor("sub_6")]; + tensor square_3 = square(x = sub_6)[name = tensor("square_3")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3)[name = tensor("reduce_mean_11")]; + tensor add_6_y_0 = const()[name = tensor("add_6_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_6 = add(x = reduce_mean_11, y = add_6_y_0)[name = tensor("add_6")]; + tensor sqrt_3 = sqrt(x = add_6)[name = tensor("sqrt_3")]; + tensor real_div_3 = real_div(x = sub_6, y = sqrt_3)[name = tensor("real_div_3")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_13 = reshape(shape = reshape_13_shape_0, x = real_div_3)[name = tensor("reshape_13")]; + tensor add_7_gamma_0 = const()[name = tensor("add_7_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197890560)))]; + tensor add_7_beta_0 = const()[name = tensor("add_7_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197892672)))]; + tensor add_7_epsilon_0 = const()[name = tensor("add_7_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_7 = batch_norm(beta = add_7_beta_0, epsilon = add_7_epsilon_0, gamma = add_7_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_13)[name = tensor("add_7")]; + tensor input_29 = silu(x = add_7)[name = tensor("input_29")]; + tensor var_160 = const()[name = tensor("op_160"), val = tensor([1, 1])]; + tensor var_162 = const()[name = tensor("op_162"), val = tensor([1, 1])]; + tensor input_31_pad_type_0 = const()[name = tensor("input_31_pad_type_0"), val = tensor("custom")]; + tensor input_31_pad_0 = const()[name = tensor("input_31_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_31 = conv(bias = decoder_mid_block_resnets_1_conv1_bias, dilations = var_162, groups = var_26, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = var_160, weight = decoder_mid_block_resnets_1_conv1_weight, x = input_29)[name = tensor("input_31")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_16 = reshape(shape = reshape_16_shape_0, x = input_31)[name = tensor("reshape_16")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16)[name = tensor("reduce_mean_12")]; + tensor sub_8 = sub(x = reshape_16, y = reduce_mean_12)[name = tensor("sub_8")]; + tensor square_4 = square(x = sub_8)[name = tensor("square_4")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4)[name = tensor("reduce_mean_14")]; + tensor add_8_y_0 = const()[name = tensor("add_8_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_8 = add(x = reduce_mean_14, y = add_8_y_0)[name = tensor("add_8")]; + tensor sqrt_4 = sqrt(x = add_8)[name = tensor("sqrt_4")]; + tensor real_div_4 = real_div(x = sub_8, y = sqrt_4)[name = tensor("real_div_4")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_17 = reshape(shape = reshape_17_shape_0, x = real_div_4)[name = tensor("reshape_17")]; + tensor add_9_gamma_0 = const()[name = tensor("add_9_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197894784)))]; + tensor add_9_beta_0 = const()[name = tensor("add_9_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197896896)))]; + tensor add_9_epsilon_0 = const()[name = tensor("add_9_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_9 = batch_norm(beta = add_9_beta_0, epsilon = add_9_epsilon_0, gamma = add_9_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_17)[name = tensor("add_9")]; + tensor input_35 = silu(x = add_9)[name = tensor("input_35")]; + tensor var_172 = const()[name = tensor("op_172"), val = tensor([1, 1])]; + tensor var_174 = const()[name = tensor("op_174"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_17 = conv(bias = decoder_mid_block_resnets_1_conv2_bias, dilations = var_174, groups = var_26, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_172, weight = decoder_mid_block_resnets_1_conv2_weight, x = input_35)[name = tensor("hidden_states_17")]; + tensor var_177 = add(x = hidden_states_15, y = hidden_states_17)[name = tensor("op_177")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_20 = reshape(shape = reshape_20_shape_0, x = var_177)[name = tensor("reshape_20")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20)[name = tensor("reduce_mean_15")]; + tensor sub_10 = sub(x = reshape_20, y = reduce_mean_15)[name = tensor("sub_10")]; + tensor square_5 = square(x = sub_10)[name = tensor("square_5")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5)[name = tensor("reduce_mean_17")]; + tensor add_10_y_0 = const()[name = tensor("add_10_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_10 = add(x = reduce_mean_17, y = add_10_y_0)[name = tensor("add_10")]; + tensor sqrt_5 = sqrt(x = add_10)[name = tensor("sqrt_5")]; + tensor real_div_5 = real_div(x = sub_10, y = sqrt_5)[name = tensor("real_div_5")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_21 = reshape(shape = reshape_21_shape_0, x = real_div_5)[name = tensor("reshape_21")]; + tensor add_11_gamma_0 = const()[name = tensor("add_11_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197899008)))]; + tensor add_11_beta_0 = const()[name = tensor("add_11_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197901120)))]; + tensor add_11_epsilon_0 = const()[name = tensor("add_11_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_11 = batch_norm(beta = add_11_beta_0, epsilon = add_11_epsilon_0, gamma = add_11_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_21)[name = tensor("add_11")]; + tensor input_43 = silu(x = add_11)[name = tensor("input_43")]; + tensor var_199 = const()[name = tensor("op_199"), val = tensor([1, 1])]; + tensor var_201 = const()[name = tensor("op_201"), val = tensor([1, 1])]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_45 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias, dilations = var_201, groups = var_26, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_199, weight = decoder_up_blocks_0_resnets_0_conv1_weight, x = input_43)[name = tensor("input_45")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_24 = reshape(shape = reshape_24_shape_0, x = input_45)[name = tensor("reshape_24")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24)[name = tensor("reduce_mean_18")]; + tensor sub_12 = sub(x = reshape_24, y = reduce_mean_18)[name = tensor("sub_12")]; + tensor square_6 = square(x = sub_12)[name = tensor("square_6")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6)[name = tensor("reduce_mean_20")]; + tensor add_12_y_0 = const()[name = tensor("add_12_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_12 = add(x = reduce_mean_20, y = add_12_y_0)[name = tensor("add_12")]; + tensor sqrt_6 = sqrt(x = add_12)[name = tensor("sqrt_6")]; + tensor real_div_6 = real_div(x = sub_12, y = sqrt_6)[name = tensor("real_div_6")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_25 = reshape(shape = reshape_25_shape_0, x = real_div_6)[name = tensor("reshape_25")]; + tensor add_13_gamma_0 = const()[name = tensor("add_13_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197903232)))]; + tensor add_13_beta_0 = const()[name = tensor("add_13_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197905344)))]; + tensor add_13_epsilon_0 = const()[name = tensor("add_13_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_13 = batch_norm(beta = add_13_beta_0, epsilon = add_13_epsilon_0, gamma = add_13_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_25)[name = tensor("add_13")]; + tensor input_49 = silu(x = add_13)[name = tensor("input_49")]; + tensor var_211 = const()[name = tensor("op_211"), val = tensor([1, 1])]; + tensor var_213 = const()[name = tensor("op_213"), val = tensor([1, 1])]; + tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_19 = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias, dilations = var_213, groups = var_26, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_211, weight = decoder_up_blocks_0_resnets_0_conv2_weight, x = input_49)[name = tensor("hidden_states_19")]; + tensor var_216 = add(x = var_177, y = hidden_states_19)[name = tensor("op_216")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_28 = reshape(shape = reshape_28_shape_0, x = var_216)[name = tensor("reshape_28")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28)[name = tensor("reduce_mean_21")]; + tensor sub_14 = sub(x = reshape_28, y = reduce_mean_21)[name = tensor("sub_14")]; + tensor square_7 = square(x = sub_14)[name = tensor("square_7")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7)[name = tensor("reduce_mean_23")]; + tensor add_14_y_0 = const()[name = tensor("add_14_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_14 = add(x = reduce_mean_23, y = add_14_y_0)[name = tensor("add_14")]; + tensor sqrt_7 = sqrt(x = add_14)[name = tensor("sqrt_7")]; + tensor real_div_7 = real_div(x = sub_14, y = sqrt_7)[name = tensor("real_div_7")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_29 = reshape(shape = reshape_29_shape_0, x = real_div_7)[name = tensor("reshape_29")]; + tensor add_15_gamma_0 = const()[name = tensor("add_15_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197907456)))]; + tensor add_15_beta_0 = const()[name = tensor("add_15_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197909568)))]; + tensor add_15_epsilon_0 = const()[name = tensor("add_15_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_15 = batch_norm(beta = add_15_beta_0, epsilon = add_15_epsilon_0, gamma = add_15_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_29)[name = tensor("add_15")]; + tensor input_57 = silu(x = add_15)[name = tensor("input_57")]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([1, 1])]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([1, 1])]; + tensor input_59_pad_type_0 = const()[name = tensor("input_59_pad_type_0"), val = tensor("custom")]; + tensor input_59_pad_0 = const()[name = tensor("input_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_59 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias, dilations = var_231, groups = var_26, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = var_229, weight = decoder_up_blocks_0_resnets_1_conv1_weight, x = input_57)[name = tensor("input_59")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_32 = reshape(shape = reshape_32_shape_0, x = input_59)[name = tensor("reshape_32")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32)[name = tensor("reduce_mean_24")]; + tensor sub_16 = sub(x = reshape_32, y = reduce_mean_24)[name = tensor("sub_16")]; + tensor square_8 = square(x = sub_16)[name = tensor("square_8")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8)[name = tensor("reduce_mean_26")]; + tensor add_16_y_0 = const()[name = tensor("add_16_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_16 = add(x = reduce_mean_26, y = add_16_y_0)[name = tensor("add_16")]; + tensor sqrt_8 = sqrt(x = add_16)[name = tensor("sqrt_8")]; + tensor real_div_8 = real_div(x = sub_16, y = sqrt_8)[name = tensor("real_div_8")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_33 = reshape(shape = reshape_33_shape_0, x = real_div_8)[name = tensor("reshape_33")]; + tensor add_17_gamma_0 = const()[name = tensor("add_17_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197911680)))]; + tensor add_17_beta_0 = const()[name = tensor("add_17_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197913792)))]; + tensor add_17_epsilon_0 = const()[name = tensor("add_17_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_17 = batch_norm(beta = add_17_beta_0, epsilon = add_17_epsilon_0, gamma = add_17_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_33)[name = tensor("add_17")]; + tensor input_63 = silu(x = add_17)[name = tensor("input_63")]; + tensor var_241 = const()[name = tensor("op_241"), val = tensor([1, 1])]; + tensor var_243 = const()[name = tensor("op_243"), val = tensor([1, 1])]; + tensor hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_21 = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias, dilations = var_243, groups = var_26, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_241, weight = decoder_up_blocks_0_resnets_1_conv2_weight, x = input_63)[name = tensor("hidden_states_21")]; + tensor var_246 = add(x = var_216, y = hidden_states_21)[name = tensor("op_246")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_36 = reshape(shape = reshape_36_shape_0, x = var_246)[name = tensor("reshape_36")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36)[name = tensor("reduce_mean_27")]; + tensor sub_18 = sub(x = reshape_36, y = reduce_mean_27)[name = tensor("sub_18")]; + tensor square_9 = square(x = sub_18)[name = tensor("square_9")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9)[name = tensor("reduce_mean_29")]; + tensor add_18_y_0 = const()[name = tensor("add_18_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_18 = add(x = reduce_mean_29, y = add_18_y_0)[name = tensor("add_18")]; + tensor sqrt_9 = sqrt(x = add_18)[name = tensor("sqrt_9")]; + tensor real_div_9 = real_div(x = sub_18, y = sqrt_9)[name = tensor("real_div_9")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_37 = reshape(shape = reshape_37_shape_0, x = real_div_9)[name = tensor("reshape_37")]; + tensor add_19_gamma_0 = const()[name = tensor("add_19_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197915904)))]; + tensor add_19_beta_0 = const()[name = tensor("add_19_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197918016)))]; + tensor add_19_epsilon_0 = const()[name = tensor("add_19_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_19 = batch_norm(beta = add_19_beta_0, epsilon = add_19_epsilon_0, gamma = add_19_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_37)[name = tensor("add_19")]; + tensor input_71 = silu(x = add_19)[name = tensor("input_71")]; + tensor var_259 = const()[name = tensor("op_259"), val = tensor([1, 1])]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1, 1])]; + tensor input_73_pad_type_0 = const()[name = tensor("input_73_pad_type_0"), val = tensor("custom")]; + tensor input_73_pad_0 = const()[name = tensor("input_73_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_73 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias, dilations = var_261, groups = var_26, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = var_259, weight = decoder_up_blocks_0_resnets_2_conv1_weight, x = input_71)[name = tensor("input_73")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_40 = reshape(shape = reshape_40_shape_0, x = input_73)[name = tensor("reshape_40")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40)[name = tensor("reduce_mean_30")]; + tensor sub_20 = sub(x = reshape_40, y = reduce_mean_30)[name = tensor("sub_20")]; + tensor square_10 = square(x = sub_20)[name = tensor("square_10")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10)[name = tensor("reduce_mean_32")]; + tensor add_20_y_0 = const()[name = tensor("add_20_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_20 = add(x = reduce_mean_32, y = add_20_y_0)[name = tensor("add_20")]; + tensor sqrt_10 = sqrt(x = add_20)[name = tensor("sqrt_10")]; + tensor real_div_10 = real_div(x = sub_20, y = sqrt_10)[name = tensor("real_div_10")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_41 = reshape(shape = reshape_41_shape_0, x = real_div_10)[name = tensor("reshape_41")]; + tensor add_21_gamma_0 = const()[name = tensor("add_21_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197920128)))]; + tensor add_21_beta_0 = const()[name = tensor("add_21_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197922240)))]; + tensor add_21_epsilon_0 = const()[name = tensor("add_21_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_21 = batch_norm(beta = add_21_beta_0, epsilon = add_21_epsilon_0, gamma = add_21_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_41)[name = tensor("add_21")]; + tensor input_77 = silu(x = add_21)[name = tensor("input_77")]; + tensor var_271 = const()[name = tensor("op_271"), val = tensor([1, 1])]; + tensor var_273 = const()[name = tensor("op_273"), val = tensor([1, 1])]; + tensor hidden_states_23_pad_type_0 = const()[name = tensor("hidden_states_23_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_23_pad_0 = const()[name = tensor("hidden_states_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_23 = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias, dilations = var_273, groups = var_26, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = var_271, weight = decoder_up_blocks_0_resnets_2_conv2_weight, x = input_77)[name = tensor("hidden_states_23")]; + tensor var_276 = add(x = var_246, y = hidden_states_23)[name = tensor("op_276")]; + tensor input_81_scale_factor_height_0 = const()[name = tensor("input_81_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_81_scale_factor_width_0 = const()[name = tensor("input_81_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_81 = upsample_nearest_neighbor(scale_factor_height = input_81_scale_factor_height_0, scale_factor_width = input_81_scale_factor_width_0, x = var_276)[name = tensor("input_81")]; + tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 1])]; + tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 1])]; + tensor input_83_pad_type_0 = const()[name = tensor("input_83_pad_type_0"), val = tensor("custom")]; + tensor input_83_pad_0 = const()[name = tensor("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_83 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias, dilations = var_286, groups = var_26, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = var_284, weight = decoder_up_blocks_0_upsamplers_0_conv_weight, x = input_81)[name = tensor("input_83")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_44 = reshape(shape = reshape_44_shape_0, x = input_83)[name = tensor("reshape_44")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44)[name = tensor("reduce_mean_33")]; + tensor sub_22 = sub(x = reshape_44, y = reduce_mean_33)[name = tensor("sub_22")]; + tensor square_11 = square(x = sub_22)[name = tensor("square_11")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11)[name = tensor("reduce_mean_35")]; + tensor add_22_y_0 = const()[name = tensor("add_22_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_22 = add(x = reduce_mean_35, y = add_22_y_0)[name = tensor("add_22")]; + tensor sqrt_11 = sqrt(x = add_22)[name = tensor("sqrt_11")]; + tensor real_div_11 = real_div(x = sub_22, y = sqrt_11)[name = tensor("real_div_11")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_45 = reshape(shape = reshape_45_shape_0, x = real_div_11)[name = tensor("reshape_45")]; + tensor add_23_gamma_0 = const()[name = tensor("add_23_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197924352)))]; + tensor add_23_beta_0 = const()[name = tensor("add_23_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197926464)))]; + tensor add_23_epsilon_0 = const()[name = tensor("add_23_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_23 = batch_norm(beta = add_23_beta_0, epsilon = add_23_epsilon_0, gamma = add_23_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_45)[name = tensor("add_23")]; + tensor input_87 = silu(x = add_23)[name = tensor("input_87")]; + tensor var_307 = const()[name = tensor("op_307"), val = tensor([1, 1])]; + tensor var_309 = const()[name = tensor("op_309"), val = tensor([1, 1])]; + tensor input_89_pad_type_0 = const()[name = tensor("input_89_pad_type_0"), val = tensor("custom")]; + tensor input_89_pad_0 = const()[name = tensor("input_89_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_89 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias, dilations = var_309, groups = var_26, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = var_307, weight = decoder_up_blocks_1_resnets_0_conv1_weight, x = input_87)[name = tensor("input_89")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_48 = reshape(shape = reshape_48_shape_0, x = input_89)[name = tensor("reshape_48")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48)[name = tensor("reduce_mean_36")]; + tensor sub_24 = sub(x = reshape_48, y = reduce_mean_36)[name = tensor("sub_24")]; + tensor square_12 = square(x = sub_24)[name = tensor("square_12")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12)[name = tensor("reduce_mean_38")]; + tensor add_24_y_0 = const()[name = tensor("add_24_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_24 = add(x = reduce_mean_38, y = add_24_y_0)[name = tensor("add_24")]; + tensor sqrt_12 = sqrt(x = add_24)[name = tensor("sqrt_12")]; + tensor real_div_12 = real_div(x = sub_24, y = sqrt_12)[name = tensor("real_div_12")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_49 = reshape(shape = reshape_49_shape_0, x = real_div_12)[name = tensor("reshape_49")]; + tensor add_25_gamma_0 = const()[name = tensor("add_25_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197928576)))]; + tensor add_25_beta_0 = const()[name = tensor("add_25_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197930688)))]; + tensor add_25_epsilon_0 = const()[name = tensor("add_25_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_25 = batch_norm(beta = add_25_beta_0, epsilon = add_25_epsilon_0, gamma = add_25_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_49)[name = tensor("add_25")]; + tensor input_93 = silu(x = add_25)[name = tensor("input_93")]; + tensor var_319 = const()[name = tensor("op_319"), val = tensor([1, 1])]; + tensor var_321 = const()[name = tensor("op_321"), val = tensor([1, 1])]; + tensor hidden_states_27_pad_type_0 = const()[name = tensor("hidden_states_27_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_27_pad_0 = const()[name = tensor("hidden_states_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_27 = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias, dilations = var_321, groups = var_26, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_319, weight = decoder_up_blocks_1_resnets_0_conv2_weight, x = input_93)[name = tensor("hidden_states_27")]; + tensor var_324 = add(x = input_83, y = hidden_states_27)[name = tensor("op_324")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_52 = reshape(shape = reshape_52_shape_0, x = var_324)[name = tensor("reshape_52")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52)[name = tensor("reduce_mean_39")]; + tensor sub_26 = sub(x = reshape_52, y = reduce_mean_39)[name = tensor("sub_26")]; + tensor square_13 = square(x = sub_26)[name = tensor("square_13")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13)[name = tensor("reduce_mean_41")]; + tensor add_26_y_0 = const()[name = tensor("add_26_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_26 = add(x = reduce_mean_41, y = add_26_y_0)[name = tensor("add_26")]; + tensor sqrt_13 = sqrt(x = add_26)[name = tensor("sqrt_13")]; + tensor real_div_13 = real_div(x = sub_26, y = sqrt_13)[name = tensor("real_div_13")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_53 = reshape(shape = reshape_53_shape_0, x = real_div_13)[name = tensor("reshape_53")]; + tensor add_27_gamma_0 = const()[name = tensor("add_27_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197932800)))]; + tensor add_27_beta_0 = const()[name = tensor("add_27_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197934912)))]; + tensor add_27_epsilon_0 = const()[name = tensor("add_27_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_27 = batch_norm(beta = add_27_beta_0, epsilon = add_27_epsilon_0, gamma = add_27_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_53)[name = tensor("add_27")]; + tensor input_101 = silu(x = add_27)[name = tensor("input_101")]; + tensor var_337 = const()[name = tensor("op_337"), val = tensor([1, 1])]; + tensor var_339 = const()[name = tensor("op_339"), val = tensor([1, 1])]; + tensor input_103_pad_type_0 = const()[name = tensor("input_103_pad_type_0"), val = tensor("custom")]; + tensor input_103_pad_0 = const()[name = tensor("input_103_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_103 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias, dilations = var_339, groups = var_26, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = var_337, weight = decoder_up_blocks_1_resnets_1_conv1_weight, x = input_101)[name = tensor("input_103")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_56 = reshape(shape = reshape_56_shape_0, x = input_103)[name = tensor("reshape_56")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56)[name = tensor("reduce_mean_42")]; + tensor sub_28 = sub(x = reshape_56, y = reduce_mean_42)[name = tensor("sub_28")]; + tensor square_14 = square(x = sub_28)[name = tensor("square_14")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14)[name = tensor("reduce_mean_44")]; + tensor add_28_y_0 = const()[name = tensor("add_28_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_28 = add(x = reduce_mean_44, y = add_28_y_0)[name = tensor("add_28")]; + tensor sqrt_14 = sqrt(x = add_28)[name = tensor("sqrt_14")]; + tensor real_div_14 = real_div(x = sub_28, y = sqrt_14)[name = tensor("real_div_14")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_57 = reshape(shape = reshape_57_shape_0, x = real_div_14)[name = tensor("reshape_57")]; + tensor add_29_gamma_0 = const()[name = tensor("add_29_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197937024)))]; + tensor add_29_beta_0 = const()[name = tensor("add_29_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197939136)))]; + tensor add_29_epsilon_0 = const()[name = tensor("add_29_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_29 = batch_norm(beta = add_29_beta_0, epsilon = add_29_epsilon_0, gamma = add_29_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_57)[name = tensor("add_29")]; + tensor input_107 = silu(x = add_29)[name = tensor("input_107")]; + tensor var_349 = const()[name = tensor("op_349"), val = tensor([1, 1])]; + tensor var_351 = const()[name = tensor("op_351"), val = tensor([1, 1])]; + tensor hidden_states_29_pad_type_0 = const()[name = tensor("hidden_states_29_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_29_pad_0 = const()[name = tensor("hidden_states_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_29 = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias, dilations = var_351, groups = var_26, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_349, weight = decoder_up_blocks_1_resnets_1_conv2_weight, x = input_107)[name = tensor("hidden_states_29")]; + tensor var_354 = add(x = var_324, y = hidden_states_29)[name = tensor("op_354")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_60 = reshape(shape = reshape_60_shape_0, x = var_354)[name = tensor("reshape_60")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60)[name = tensor("reduce_mean_45")]; + tensor sub_30 = sub(x = reshape_60, y = reduce_mean_45)[name = tensor("sub_30")]; + tensor square_15 = square(x = sub_30)[name = tensor("square_15")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15)[name = tensor("reduce_mean_47")]; + tensor add_30_y_0 = const()[name = tensor("add_30_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_30 = add(x = reduce_mean_47, y = add_30_y_0)[name = tensor("add_30")]; + tensor sqrt_15 = sqrt(x = add_30)[name = tensor("sqrt_15")]; + tensor real_div_15 = real_div(x = sub_30, y = sqrt_15)[name = tensor("real_div_15")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_61 = reshape(shape = reshape_61_shape_0, x = real_div_15)[name = tensor("reshape_61")]; + tensor add_31_gamma_0 = const()[name = tensor("add_31_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197941248)))]; + tensor add_31_beta_0 = const()[name = tensor("add_31_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197943360)))]; + tensor add_31_epsilon_0 = const()[name = tensor("add_31_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_31 = batch_norm(beta = add_31_beta_0, epsilon = add_31_epsilon_0, gamma = add_31_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_61)[name = tensor("add_31")]; + tensor input_115 = silu(x = add_31)[name = tensor("input_115")]; + tensor var_367 = const()[name = tensor("op_367"), val = tensor([1, 1])]; + tensor var_369 = const()[name = tensor("op_369"), val = tensor([1, 1])]; + tensor input_117_pad_type_0 = const()[name = tensor("input_117_pad_type_0"), val = tensor("custom")]; + tensor input_117_pad_0 = const()[name = tensor("input_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_117 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias, dilations = var_369, groups = var_26, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_367, weight = decoder_up_blocks_1_resnets_2_conv1_weight, x = input_115)[name = tensor("input_117")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_64 = reshape(shape = reshape_64_shape_0, x = input_117)[name = tensor("reshape_64")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64)[name = tensor("reduce_mean_48")]; + tensor sub_32 = sub(x = reshape_64, y = reduce_mean_48)[name = tensor("sub_32")]; + tensor square_16 = square(x = sub_32)[name = tensor("square_16")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16)[name = tensor("reduce_mean_50")]; + tensor add_32_y_0 = const()[name = tensor("add_32_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_32 = add(x = reduce_mean_50, y = add_32_y_0)[name = tensor("add_32")]; + tensor sqrt_16 = sqrt(x = add_32)[name = tensor("sqrt_16")]; + tensor real_div_16 = real_div(x = sub_32, y = sqrt_16)[name = tensor("real_div_16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_65 = reshape(shape = reshape_65_shape_0, x = real_div_16)[name = tensor("reshape_65")]; + tensor add_33_gamma_0 = const()[name = tensor("add_33_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197945472)))]; + tensor add_33_beta_0 = const()[name = tensor("add_33_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197947584)))]; + tensor add_33_epsilon_0 = const()[name = tensor("add_33_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_33 = batch_norm(beta = add_33_beta_0, epsilon = add_33_epsilon_0, gamma = add_33_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_65)[name = tensor("add_33")]; + tensor input_121 = silu(x = add_33)[name = tensor("input_121")]; + tensor var_379 = const()[name = tensor("op_379"), val = tensor([1, 1])]; + tensor var_381 = const()[name = tensor("op_381"), val = tensor([1, 1])]; + tensor hidden_states_31_pad_type_0 = const()[name = tensor("hidden_states_31_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_31 = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias, dilations = var_381, groups = var_26, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_379, weight = decoder_up_blocks_1_resnets_2_conv2_weight, x = input_121)[name = tensor("hidden_states_31")]; + tensor var_384 = add(x = var_354, y = hidden_states_31)[name = tensor("op_384")]; + tensor input_125_scale_factor_height_0 = const()[name = tensor("input_125_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_125_scale_factor_width_0 = const()[name = tensor("input_125_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_125 = upsample_nearest_neighbor(scale_factor_height = input_125_scale_factor_height_0, scale_factor_width = input_125_scale_factor_width_0, x = var_384)[name = tensor("input_125")]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1])]; + tensor var_394 = const()[name = tensor("op_394"), val = tensor([1, 1])]; + tensor input_127_pad_type_0 = const()[name = tensor("input_127_pad_type_0"), val = tensor("custom")]; + tensor input_127_pad_0 = const()[name = tensor("input_127_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_127 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias, dilations = var_394, groups = var_26, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = var_392, weight = decoder_up_blocks_1_upsamplers_0_conv_weight, x = input_125)[name = tensor("input_127")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 512, 512])]; + tensor reshape_68 = reshape(shape = reshape_68_shape_0, x = input_127)[name = tensor("reshape_68")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68)[name = tensor("reduce_mean_51")]; + tensor sub_34 = sub(x = reshape_68, y = reduce_mean_51)[name = tensor("sub_34")]; + tensor square_17 = square(x = sub_34)[name = tensor("square_17")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17)[name = tensor("reduce_mean_53")]; + tensor add_34_y_0 = const()[name = tensor("add_34_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_34 = add(x = reduce_mean_53, y = add_34_y_0)[name = tensor("add_34")]; + tensor sqrt_17 = sqrt(x = add_34)[name = tensor("sqrt_17")]; + tensor real_div_17 = real_div(x = sub_34, y = sqrt_17)[name = tensor("real_div_17")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 512, 512, 512])]; + tensor reshape_69 = reshape(shape = reshape_69_shape_0, x = real_div_17)[name = tensor("reshape_69")]; + tensor add_35_gamma_0 = const()[name = tensor("add_35_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197949696)))]; + tensor add_35_beta_0 = const()[name = tensor("add_35_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197951808)))]; + tensor add_35_epsilon_0 = const()[name = tensor("add_35_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_35 = batch_norm(beta = add_35_beta_0, epsilon = add_35_epsilon_0, gamma = add_35_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_69)[name = tensor("add_35")]; + tensor input_131 = silu(x = add_35)[name = tensor("input_131")]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([1, 1])]; + tensor var_418 = const()[name = tensor("op_418"), val = tensor([1, 1])]; + tensor input_133_pad_type_0 = const()[name = tensor("input_133_pad_type_0"), val = tensor("custom")]; + tensor input_133_pad_0 = const()[name = tensor("input_133_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_133 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias, dilations = var_418, groups = var_26, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = var_416, weight = decoder_up_blocks_2_resnets_0_conv1_weight, x = input_131)[name = tensor("input_133")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_72 = reshape(shape = reshape_72_shape_0, x = input_133)[name = tensor("reshape_72")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72)[name = tensor("reduce_mean_54")]; + tensor sub_36 = sub(x = reshape_72, y = reduce_mean_54)[name = tensor("sub_36")]; + tensor square_18 = square(x = sub_36)[name = tensor("square_18")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18)[name = tensor("reduce_mean_56")]; + tensor add_36_y_0 = const()[name = tensor("add_36_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_36 = add(x = reduce_mean_56, y = add_36_y_0)[name = tensor("add_36")]; + tensor sqrt_18 = sqrt(x = add_36)[name = tensor("sqrt_18")]; + tensor real_div_18 = real_div(x = sub_36, y = sqrt_18)[name = tensor("real_div_18")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_73 = reshape(shape = reshape_73_shape_0, x = real_div_18)[name = tensor("reshape_73")]; + tensor add_37_mean_0 = const()[name = tensor("add_37_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197953920)))]; + tensor add_37_variance_0 = const()[name = tensor("add_37_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197955008)))]; + tensor add_37_gamma_0 = const()[name = tensor("add_37_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197956096)))]; + tensor add_37_beta_0 = const()[name = tensor("add_37_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197957184)))]; + tensor add_37_epsilon_0 = const()[name = tensor("add_37_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_37 = batch_norm(beta = add_37_beta_0, epsilon = add_37_epsilon_0, gamma = add_37_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_73)[name = tensor("add_37")]; + tensor input_137 = silu(x = add_37)[name = tensor("input_137")]; + tensor var_428 = const()[name = tensor("op_428"), val = tensor([1, 1])]; + tensor var_430 = const()[name = tensor("op_430"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_35 = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias, dilations = var_430, groups = var_26, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_428, weight = decoder_up_blocks_2_resnets_0_conv2_weight, x = input_137)[name = tensor("hidden_states_35")]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([1, 1])]; + tensor var_437 = const()[name = tensor("op_437"), val = tensor([1, 1])]; + tensor input_tensor_1_pad_type_0 = const()[name = tensor("input_tensor_1_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_1_pad_0 = const()[name = tensor("input_tensor_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_tensor_1 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias, dilations = var_437, groups = var_26, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_435, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight, x = input_127)[name = tensor("input_tensor_1")]; + tensor var_440 = add(x = input_tensor_1, y = hidden_states_35)[name = tensor("op_440")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_76 = reshape(shape = reshape_76_shape_0, x = var_440)[name = tensor("reshape_76")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76)[name = tensor("reduce_mean_57")]; + tensor sub_38 = sub(x = reshape_76, y = reduce_mean_57)[name = tensor("sub_38")]; + tensor square_19 = square(x = sub_38)[name = tensor("square_19")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19)[name = tensor("reduce_mean_59")]; + tensor add_38_y_0 = const()[name = tensor("add_38_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_38 = add(x = reduce_mean_59, y = add_38_y_0)[name = tensor("add_38")]; + tensor sqrt_19 = sqrt(x = add_38)[name = tensor("sqrt_19")]; + tensor real_div_19 = real_div(x = sub_38, y = sqrt_19)[name = tensor("real_div_19")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_77 = reshape(shape = reshape_77_shape_0, x = real_div_19)[name = tensor("reshape_77")]; + tensor add_39_gamma_0 = const()[name = tensor("add_39_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197958272)))]; + tensor add_39_beta_0 = const()[name = tensor("add_39_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197959360)))]; + tensor add_39_epsilon_0 = const()[name = tensor("add_39_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_39 = batch_norm(beta = add_39_beta_0, epsilon = add_39_epsilon_0, gamma = add_39_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_77)[name = tensor("add_39")]; + tensor input_145 = silu(x = add_39)[name = tensor("input_145")]; + tensor var_453 = const()[name = tensor("op_453"), val = tensor([1, 1])]; + tensor var_455 = const()[name = tensor("op_455"), val = tensor([1, 1])]; + tensor input_147_pad_type_0 = const()[name = tensor("input_147_pad_type_0"), val = tensor("custom")]; + tensor input_147_pad_0 = const()[name = tensor("input_147_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_147 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias, dilations = var_455, groups = var_26, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = var_453, weight = decoder_up_blocks_2_resnets_1_conv1_weight, x = input_145)[name = tensor("input_147")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_80 = reshape(shape = reshape_80_shape_0, x = input_147)[name = tensor("reshape_80")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80)[name = tensor("reduce_mean_60")]; + tensor sub_40 = sub(x = reshape_80, y = reduce_mean_60)[name = tensor("sub_40")]; + tensor square_20 = square(x = sub_40)[name = tensor("square_20")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20)[name = tensor("reduce_mean_62")]; + tensor add_40_y_0 = const()[name = tensor("add_40_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_40 = add(x = reduce_mean_62, y = add_40_y_0)[name = tensor("add_40")]; + tensor sqrt_20 = sqrt(x = add_40)[name = tensor("sqrt_20")]; + tensor real_div_20 = real_div(x = sub_40, y = sqrt_20)[name = tensor("real_div_20")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_81 = reshape(shape = reshape_81_shape_0, x = real_div_20)[name = tensor("reshape_81")]; + tensor add_41_gamma_0 = const()[name = tensor("add_41_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197960448)))]; + tensor add_41_beta_0 = const()[name = tensor("add_41_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197961536)))]; + tensor add_41_epsilon_0 = const()[name = tensor("add_41_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_41 = batch_norm(beta = add_41_beta_0, epsilon = add_41_epsilon_0, gamma = add_41_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_81)[name = tensor("add_41")]; + tensor input_151 = silu(x = add_41)[name = tensor("input_151")]; + tensor var_465 = const()[name = tensor("op_465"), val = tensor([1, 1])]; + tensor var_467 = const()[name = tensor("op_467"), val = tensor([1, 1])]; + tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_37 = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias, dilations = var_467, groups = var_26, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_465, weight = decoder_up_blocks_2_resnets_1_conv2_weight, x = input_151)[name = tensor("hidden_states_37")]; + tensor var_470 = add(x = var_440, y = hidden_states_37)[name = tensor("op_470")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_84 = reshape(shape = reshape_84_shape_0, x = var_470)[name = tensor("reshape_84")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84)[name = tensor("reduce_mean_63")]; + tensor sub_42 = sub(x = reshape_84, y = reduce_mean_63)[name = tensor("sub_42")]; + tensor square_21 = square(x = sub_42)[name = tensor("square_21")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21)[name = tensor("reduce_mean_65")]; + tensor add_42_y_0 = const()[name = tensor("add_42_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_42 = add(x = reduce_mean_65, y = add_42_y_0)[name = tensor("add_42")]; + tensor sqrt_21 = sqrt(x = add_42)[name = tensor("sqrt_21")]; + tensor real_div_21 = real_div(x = sub_42, y = sqrt_21)[name = tensor("real_div_21")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_85 = reshape(shape = reshape_85_shape_0, x = real_div_21)[name = tensor("reshape_85")]; + tensor add_43_gamma_0 = const()[name = tensor("add_43_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197962624)))]; + tensor add_43_beta_0 = const()[name = tensor("add_43_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197963712)))]; + tensor add_43_epsilon_0 = const()[name = tensor("add_43_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_43 = batch_norm(beta = add_43_beta_0, epsilon = add_43_epsilon_0, gamma = add_43_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_85)[name = tensor("add_43")]; + tensor input_159 = silu(x = add_43)[name = tensor("input_159")]; + tensor var_483 = const()[name = tensor("op_483"), val = tensor([1, 1])]; + tensor var_485 = const()[name = tensor("op_485"), val = tensor([1, 1])]; + tensor input_161_pad_type_0 = const()[name = tensor("input_161_pad_type_0"), val = tensor("custom")]; + tensor input_161_pad_0 = const()[name = tensor("input_161_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_161 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias, dilations = var_485, groups = var_26, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = var_483, weight = decoder_up_blocks_2_resnets_2_conv1_weight, x = input_159)[name = tensor("input_161")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_88 = reshape(shape = reshape_88_shape_0, x = input_161)[name = tensor("reshape_88")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88)[name = tensor("reduce_mean_66")]; + tensor sub_44 = sub(x = reshape_88, y = reduce_mean_66)[name = tensor("sub_44")]; + tensor square_22 = square(x = sub_44)[name = tensor("square_22")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22)[name = tensor("reduce_mean_68")]; + tensor add_44_y_0 = const()[name = tensor("add_44_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_44 = add(x = reduce_mean_68, y = add_44_y_0)[name = tensor("add_44")]; + tensor sqrt_22 = sqrt(x = add_44)[name = tensor("sqrt_22")]; + tensor real_div_22 = real_div(x = sub_44, y = sqrt_22)[name = tensor("real_div_22")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_89 = reshape(shape = reshape_89_shape_0, x = real_div_22)[name = tensor("reshape_89")]; + tensor add_45_gamma_0 = const()[name = tensor("add_45_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197964800)))]; + tensor add_45_beta_0 = const()[name = tensor("add_45_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197965888)))]; + tensor add_45_epsilon_0 = const()[name = tensor("add_45_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_45 = batch_norm(beta = add_45_beta_0, epsilon = add_45_epsilon_0, gamma = add_45_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_89)[name = tensor("add_45")]; + tensor input_165 = silu(x = add_45)[name = tensor("input_165")]; + tensor var_495 = const()[name = tensor("op_495"), val = tensor([1, 1])]; + tensor var_497 = const()[name = tensor("op_497"), val = tensor([1, 1])]; + tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_39 = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias, dilations = var_497, groups = var_26, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_495, weight = decoder_up_blocks_2_resnets_2_conv2_weight, x = input_165)[name = tensor("hidden_states_39")]; + tensor var_500 = add(x = var_470, y = hidden_states_39)[name = tensor("op_500")]; + tensor input_169_scale_factor_height_0 = const()[name = tensor("input_169_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_169_scale_factor_width_0 = const()[name = tensor("input_169_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_169 = upsample_nearest_neighbor(scale_factor_height = input_169_scale_factor_height_0, scale_factor_width = input_169_scale_factor_width_0, x = var_500)[name = tensor("input_169")]; + tensor var_508 = const()[name = tensor("op_508"), val = tensor([1, 1])]; + tensor var_510 = const()[name = tensor("op_510"), val = tensor([1, 1])]; + tensor input_171_pad_type_0 = const()[name = tensor("input_171_pad_type_0"), val = tensor("custom")]; + tensor input_171_pad_0 = const()[name = tensor("input_171_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_171 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias, dilations = var_510, groups = var_26, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_508, weight = decoder_up_blocks_2_upsamplers_0_conv_weight, x = input_169)[name = tensor("input_171")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([1, 32, 8, 1024, 1024])]; + tensor reshape_92 = reshape(shape = reshape_92_shape_0, x = input_171)[name = tensor("reshape_92")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92)[name = tensor("reduce_mean_69")]; + tensor sub_46 = sub(x = reshape_92, y = reduce_mean_69)[name = tensor("sub_46")]; + tensor square_23 = square(x = sub_46)[name = tensor("square_23")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23)[name = tensor("reduce_mean_71")]; + tensor add_46_y_0 = const()[name = tensor("add_46_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_46 = add(x = reduce_mean_71, y = add_46_y_0)[name = tensor("add_46")]; + tensor sqrt_23 = sqrt(x = add_46)[name = tensor("sqrt_23")]; + tensor real_div_23 = real_div(x = sub_46, y = sqrt_23)[name = tensor("real_div_23")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([1, 256, 1024, 1024])]; + tensor reshape_93 = reshape(shape = reshape_93_shape_0, x = real_div_23)[name = tensor("reshape_93")]; + tensor add_47_gamma_0 = const()[name = tensor("add_47_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197966976)))]; + tensor add_47_beta_0 = const()[name = tensor("add_47_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197968064)))]; + tensor add_47_epsilon_0 = const()[name = tensor("add_47_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_47 = batch_norm(beta = add_47_beta_0, epsilon = add_47_epsilon_0, gamma = add_47_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_93)[name = tensor("add_47")]; + tensor input_175 = silu(x = add_47)[name = tensor("input_175")]; + tensor var_530 = const()[name = tensor("op_530"), val = tensor([1, 1])]; + tensor var_532 = const()[name = tensor("op_532"), val = tensor([1, 1])]; + tensor input_177_pad_type_0 = const()[name = tensor("input_177_pad_type_0"), val = tensor("custom")]; + tensor input_177_pad_0 = const()[name = tensor("input_177_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_177 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias, dilations = var_532, groups = var_26, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = var_530, weight = decoder_up_blocks_3_resnets_0_conv1_weight, x = input_175)[name = tensor("input_177")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_96 = reshape(shape = reshape_96_shape_0, x = input_177)[name = tensor("reshape_96")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96)[name = tensor("reduce_mean_72")]; + tensor sub_48 = sub(x = reshape_96, y = reduce_mean_72)[name = tensor("sub_48")]; + tensor square_24 = square(x = sub_48)[name = tensor("square_24")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24)[name = tensor("reduce_mean_74")]; + tensor add_48_y_0 = const()[name = tensor("add_48_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_48 = add(x = reduce_mean_74, y = add_48_y_0)[name = tensor("add_48")]; + tensor sqrt_24 = sqrt(x = add_48)[name = tensor("sqrt_24")]; + tensor real_div_24 = real_div(x = sub_48, y = sqrt_24)[name = tensor("real_div_24")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_97 = reshape(shape = reshape_97_shape_0, x = real_div_24)[name = tensor("reshape_97")]; + tensor add_49_mean_0 = const()[name = tensor("add_49_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197969152)))]; + tensor add_49_variance_0 = const()[name = tensor("add_49_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197969728)))]; + tensor add_49_gamma_0 = const()[name = tensor("add_49_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197970304)))]; + tensor add_49_beta_0 = const()[name = tensor("add_49_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197970880)))]; + tensor add_49_epsilon_0 = const()[name = tensor("add_49_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_49 = batch_norm(beta = add_49_beta_0, epsilon = add_49_epsilon_0, gamma = add_49_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_97)[name = tensor("add_49")]; + tensor input_181 = silu(x = add_49)[name = tensor("input_181")]; + tensor var_542 = const()[name = tensor("op_542"), val = tensor([1, 1])]; + tensor var_544 = const()[name = tensor("op_544"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_43 = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias, dilations = var_544, groups = var_26, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_542, weight = decoder_up_blocks_3_resnets_0_conv2_weight, x = input_181)[name = tensor("hidden_states_43")]; + tensor var_549 = const()[name = tensor("op_549"), val = tensor([1, 1])]; + tensor var_551 = const()[name = tensor("op_551"), val = tensor([1, 1])]; + tensor input_tensor_pad_type_0 = const()[name = tensor("input_tensor_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_pad_0 = const()[name = tensor("input_tensor_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_tensor = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias, dilations = var_551, groups = var_26, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_549, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight, x = input_171)[name = tensor("input_tensor")]; + tensor var_554 = add(x = input_tensor, y = hidden_states_43)[name = tensor("op_554")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_100 = reshape(shape = reshape_100_shape_0, x = var_554)[name = tensor("reshape_100")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100)[name = tensor("reduce_mean_75")]; + tensor sub_50 = sub(x = reshape_100, y = reduce_mean_75)[name = tensor("sub_50")]; + tensor square_25 = square(x = sub_50)[name = tensor("square_25")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25)[name = tensor("reduce_mean_77")]; + tensor add_50_y_0 = const()[name = tensor("add_50_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_50 = add(x = reduce_mean_77, y = add_50_y_0)[name = tensor("add_50")]; + tensor sqrt_25 = sqrt(x = add_50)[name = tensor("sqrt_25")]; + tensor real_div_25 = real_div(x = sub_50, y = sqrt_25)[name = tensor("real_div_25")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_101 = reshape(shape = reshape_101_shape_0, x = real_div_25)[name = tensor("reshape_101")]; + tensor add_51_gamma_0 = const()[name = tensor("add_51_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197971456)))]; + tensor add_51_beta_0 = const()[name = tensor("add_51_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197972032)))]; + tensor add_51_epsilon_0 = const()[name = tensor("add_51_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_51 = batch_norm(beta = add_51_beta_0, epsilon = add_51_epsilon_0, gamma = add_51_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_101)[name = tensor("add_51")]; + tensor input_189 = silu(x = add_51)[name = tensor("input_189")]; + tensor var_567 = const()[name = tensor("op_567"), val = tensor([1, 1])]; + tensor var_569 = const()[name = tensor("op_569"), val = tensor([1, 1])]; + tensor input_191_pad_type_0 = const()[name = tensor("input_191_pad_type_0"), val = tensor("custom")]; + tensor input_191_pad_0 = const()[name = tensor("input_191_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_191 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias, dilations = var_569, groups = var_26, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = var_567, weight = decoder_up_blocks_3_resnets_1_conv1_weight, x = input_189)[name = tensor("input_191")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_104 = reshape(shape = reshape_104_shape_0, x = input_191)[name = tensor("reshape_104")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104)[name = tensor("reduce_mean_78")]; + tensor sub_52 = sub(x = reshape_104, y = reduce_mean_78)[name = tensor("sub_52")]; + tensor square_26 = square(x = sub_52)[name = tensor("square_26")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26)[name = tensor("reduce_mean_80")]; + tensor add_52_y_0 = const()[name = tensor("add_52_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_52 = add(x = reduce_mean_80, y = add_52_y_0)[name = tensor("add_52")]; + tensor sqrt_26 = sqrt(x = add_52)[name = tensor("sqrt_26")]; + tensor real_div_26 = real_div(x = sub_52, y = sqrt_26)[name = tensor("real_div_26")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_105 = reshape(shape = reshape_105_shape_0, x = real_div_26)[name = tensor("reshape_105")]; + tensor add_53_gamma_0 = const()[name = tensor("add_53_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197972608)))]; + tensor add_53_beta_0 = const()[name = tensor("add_53_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197973184)))]; + tensor add_53_epsilon_0 = const()[name = tensor("add_53_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_53 = batch_norm(beta = add_53_beta_0, epsilon = add_53_epsilon_0, gamma = add_53_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_105)[name = tensor("add_53")]; + tensor input_195 = silu(x = add_53)[name = tensor("input_195")]; + tensor var_579 = const()[name = tensor("op_579"), val = tensor([1, 1])]; + tensor var_581 = const()[name = tensor("op_581"), val = tensor([1, 1])]; + tensor hidden_states_45_pad_type_0 = const()[name = tensor("hidden_states_45_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_45_pad_0 = const()[name = tensor("hidden_states_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_45 = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias, dilations = var_581, groups = var_26, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = var_579, weight = decoder_up_blocks_3_resnets_1_conv2_weight, x = input_195)[name = tensor("hidden_states_45")]; + tensor var_584 = add(x = var_554, y = hidden_states_45)[name = tensor("op_584")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_108 = reshape(shape = reshape_108_shape_0, x = var_584)[name = tensor("reshape_108")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108)[name = tensor("reduce_mean_81")]; + tensor sub_54 = sub(x = reshape_108, y = reduce_mean_81)[name = tensor("sub_54")]; + tensor square_27 = square(x = sub_54)[name = tensor("square_27")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27)[name = tensor("reduce_mean_83")]; + tensor add_54_y_0 = const()[name = tensor("add_54_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_54 = add(x = reduce_mean_83, y = add_54_y_0)[name = tensor("add_54")]; + tensor sqrt_27 = sqrt(x = add_54)[name = tensor("sqrt_27")]; + tensor real_div_27 = real_div(x = sub_54, y = sqrt_27)[name = tensor("real_div_27")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_109 = reshape(shape = reshape_109_shape_0, x = real_div_27)[name = tensor("reshape_109")]; + tensor add_55_gamma_0 = const()[name = tensor("add_55_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197973760)))]; + tensor add_55_beta_0 = const()[name = tensor("add_55_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197974336)))]; + tensor add_55_epsilon_0 = const()[name = tensor("add_55_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_55 = batch_norm(beta = add_55_beta_0, epsilon = add_55_epsilon_0, gamma = add_55_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_109)[name = tensor("add_55")]; + tensor input_203 = silu(x = add_55)[name = tensor("input_203")]; + tensor var_597 = const()[name = tensor("op_597"), val = tensor([1, 1])]; + tensor var_599 = const()[name = tensor("op_599"), val = tensor([1, 1])]; + tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("custom")]; + tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_205 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias, dilations = var_599, groups = var_26, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = var_597, weight = decoder_up_blocks_3_resnets_2_conv1_weight, x = input_203)[name = tensor("input_205")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_112 = reshape(shape = reshape_112_shape_0, x = input_205)[name = tensor("reshape_112")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112)[name = tensor("reduce_mean_84")]; + tensor sub_56 = sub(x = reshape_112, y = reduce_mean_84)[name = tensor("sub_56")]; + tensor square_28 = square(x = sub_56)[name = tensor("square_28")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28)[name = tensor("reduce_mean_86")]; + tensor add_56_y_0 = const()[name = tensor("add_56_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_56 = add(x = reduce_mean_86, y = add_56_y_0)[name = tensor("add_56")]; + tensor sqrt_28 = sqrt(x = add_56)[name = tensor("sqrt_28")]; + tensor real_div_28 = real_div(x = sub_56, y = sqrt_28)[name = tensor("real_div_28")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_113 = reshape(shape = reshape_113_shape_0, x = real_div_28)[name = tensor("reshape_113")]; + tensor add_57_gamma_0 = const()[name = tensor("add_57_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197974912)))]; + tensor add_57_beta_0 = const()[name = tensor("add_57_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197975488)))]; + tensor add_57_epsilon_0 = const()[name = tensor("add_57_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_57 = batch_norm(beta = add_57_beta_0, epsilon = add_57_epsilon_0, gamma = add_57_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_113)[name = tensor("add_57")]; + tensor input_209 = silu(x = add_57)[name = tensor("input_209")]; + tensor var_609 = const()[name = tensor("op_609"), val = tensor([1, 1])]; + tensor var_611 = const()[name = tensor("op_611"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias, dilations = var_611, groups = var_26, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_609, weight = decoder_up_blocks_3_resnets_2_conv2_weight, x = input_209)[name = tensor("hidden_states")]; + tensor var_614 = add(x = var_584, y = hidden_states)[name = tensor("op_614")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_116 = reshape(shape = reshape_116_shape_0, x = var_614)[name = tensor("reshape_116")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116)[name = tensor("reduce_mean_87")]; + tensor sub_58 = sub(x = reshape_116, y = reduce_mean_87)[name = tensor("sub_58")]; + tensor square_29 = square(x = sub_58)[name = tensor("square_29")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29)[name = tensor("reduce_mean_89")]; + tensor add_58_y_0 = const()[name = tensor("add_58_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_58 = add(x = reduce_mean_89, y = add_58_y_0)[name = tensor("add_58")]; + tensor sqrt_29 = sqrt(x = add_58)[name = tensor("sqrt_29")]; + tensor real_div_29 = real_div(x = sub_58, y = sqrt_29)[name = tensor("real_div_29")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_117 = reshape(shape = reshape_117_shape_0, x = real_div_29)[name = tensor("reshape_117")]; + tensor add_59_gamma_0 = const()[name = tensor("add_59_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197976064)))]; + tensor add_59_beta_0 = const()[name = tensor("add_59_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197976640)))]; + tensor add_59_epsilon_0 = const()[name = tensor("add_59_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_59 = batch_norm(beta = add_59_beta_0, epsilon = add_59_epsilon_0, gamma = add_59_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_117)[name = tensor("add_59")]; + tensor input = silu(x = add_59)[name = tensor("input")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([1, 1])]; + tensor var_625 = const()[name = tensor("op_625"), val = tensor([1, 1])]; + tensor var_627_pad_type_0 = const()[name = tensor("op_627_pad_type_0"), val = tensor("custom")]; + tensor var_627_pad_0 = const()[name = tensor("op_627_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor image = conv(bias = decoder_conv_out_bias, dilations = var_625, groups = var_26, pad = var_627_pad_0, pad_type = var_627_pad_type_0, strides = var_623, weight = decoder_conv_out_weight, x = input)[name = tensor("op_627")]; + } -> (image); +} \ No newline at end of file diff --git a/compiled/VAEDecoder.mlmodelc/weights/weight.bin b/compiled/VAEDecoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..10a9bb7a56bab8bda6b91e312c1a30847f14d99e --- /dev/null +++ b/compiled/VAEDecoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ade814d6037fb5ba892963be1596c8e37852f96c399101401831f8c07e64bd2 +size 197977216 diff --git a/compiled/VAEEncoder.mlmodelc/analytics/coremldata.bin b/compiled/VAEEncoder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..722163a423cdb80707f6eb5ad91d80f1438bd128 --- /dev/null +++ b/compiled/VAEEncoder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e44297c358d64101602d9abfe4d6c9fb96ddb3b120f84fbb74001aa4312cf93 +size 207 diff --git a/compiled/VAEEncoder.mlmodelc/coremldata.bin b/compiled/VAEEncoder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..c8241a4d50e775e295089590466b9a8f9e8f0bf2 --- /dev/null +++ b/compiled/VAEEncoder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7269e365034d061c3ad035d9a5b7c38864d7db71bee6fb7545c97a4942a865f1 +size 783 diff --git a/compiled/VAEEncoder.mlmodelc/metadata.json b/compiled/VAEEncoder.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..216c974d24d18e3116bc89b4321848fb20963592 --- /dev/null +++ b/compiled/VAEEncoder.mlmodelc/metadata.json @@ -0,0 +1,75 @@ +[ + { + "shortDescription" : "Stable Diffusion generates images conditioned on text and\/or other images as input through the diffusion process. Please refer to https:\/\/arxiv.org\/abs\/2112.10752 for details.", + "metadataOutputVersion" : "3.0", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float32", + "formattedType" : "MultiArray (Float32)", + "shortDescription" : "The latent embeddings from the unet model from the input image.", + "shape" : "[]", + "name" : "latent", + "type" : "MultiArray" + } + ], + "version" : "diffusers\/stable-diffusion-xl-base-1.0", + "modelParameters" : [ + + ], + "author" : "Please refer to the Model Card available at huggingface.co\/diffusers\/stable-diffusion-xl-base-1.0", + "specificationVersion" : 7, + "storagePrecision" : "Float16", + "license" : "OpenRAIL (https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license)", + "mlProgramOperationTypeHistogram" : { + "Pad" : 3, + "Ios16.cast" : 1, + "Ios16.mul" : 2, + "Ios16.sqrt" : 22, + "Ios16.sub" : 22, + "Transpose" : 6, + "Ios16.conv" : 28, + "Ios16.add" : 34, + "Ios16.linear" : 4, + "Ios16.matmul" : 2, + "Ios16.realDiv" : 22, + "Ios16.reduceMean" : 44, + "Ios16.softmax" : 1, + "Ios16.batchNorm" : 21, + "Ios16.square" : 22, + "Ios16.reshape" : 49, + "Ios16.silu" : 21 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 3 × 1024 × 1024)", + "shortDescription" : "The input image to base the initial latents on normalized to range [-1, 1]", + "shape" : "[1, 3, 1024, 1024]", + "name" : "z", + "type" : "MultiArray" + } + ], + "userDefinedMetadata" : { + "com.github.apple.coremltools.version" : "7.0b1", + "com.github.apple.coremltools.source" : "torch==2.0.1+cu117" + }, + "generatedClassName" : "Stable_Diffusion_version_diffusers_stable_diffusion_xl_base_1_0_vae_encoder", + "method" : "predict" + } +] \ No newline at end of file diff --git a/compiled/VAEEncoder.mlmodelc/model.mil b/compiled/VAEEncoder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..f7ba6ef5d577f3010bf818be77b5a26f77063e7c --- /dev/null +++ b/compiled/VAEEncoder.mlmodelc/model.mil @@ -0,0 +1,740 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.33.4"}, {"coremlc-version", "1839.0.0"}, {"coremltools-component-torch", "2.0.1+cu117"}, {"coremltools-version", "7.0b1"}})] +{ + func main(tensor z) { + tensor var_15 = const()[name = tensor("op_15"), val = tensor(1)]; + tensor var_33 = const()[name = tensor("op_33"), val = tensor([1, 1])]; + tensor var_35 = const()[name = tensor("op_35"), val = tensor([1, 1])]; + tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("custom")]; + tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_conv_in_weight_to_fp16 = const()[name = tensor("encoder_conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor encoder_conv_in_bias_to_fp16 = const()[name = tensor("encoder_conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7040)))]; + tensor input_1_cast = conv(bias = encoder_conv_in_bias_to_fp16, dilations = var_35, groups = var_15, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_33, weight = encoder_conv_in_weight_to_fp16, x = z)[name = tensor("input_1_cast")]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_1_cast)[name = tensor("reshape_0_cast")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast)[name = tensor("reduce_mean_0_cast")]; + tensor sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast)[name = tensor("sub_0_cast")]; + tensor square_0_cast = square(x = sub_0_cast)[name = tensor("square_0_cast")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast)[name = tensor("reduce_mean_2_cast")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_0_cast = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast")]; + tensor sqrt_0_cast = sqrt(x = add_0_cast)[name = tensor("sqrt_0_cast")]; + tensor real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast)[name = tensor("real_div_0_cast")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast)[name = tensor("reshape_1_cast")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7360)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7680)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8000)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8320)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast)[name = tensor("add_1_cast")]; + tensor input_5_cast = silu(x = add_1_cast)[name = tensor("input_5_cast")]; + tensor var_54 = const()[name = tensor("op_54"), val = tensor([1, 1])]; + tensor var_56 = const()[name = tensor("op_56"), val = tensor([1, 1])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8640)))]; + tensor encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303616)))]; + tensor input_7_cast = conv(bias = encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_56, groups = var_15, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_54, weight = encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_5_cast)[name = tensor("input_7_cast")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_7_cast)[name = tensor("reshape_4_cast")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast)[name = tensor("reduce_mean_3_cast")]; + tensor sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast)[name = tensor("sub_2_cast")]; + tensor square_1_cast = square(x = sub_2_cast)[name = tensor("square_1_cast")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast)[name = tensor("reduce_mean_5_cast")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_2_cast = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast")]; + tensor sqrt_1_cast = sqrt(x = add_2_cast)[name = tensor("sqrt_1_cast")]; + tensor real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast)[name = tensor("real_div_1_cast")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast)[name = tensor("reshape_5_cast")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303936)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304256)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast)[name = tensor("add_3_cast")]; + tensor input_11_cast = silu(x = add_3_cast)[name = tensor("input_11_cast")]; + tensor var_66 = const()[name = tensor("op_66"), val = tensor([1, 1])]; + tensor var_68 = const()[name = tensor("op_68"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304576)))]; + tensor encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599552)))]; + tensor hidden_states_1_cast = conv(bias = encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_68, groups = var_15, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_66, weight = encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_11_cast)[name = tensor("hidden_states_1_cast")]; + tensor var_71_cast = add(x = input_1_cast, y = hidden_states_1_cast)[name = tensor("op_71_cast")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_8_cast = reshape(shape = reshape_8_shape_0, x = var_71_cast)[name = tensor("reshape_8_cast")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast)[name = tensor("reduce_mean_6_cast")]; + tensor sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast)[name = tensor("sub_4_cast")]; + tensor square_2_cast = square(x = sub_4_cast)[name = tensor("square_2_cast")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast)[name = tensor("reduce_mean_8_cast")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast")]; + tensor sqrt_2_cast = sqrt(x = add_4_cast)[name = tensor("sqrt_2_cast")]; + tensor real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast)[name = tensor("real_div_2_cast")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast)[name = tensor("reshape_9_cast")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599872)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600192)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast)[name = tensor("add_5_cast")]; + tensor input_19_cast = silu(x = add_5_cast)[name = tensor("input_19_cast")]; + tensor var_84 = const()[name = tensor("op_84"), val = tensor([1, 1])]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor([1, 1])]; + tensor input_21_pad_type_0 = const()[name = tensor("input_21_pad_type_0"), val = tensor("custom")]; + tensor input_21_pad_0 = const()[name = tensor("input_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600512)))]; + tensor encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(895488)))]; + tensor input_21_cast = conv(bias = encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_86, groups = var_15, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = var_84, weight = encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_19_cast)[name = tensor("input_21_cast")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_21_cast)[name = tensor("reshape_12_cast")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast)[name = tensor("reduce_mean_9_cast")]; + tensor sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast)[name = tensor("sub_6_cast")]; + tensor square_3_cast = square(x = sub_6_cast)[name = tensor("square_3_cast")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast)[name = tensor("reduce_mean_11_cast")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_6_cast = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast")]; + tensor sqrt_3_cast = sqrt(x = add_6_cast)[name = tensor("sqrt_3_cast")]; + tensor real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast)[name = tensor("real_div_3_cast")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast)[name = tensor("reshape_13_cast")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(895808)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896128)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast)[name = tensor("add_7_cast")]; + tensor input_25_cast = silu(x = add_7_cast)[name = tensor("input_25_cast")]; + tensor var_96 = const()[name = tensor("op_96"), val = tensor([1, 1])]; + tensor var_98 = const()[name = tensor("op_98"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896448)))]; + tensor encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191424)))]; + tensor hidden_states_3_cast = conv(bias = encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_98, groups = var_15, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_96, weight = encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_25_cast)[name = tensor("hidden_states_3_cast")]; + tensor var_101_cast = add(x = var_71_cast, y = hidden_states_3_cast)[name = tensor("op_101_cast")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_7_mode_0 = const()[name = tensor("hidden_states_7_mode_0"), val = tensor("constant")]; + tensor hidden_states_7_constant_val_0_to_fp16 = const()[name = tensor("hidden_states_7_constant_val_0_to_fp16"), val = tensor(0x0p+0)]; + tensor hidden_states_7_cast = pad(constant_val = hidden_states_7_constant_val_0_to_fp16, mode = hidden_states_7_mode_0, pad = hidden_states_7_pad_0, x = var_101_cast)[name = tensor("hidden_states_7_cast")]; + tensor var_109 = const()[name = tensor("op_109"), val = tensor([2, 2])]; + tensor var_111 = const()[name = tensor("op_111"), val = tensor([1, 1])]; + tensor input_29_pad_type_0 = const()[name = tensor("input_29_pad_type_0"), val = tensor("custom")]; + tensor input_29_pad_0 = const()[name = tensor("input_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191744)))]; + tensor encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1486720)))]; + tensor input_29_cast = conv(bias = encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_111, groups = var_15, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = var_109, weight = encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = hidden_states_7_cast)[name = tensor("input_29_cast")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_29_cast)[name = tensor("reshape_16_cast")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12_cast = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast)[name = tensor("reduce_mean_12_cast")]; + tensor sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast)[name = tensor("sub_8_cast")]; + tensor square_4_cast = square(x = sub_8_cast)[name = tensor("square_4_cast")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14_cast = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast)[name = tensor("reduce_mean_14_cast")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_8_cast = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast")]; + tensor sqrt_4_cast = sqrt(x = add_8_cast)[name = tensor("sqrt_4_cast")]; + tensor real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast)[name = tensor("real_div_4_cast")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast)[name = tensor("reshape_17_cast")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1487040)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1487360)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast)[name = tensor("add_9_cast")]; + tensor input_33_cast = silu(x = add_9_cast)[name = tensor("input_33_cast")]; + tensor var_131 = const()[name = tensor("op_131"), val = tensor([1, 1])]; + tensor var_133 = const()[name = tensor("op_133"), val = tensor([1, 1])]; + tensor input_35_pad_type_0 = const()[name = tensor("input_35_pad_type_0"), val = tensor("custom")]; + tensor input_35_pad_0 = const()[name = tensor("input_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1487680)))]; + tensor encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2077568)))]; + tensor input_35_cast = conv(bias = encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_133, groups = var_15, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_131, weight = encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_33_cast)[name = tensor("input_35_cast")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_20_cast = reshape(shape = reshape_20_shape_0, x = input_35_cast)[name = tensor("reshape_20_cast")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15_cast = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast)[name = tensor("reduce_mean_15_cast")]; + tensor sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast)[name = tensor("sub_10_cast")]; + tensor square_5_cast = square(x = sub_10_cast)[name = tensor("square_5_cast")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17_cast = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast)[name = tensor("reduce_mean_17_cast")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_10_cast = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast")]; + tensor sqrt_5_cast = sqrt(x = add_10_cast)[name = tensor("sqrt_5_cast")]; + tensor real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast)[name = tensor("real_div_5_cast")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast)[name = tensor("reshape_21_cast")]; + tensor add_11_mean_0_to_fp16 = const()[name = tensor("add_11_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2078144)))]; + tensor add_11_variance_0_to_fp16 = const()[name = tensor("add_11_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2078720)))]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2079296)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2079872)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast)[name = tensor("add_11_cast")]; + tensor input_39_cast = silu(x = add_11_cast)[name = tensor("input_39_cast")]; + tensor var_143 = const()[name = tensor("op_143"), val = tensor([1, 1])]; + tensor var_145 = const()[name = tensor("op_145"), val = tensor([1, 1])]; + tensor hidden_states_9_pad_type_0 = const()[name = tensor("hidden_states_9_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_9_pad_0 = const()[name = tensor("hidden_states_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2080448)))]; + tensor encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3260160)))]; + tensor hidden_states_9_cast = conv(bias = encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_145, groups = var_15, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_143, weight = encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_39_cast)[name = tensor("hidden_states_9_cast")]; + tensor var_150 = const()[name = tensor("op_150"), val = tensor([1, 1])]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([1, 1])]; + tensor input_tensor_1_pad_type_0 = const()[name = tensor("input_tensor_1_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_1_pad_0 = const()[name = tensor("input_tensor_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3260736)))]; + tensor encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3326336)))]; + tensor input_tensor_1_cast = conv(bias = encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_152, groups = var_15, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_150, weight = encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_29_cast)[name = tensor("input_tensor_1_cast")]; + tensor var_155_cast = add(x = input_tensor_1_cast, y = hidden_states_9_cast)[name = tensor("op_155_cast")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_24_cast = reshape(shape = reshape_24_shape_0, x = var_155_cast)[name = tensor("reshape_24_cast")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18_cast = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast)[name = tensor("reduce_mean_18_cast")]; + tensor sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast)[name = tensor("sub_12_cast")]; + tensor square_6_cast = square(x = sub_12_cast)[name = tensor("square_6_cast")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20_cast = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast)[name = tensor("reduce_mean_20_cast")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_12_cast = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast")]; + tensor sqrt_6_cast = sqrt(x = add_12_cast)[name = tensor("sqrt_6_cast")]; + tensor real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast)[name = tensor("real_div_6_cast")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast)[name = tensor("reshape_25_cast")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3326912)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3327488)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_25_cast)[name = tensor("add_13_cast")]; + tensor input_47_cast = silu(x = add_13_cast)[name = tensor("input_47_cast")]; + tensor var_168 = const()[name = tensor("op_168"), val = tensor([1, 1])]; + tensor var_170 = const()[name = tensor("op_170"), val = tensor([1, 1])]; + tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("custom")]; + tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3328064)))]; + tensor encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4507776)))]; + tensor input_49_cast = conv(bias = encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_170, groups = var_15, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = var_168, weight = encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_47_cast)[name = tensor("input_49_cast")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_49_cast)[name = tensor("reshape_28_cast")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21_cast = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast)[name = tensor("reduce_mean_21_cast")]; + tensor sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast)[name = tensor("sub_14_cast")]; + tensor square_7_cast = square(x = sub_14_cast)[name = tensor("square_7_cast")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23_cast = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast)[name = tensor("reduce_mean_23_cast")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_14_cast = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast")]; + tensor sqrt_7_cast = sqrt(x = add_14_cast)[name = tensor("sqrt_7_cast")]; + tensor real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast)[name = tensor("real_div_7_cast")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast)[name = tensor("reshape_29_cast")]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4508352)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4508928)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_29_cast)[name = tensor("add_15_cast")]; + tensor input_53_cast = silu(x = add_15_cast)[name = tensor("input_53_cast")]; + tensor var_180 = const()[name = tensor("op_180"), val = tensor([1, 1])]; + tensor var_182 = const()[name = tensor("op_182"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_type_0 = const()[name = tensor("hidden_states_11_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_11_pad_0 = const()[name = tensor("hidden_states_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4509504)))]; + tensor encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5689216)))]; + tensor hidden_states_11_cast = conv(bias = encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_182, groups = var_15, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_180, weight = encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_53_cast)[name = tensor("hidden_states_11_cast")]; + tensor var_185_cast = add(x = var_155_cast, y = hidden_states_11_cast)[name = tensor("op_185_cast")]; + tensor hidden_states_15_pad_0 = const()[name = tensor("hidden_states_15_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_15_mode_0 = const()[name = tensor("hidden_states_15_mode_0"), val = tensor("constant")]; + tensor hidden_states_15_constant_val_0_to_fp16 = const()[name = tensor("hidden_states_15_constant_val_0_to_fp16"), val = tensor(0x0p+0)]; + tensor hidden_states_15_cast = pad(constant_val = hidden_states_15_constant_val_0_to_fp16, mode = hidden_states_15_mode_0, pad = hidden_states_15_pad_0, x = var_185_cast)[name = tensor("hidden_states_15_cast")]; + tensor var_193 = const()[name = tensor("op_193"), val = tensor([2, 2])]; + tensor var_195 = const()[name = tensor("op_195"), val = tensor([1, 1])]; + tensor input_57_pad_type_0 = const()[name = tensor("input_57_pad_type_0"), val = tensor("custom")]; + tensor input_57_pad_0 = const()[name = tensor("input_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5689792)))]; + tensor encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6869504)))]; + tensor input_57_cast = conv(bias = encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_195, groups = var_15, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = var_193, weight = encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = hidden_states_15_cast)[name = tensor("input_57_cast")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 8, 256, 256])]; + tensor reshape_32_cast = reshape(shape = reshape_32_shape_0, x = input_57_cast)[name = tensor("reshape_32_cast")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24_cast = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast)[name = tensor("reduce_mean_24_cast")]; + tensor sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast)[name = tensor("sub_16_cast")]; + tensor square_8_cast = square(x = sub_16_cast)[name = tensor("square_8_cast")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26_cast = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast)[name = tensor("reduce_mean_26_cast")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_16_cast = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast")]; + tensor sqrt_8_cast = sqrt(x = add_16_cast)[name = tensor("sqrt_8_cast")]; + tensor real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast)[name = tensor("real_div_8_cast")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 256, 256, 256])]; + tensor reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast)[name = tensor("reshape_33_cast")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6870080)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6870656)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_33_cast)[name = tensor("add_17_cast")]; + tensor input_61_cast = silu(x = add_17_cast)[name = tensor("input_61_cast")]; + tensor var_215 = const()[name = tensor("op_215"), val = tensor([1, 1])]; + tensor var_217 = const()[name = tensor("op_217"), val = tensor([1, 1])]; + tensor input_63_pad_type_0 = const()[name = tensor("input_63_pad_type_0"), val = tensor("custom")]; + tensor input_63_pad_0 = const()[name = tensor("input_63_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6871232)))]; + tensor encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9230592)))]; + tensor input_63_cast = conv(bias = encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_217, groups = var_15, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_215, weight = encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_61_cast)[name = tensor("input_63_cast")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_36_cast = reshape(shape = reshape_36_shape_0, x = input_63_cast)[name = tensor("reshape_36_cast")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27_cast = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast)[name = tensor("reduce_mean_27_cast")]; + tensor sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast)[name = tensor("sub_18_cast")]; + tensor square_9_cast = square(x = sub_18_cast)[name = tensor("square_9_cast")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29_cast = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast)[name = tensor("reduce_mean_29_cast")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_18_cast = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast")]; + tensor sqrt_9_cast = sqrt(x = add_18_cast)[name = tensor("sqrt_9_cast")]; + tensor real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast)[name = tensor("real_div_9_cast")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast)[name = tensor("reshape_37_cast")]; + tensor add_19_mean_0_to_fp16 = const()[name = tensor("add_19_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9231680)))]; + tensor add_19_variance_0_to_fp16 = const()[name = tensor("add_19_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9232768)))]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9233856)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9234944)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_37_cast)[name = tensor("add_19_cast")]; + tensor input_67_cast = silu(x = add_19_cast)[name = tensor("input_67_cast")]; + tensor var_227 = const()[name = tensor("op_227"), val = tensor([1, 1])]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9236032)))]; + tensor encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13954688)))]; + tensor hidden_states_17_cast = conv(bias = encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_229, groups = var_15, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_227, weight = encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_67_cast)[name = tensor("hidden_states_17_cast")]; + tensor var_234 = const()[name = tensor("op_234"), val = tensor([1, 1])]; + tensor var_236 = const()[name = tensor("op_236"), val = tensor([1, 1])]; + tensor input_tensor_pad_type_0 = const()[name = tensor("input_tensor_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_pad_0 = const()[name = tensor("input_tensor_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13955776)))]; + tensor encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14217984)))]; + tensor input_tensor_cast = conv(bias = encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_236, groups = var_15, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_234, weight = encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_57_cast)[name = tensor("input_tensor_cast")]; + tensor var_239_cast = add(x = input_tensor_cast, y = hidden_states_17_cast)[name = tensor("op_239_cast")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_40_cast = reshape(shape = reshape_40_shape_0, x = var_239_cast)[name = tensor("reshape_40_cast")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30_cast = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast)[name = tensor("reduce_mean_30_cast")]; + tensor sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast)[name = tensor("sub_20_cast")]; + tensor square_10_cast = square(x = sub_20_cast)[name = tensor("square_10_cast")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32_cast = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast)[name = tensor("reduce_mean_32_cast")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_20_cast = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast")]; + tensor sqrt_10_cast = sqrt(x = add_20_cast)[name = tensor("sqrt_10_cast")]; + tensor real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast)[name = tensor("real_div_10_cast")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast)[name = tensor("reshape_41_cast")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14219072)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14220160)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_41_cast)[name = tensor("add_21_cast")]; + tensor input_75_cast = silu(x = add_21_cast)[name = tensor("input_75_cast")]; + tensor var_252 = const()[name = tensor("op_252"), val = tensor([1, 1])]; + tensor var_254 = const()[name = tensor("op_254"), val = tensor([1, 1])]; + tensor input_77_pad_type_0 = const()[name = tensor("input_77_pad_type_0"), val = tensor("custom")]; + tensor input_77_pad_0 = const()[name = tensor("input_77_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14221248)))]; + tensor encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18939904)))]; + tensor input_77_cast = conv(bias = encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_254, groups = var_15, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = var_252, weight = encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_75_cast)[name = tensor("input_77_cast")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_44_cast = reshape(shape = reshape_44_shape_0, x = input_77_cast)[name = tensor("reshape_44_cast")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33_cast = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast)[name = tensor("reduce_mean_33_cast")]; + tensor sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast)[name = tensor("sub_22_cast")]; + tensor square_11_cast = square(x = sub_22_cast)[name = tensor("square_11_cast")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35_cast = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast)[name = tensor("reduce_mean_35_cast")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_22_cast = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast")]; + tensor sqrt_11_cast = sqrt(x = add_22_cast)[name = tensor("sqrt_11_cast")]; + tensor real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast)[name = tensor("real_div_11_cast")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast)[name = tensor("reshape_45_cast")]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18940992)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18942080)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_45_cast)[name = tensor("add_23_cast")]; + tensor input_81_cast = silu(x = add_23_cast)[name = tensor("input_81_cast")]; + tensor var_264 = const()[name = tensor("op_264"), val = tensor([1, 1])]; + tensor var_266 = const()[name = tensor("op_266"), val = tensor([1, 1])]; + tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18943168)))]; + tensor encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23661824)))]; + tensor hidden_states_19_cast = conv(bias = encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_266, groups = var_15, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_264, weight = encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_81_cast)[name = tensor("hidden_states_19_cast")]; + tensor var_269_cast = add(x = var_239_cast, y = hidden_states_19_cast)[name = tensor("op_269_cast")]; + tensor hidden_states_23_pad_0 = const()[name = tensor("hidden_states_23_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_23_mode_0 = const()[name = tensor("hidden_states_23_mode_0"), val = tensor("constant")]; + tensor hidden_states_23_constant_val_0_to_fp16 = const()[name = tensor("hidden_states_23_constant_val_0_to_fp16"), val = tensor(0x0p+0)]; + tensor hidden_states_23_cast = pad(constant_val = hidden_states_23_constant_val_0_to_fp16, mode = hidden_states_23_mode_0, pad = hidden_states_23_pad_0, x = var_269_cast)[name = tensor("hidden_states_23_cast")]; + tensor var_277 = const()[name = tensor("op_277"), val = tensor([2, 2])]; + tensor var_279 = const()[name = tensor("op_279"), val = tensor([1, 1])]; + tensor input_85_pad_type_0 = const()[name = tensor("input_85_pad_type_0"), val = tensor("custom")]; + tensor input_85_pad_0 = const()[name = tensor("input_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23662912)))]; + tensor encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28381568)))]; + tensor input_85_cast = conv(bias = encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_279, groups = var_15, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_277, weight = encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("input_85_cast")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_48_cast = reshape(shape = reshape_48_shape_0, x = input_85_cast)[name = tensor("reshape_48_cast")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36_cast = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast)[name = tensor("reduce_mean_36_cast")]; + tensor sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast)[name = tensor("sub_24_cast")]; + tensor square_12_cast = square(x = sub_24_cast)[name = tensor("square_12_cast")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38_cast = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast)[name = tensor("reduce_mean_38_cast")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_24_cast = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast")]; + tensor sqrt_12_cast = sqrt(x = add_24_cast)[name = tensor("sqrt_12_cast")]; + tensor real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast)[name = tensor("real_div_12_cast")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast)[name = tensor("reshape_49_cast")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28382656)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28383744)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_49_cast)[name = tensor("add_25_cast")]; + tensor input_89_cast = silu(x = add_25_cast)[name = tensor("input_89_cast")]; + tensor var_296 = const()[name = tensor("op_296"), val = tensor([1, 1])]; + tensor var_298 = const()[name = tensor("op_298"), val = tensor([1, 1])]; + tensor input_91_pad_type_0 = const()[name = tensor("input_91_pad_type_0"), val = tensor("custom")]; + tensor input_91_pad_0 = const()[name = tensor("input_91_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28384832)))]; + tensor encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33103488)))]; + tensor input_91_cast = conv(bias = encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_298, groups = var_15, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = var_296, weight = encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_89_cast)[name = tensor("input_91_cast")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_91_cast)[name = tensor("reshape_52_cast")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39_cast = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast)[name = tensor("reduce_mean_39_cast")]; + tensor sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast)[name = tensor("sub_26_cast")]; + tensor square_13_cast = square(x = sub_26_cast)[name = tensor("square_13_cast")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41_cast = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast)[name = tensor("reduce_mean_41_cast")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_26_cast = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast")]; + tensor sqrt_13_cast = sqrt(x = add_26_cast)[name = tensor("sqrt_13_cast")]; + tensor real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast)[name = tensor("real_div_13_cast")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast)[name = tensor("reshape_53_cast")]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33104576)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33105664)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_53_cast)[name = tensor("add_27_cast")]; + tensor input_95_cast = silu(x = add_27_cast)[name = tensor("input_95_cast")]; + tensor var_308 = const()[name = tensor("op_308"), val = tensor([1, 1])]; + tensor var_310 = const()[name = tensor("op_310"), val = tensor([1, 1])]; + tensor hidden_states_25_pad_type_0 = const()[name = tensor("hidden_states_25_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_25_pad_0 = const()[name = tensor("hidden_states_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33106752)))]; + tensor encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37825408)))]; + tensor hidden_states_25_cast = conv(bias = encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_310, groups = var_15, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_308, weight = encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_95_cast)[name = tensor("hidden_states_25_cast")]; + tensor var_313_cast = add(x = input_85_cast, y = hidden_states_25_cast)[name = tensor("op_313_cast")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_56_cast = reshape(shape = reshape_56_shape_0, x = var_313_cast)[name = tensor("reshape_56_cast")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42_cast = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast)[name = tensor("reduce_mean_42_cast")]; + tensor sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast)[name = tensor("sub_28_cast")]; + tensor square_14_cast = square(x = sub_28_cast)[name = tensor("square_14_cast")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44_cast = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast)[name = tensor("reduce_mean_44_cast")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_28_cast = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast")]; + tensor sqrt_14_cast = sqrt(x = add_28_cast)[name = tensor("sqrt_14_cast")]; + tensor real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast)[name = tensor("real_div_14_cast")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast)[name = tensor("reshape_57_cast")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37826496)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37827584)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_57_cast)[name = tensor("add_29_cast")]; + tensor input_103_cast = silu(x = add_29_cast)[name = tensor("input_103_cast")]; + tensor var_326 = const()[name = tensor("op_326"), val = tensor([1, 1])]; + tensor var_328 = const()[name = tensor("op_328"), val = tensor([1, 1])]; + tensor input_105_pad_type_0 = const()[name = tensor("input_105_pad_type_0"), val = tensor("custom")]; + tensor input_105_pad_0 = const()[name = tensor("input_105_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37828672)))]; + tensor encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42547328)))]; + tensor input_105_cast = conv(bias = encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_328, groups = var_15, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = var_326, weight = encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_103_cast)[name = tensor("input_105_cast")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_60_cast = reshape(shape = reshape_60_shape_0, x = input_105_cast)[name = tensor("reshape_60_cast")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45_cast = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast)[name = tensor("reduce_mean_45_cast")]; + tensor sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast)[name = tensor("sub_30_cast")]; + tensor square_15_cast = square(x = sub_30_cast)[name = tensor("square_15_cast")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47_cast = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast)[name = tensor("reduce_mean_47_cast")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_30_cast = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast")]; + tensor sqrt_15_cast = sqrt(x = add_30_cast)[name = tensor("sqrt_15_cast")]; + tensor real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast)[name = tensor("real_div_15_cast")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast)[name = tensor("reshape_61_cast")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42548416)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42549504)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_61_cast)[name = tensor("add_31_cast")]; + tensor input_109_cast = silu(x = add_31_cast)[name = tensor("input_109_cast")]; + tensor var_338 = const()[name = tensor("op_338"), val = tensor([1, 1])]; + tensor var_340 = const()[name = tensor("op_340"), val = tensor([1, 1])]; + tensor hidden_states_27_pad_type_0 = const()[name = tensor("hidden_states_27_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_27_pad_0 = const()[name = tensor("hidden_states_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42550592)))]; + tensor encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47269248)))]; + tensor hidden_states_27_cast = conv(bias = encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_340, groups = var_15, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_338, weight = encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_109_cast)[name = tensor("hidden_states_27_cast")]; + tensor var_343_cast = add(x = var_313_cast, y = hidden_states_27_cast)[name = tensor("op_343_cast")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_64_cast = reshape(shape = reshape_64_shape_0, x = var_343_cast)[name = tensor("reshape_64_cast")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48_cast = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast)[name = tensor("reduce_mean_48_cast")]; + tensor sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast)[name = tensor("sub_32_cast")]; + tensor square_16_cast = square(x = sub_32_cast)[name = tensor("square_16_cast")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50_cast = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast)[name = tensor("reduce_mean_50_cast")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_32_cast = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast")]; + tensor sqrt_16_cast = sqrt(x = add_32_cast)[name = tensor("sqrt_16_cast")]; + tensor real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast)[name = tensor("real_div_16_cast")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast)[name = tensor("reshape_65_cast")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47270336)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47271424)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_65_cast)[name = tensor("add_33_cast")]; + tensor input_117_cast = silu(x = add_33_cast)[name = tensor("input_117_cast")]; + tensor var_362 = const()[name = tensor("op_362"), val = tensor([1, 1])]; + tensor var_364 = const()[name = tensor("op_364"), val = tensor([1, 1])]; + tensor input_119_pad_type_0 = const()[name = tensor("input_119_pad_type_0"), val = tensor("custom")]; + tensor input_119_pad_0 = const()[name = tensor("input_119_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47272512)))]; + tensor encoder_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51991168)))]; + tensor input_119_cast = conv(bias = encoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_364, groups = var_15, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = var_362, weight = encoder_mid_block_resnets_0_conv1_weight_to_fp16, x = input_117_cast)[name = tensor("input_119_cast")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_68_cast = reshape(shape = reshape_68_shape_0, x = input_119_cast)[name = tensor("reshape_68_cast")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51_cast = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast)[name = tensor("reduce_mean_51_cast")]; + tensor sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast)[name = tensor("sub_34_cast")]; + tensor square_17_cast = square(x = sub_34_cast)[name = tensor("square_17_cast")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53_cast = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast)[name = tensor("reduce_mean_53_cast")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_34_cast = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast")]; + tensor sqrt_17_cast = sqrt(x = add_34_cast)[name = tensor("sqrt_17_cast")]; + tensor real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast)[name = tensor("real_div_17_cast")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast)[name = tensor("reshape_69_cast")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51992256)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51993344)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_69_cast)[name = tensor("add_35_cast")]; + tensor input_123_cast = silu(x = add_35_cast)[name = tensor("input_123_cast")]; + tensor var_374 = const()[name = tensor("op_374"), val = tensor([1, 1])]; + tensor var_376 = const()[name = tensor("op_376"), val = tensor([1, 1])]; + tensor hidden_states_29_pad_type_0 = const()[name = tensor("hidden_states_29_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_29_pad_0 = const()[name = tensor("hidden_states_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51994432)))]; + tensor encoder_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56713088)))]; + tensor hidden_states_29_cast = conv(bias = encoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_376, groups = var_15, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_374, weight = encoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_123_cast)[name = tensor("hidden_states_29_cast")]; + tensor var_379_cast = add(x = var_343_cast, y = hidden_states_29_cast)[name = tensor("op_379_cast")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 16, 16384])]; + tensor reshape_72_cast = reshape(shape = reshape_72_shape_0, x = var_379_cast)[name = tensor("reshape_72_cast")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast)[name = tensor("reduce_mean_54_cast")]; + tensor sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast)[name = tensor("sub_36_cast")]; + tensor square_18_cast = square(x = sub_36_cast)[name = tensor("square_18_cast")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast)[name = tensor("reduce_mean_56_cast")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_36_cast = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast")]; + tensor sqrt_18_cast = sqrt(x = add_36_cast)[name = tensor("sqrt_18_cast")]; + tensor real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast)[name = tensor("real_div_18_cast")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 512, 16384])]; + tensor reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast)[name = tensor("reshape_73_cast")]; + tensor reshape_74_to_fp16 = const()[name = tensor("reshape_74_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56714176)))]; + tensor mul_18_cast = mul(x = reshape_73_cast, y = reshape_74_to_fp16)[name = tensor("mul_18_cast")]; + tensor reshape_75_to_fp16 = const()[name = tensor("reshape_75_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56715264)))]; + tensor add_37_cast = add(x = mul_18_cast, y = reshape_75_to_fp16)[name = tensor("add_37_cast")]; + tensor input_129_perm_0 = const()[name = tensor("input_129_perm_0"), val = tensor([0, 2, 1])]; + tensor encoder_mid_block_attentions_0_to_q_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56716352)))]; + tensor encoder_mid_block_attentions_0_to_q_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57240704)))]; + tensor transpose_9 = transpose(perm = input_129_perm_0, x = add_37_cast)[name = tensor("transpose_9")]; + tensor query_1_cast = linear(bias = encoder_mid_block_attentions_0_to_q_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_q_weight_to_fp16, x = transpose_9)[name = tensor("query_1_cast")]; + tensor encoder_mid_block_attentions_0_to_k_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57241792)))]; + tensor encoder_mid_block_attentions_0_to_k_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57766144)))]; + tensor key_1_cast = linear(bias = encoder_mid_block_attentions_0_to_k_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_k_weight_to_fp16, x = transpose_9)[name = tensor("key_1_cast")]; + tensor encoder_mid_block_attentions_0_to_v_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57767232)))]; + tensor encoder_mid_block_attentions_0_to_v_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58291584)))]; + tensor value_1_cast = linear(bias = encoder_mid_block_attentions_0_to_v_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_v_weight_to_fp16, x = transpose_9)[name = tensor("value_1_cast")]; + tensor var_420 = const()[name = tensor("op_420"), val = tensor([1, -1, 1, 512])]; + tensor var_421_cast = reshape(shape = var_420, x = query_1_cast)[name = tensor("op_421_cast")]; + tensor var_423 = const()[name = tensor("op_423"), val = tensor([1, -1, 1, 512])]; + tensor var_424_cast = reshape(shape = var_423, x = key_1_cast)[name = tensor("op_424_cast")]; + tensor var_426 = const()[name = tensor("op_426"), val = tensor([1, -1, 1, 512])]; + tensor var_427_cast = reshape(shape = var_426, x = value_1_cast)[name = tensor("op_427_cast")]; + tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor mul_19_y_0_to_fp16 = const()[name = tensor("mul_19_y_0_to_fp16"), val = tensor(0x1.6ap-5)]; + tensor mul_19_cast = mul(x = var_421_cast, y = mul_19_y_0_to_fp16)[name = tensor("mul_19_cast")]; + tensor matmul_0_transpose_y_0 = const()[name = tensor("matmul_0_transpose_y_0"), val = tensor(true)]; + tensor matmul_0_transpose_x_0 = const()[name = tensor("matmul_0_transpose_x_0"), val = tensor(false)]; + tensor transpose_2_perm_0 = const()[name = tensor("transpose_2_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_3_perm_0 = const()[name = tensor("transpose_3_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_6 = transpose(perm = transpose_3_perm_0, x = var_424_cast)[name = tensor("transpose_6")]; + tensor transpose_7 = transpose(perm = transpose_2_perm_0, x = mul_19_cast)[name = tensor("transpose_7")]; + tensor matmul_0_cast = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_7, y = transpose_6)[name = tensor("matmul_0_cast")]; + tensor softmax_0_axis_0 = const()[name = tensor("softmax_0_axis_0"), val = tensor(-1)]; + tensor softmax_0_cast = softmax(axis = softmax_0_axis_0, x = matmul_0_cast)[name = tensor("softmax_0_cast")]; + tensor hidden_states_35_transpose_x_0 = const()[name = tensor("hidden_states_35_transpose_x_0"), val = tensor(false)]; + tensor hidden_states_35_transpose_y_0 = const()[name = tensor("hidden_states_35_transpose_y_0"), val = tensor(false)]; + tensor transpose_8 = transpose(perm = value_perm_0, x = var_427_cast)[name = tensor("transpose_8")]; + tensor hidden_states_35_cast = matmul(transpose_x = hidden_states_35_transpose_x_0, transpose_y = hidden_states_35_transpose_y_0, x = softmax_0_cast, y = transpose_8)[name = tensor("hidden_states_35_cast")]; + tensor var_430_perm_0 = const()[name = tensor("op_430_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_434 = const()[name = tensor("op_434"), val = tensor([1, -1, 512])]; + tensor transpose_5 = transpose(perm = var_430_perm_0, x = hidden_states_35_cast)[name = tensor("transpose_5")]; + tensor hidden_states_37_cast = reshape(shape = var_434, x = transpose_5)[name = tensor("hidden_states_37_cast")]; + tensor encoder_mid_block_attentions_0_to_out_0_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58292672)))]; + tensor encoder_mid_block_attentions_0_to_out_0_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58817024)))]; + tensor input_133_cast = linear(bias = encoder_mid_block_attentions_0_to_out_0_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_out_0_weight_to_fp16, x = hidden_states_37_cast)[name = tensor("input_133_cast")]; + tensor var_441_perm_0 = const()[name = tensor("op_441_perm_0"), val = tensor([0, -1, -2])]; + tensor var_442 = const()[name = tensor("op_442"), val = tensor([1, 512, 128, 128])]; + tensor transpose_4 = transpose(perm = var_441_perm_0, x = input_133_cast)[name = tensor("transpose_4")]; + tensor hidden_states_41_cast = reshape(shape = var_442, x = transpose_4)[name = tensor("hidden_states_41_cast")]; + tensor hidden_states_43_cast = add(x = hidden_states_41_cast, y = var_379_cast)[name = tensor("hidden_states_43_cast")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_76_cast = reshape(shape = reshape_76_shape_0, x = hidden_states_43_cast)[name = tensor("reshape_76_cast")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57_cast = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast)[name = tensor("reduce_mean_57_cast")]; + tensor sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast)[name = tensor("sub_38_cast")]; + tensor square_19_cast = square(x = sub_38_cast)[name = tensor("square_19_cast")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59_cast = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast)[name = tensor("reduce_mean_59_cast")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_38_cast = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast")]; + tensor sqrt_19_cast = sqrt(x = add_38_cast)[name = tensor("sqrt_19_cast")]; + tensor real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast)[name = tensor("real_div_19_cast")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast)[name = tensor("reshape_77_cast")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58818112)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58819200)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_77_cast)[name = tensor("add_39_cast")]; + tensor input_139_cast = silu(x = add_39_cast)[name = tensor("input_139_cast")]; + tensor var_457 = const()[name = tensor("op_457"), val = tensor([1, 1])]; + tensor var_459 = const()[name = tensor("op_459"), val = tensor([1, 1])]; + tensor input_141_pad_type_0 = const()[name = tensor("input_141_pad_type_0"), val = tensor("custom")]; + tensor input_141_pad_0 = const()[name = tensor("input_141_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58820288)))]; + tensor encoder_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63538944)))]; + tensor input_141_cast = conv(bias = encoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_459, groups = var_15, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = var_457, weight = encoder_mid_block_resnets_1_conv1_weight_to_fp16, x = input_139_cast)[name = tensor("input_141_cast")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_141_cast)[name = tensor("reshape_80_cast")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60_cast = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast)[name = tensor("reduce_mean_60_cast")]; + tensor sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast)[name = tensor("sub_40_cast")]; + tensor square_20_cast = square(x = sub_40_cast)[name = tensor("square_20_cast")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62_cast = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast)[name = tensor("reduce_mean_62_cast")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_40_cast = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast")]; + tensor sqrt_20_cast = sqrt(x = add_40_cast)[name = tensor("sqrt_20_cast")]; + tensor real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast)[name = tensor("real_div_20_cast")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast)[name = tensor("reshape_81_cast")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63540032)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63541120)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_81_cast)[name = tensor("add_41_cast")]; + tensor input_145_cast = silu(x = add_41_cast)[name = tensor("input_145_cast")]; + tensor var_469 = const()[name = tensor("op_469"), val = tensor([1, 1])]; + tensor var_471 = const()[name = tensor("op_471"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63542208)))]; + tensor encoder_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68260864)))]; + tensor hidden_states_cast = conv(bias = encoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_471, groups = var_15, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_469, weight = encoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_145_cast)[name = tensor("hidden_states_cast")]; + tensor var_474_cast = add(x = hidden_states_43_cast, y = hidden_states_cast)[name = tensor("op_474_cast")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_84_cast = reshape(shape = reshape_84_shape_0, x = var_474_cast)[name = tensor("reshape_84_cast")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63_cast = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast)[name = tensor("reduce_mean_63_cast")]; + tensor sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast)[name = tensor("sub_42_cast")]; + tensor square_21_cast = square(x = sub_42_cast)[name = tensor("square_21_cast")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65_cast = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast)[name = tensor("reduce_mean_65_cast")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_42_cast = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast")]; + tensor sqrt_21_cast = sqrt(x = add_42_cast)[name = tensor("sqrt_21_cast")]; + tensor real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast)[name = tensor("real_div_21_cast")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast)[name = tensor("reshape_85_cast")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68261952)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68263040)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_85_cast)[name = tensor("add_43_cast")]; + tensor input_153_cast = silu(x = add_43_cast)[name = tensor("input_153_cast")]; + tensor var_483 = const()[name = tensor("op_483"), val = tensor([1, 1])]; + tensor var_485 = const()[name = tensor("op_485"), val = tensor([1, 1])]; + tensor input_pad_type_0 = const()[name = tensor("input_pad_type_0"), val = tensor("custom")]; + tensor input_pad_0 = const()[name = tensor("input_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_conv_out_weight_to_fp16 = const()[name = tensor("encoder_conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68264128)))]; + tensor encoder_conv_out_bias_to_fp16 = const()[name = tensor("encoder_conv_out_bias_to_fp16"), val = tensor([0x1.c88p-7, -0x1.04cp-4, 0x1.944p-3, 0x1.d9cp-3, 0x1.e78p-3, 0x1.78cp-5, 0x1.bb8p-5, -0x1.824p-3])]; + tensor input_cast = conv(bias = encoder_conv_out_bias_to_fp16, dilations = var_485, groups = var_15, pad = input_pad_0, pad_type = input_pad_type_0, strides = var_483, weight = encoder_conv_out_weight_to_fp16, x = input_153_cast)[name = tensor("input_cast")]; + tensor var_491 = const()[name = tensor("op_491"), val = tensor(1)]; + tensor var_494 = const()[name = tensor("op_494"), val = tensor([1, 1])]; + tensor var_496 = const()[name = tensor("op_496"), val = tensor([1, 1])]; + tensor var_498_pad_type_0 = const()[name = tensor("op_498_pad_type_0"), val = tensor("custom")]; + tensor var_498_pad_0 = const()[name = tensor("op_498_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor quant_conv_weight_to_fp16 = const()[name = tensor("quant_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68337920)))]; + tensor quant_conv_bias_to_fp16 = const()[name = tensor("quant_conv_bias_to_fp16"), val = tensor([0x1.f48p-4, 0x1.088p-4, -0x1.e48p-3, -0x1.bf8p-2, -0x1.56cp+4, -0x1.598p+4, -0x1.62p+4, -0x1.664p+4])]; + tensor var_498_cast = conv(bias = quant_conv_bias_to_fp16, dilations = var_496, groups = var_491, pad = var_498_pad_0, pad_type = var_498_pad_type_0, strides = var_494, weight = quant_conv_weight_to_fp16, x = input_cast)[name = tensor("op_498_cast")]; + tensor var_498_cast_to_fp32_dtype_0 = const()[name = tensor("op_498_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor latent = cast(dtype = var_498_cast_to_fp32_dtype_0, x = var_498_cast)[name = tensor("cast_29")]; + } -> (latent); +} \ No newline at end of file diff --git a/compiled/VAEEncoder.mlmodelc/weights/weight.bin b/compiled/VAEEncoder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..b65da487e8950ca2002120fefa9387de7c571e32 --- /dev/null +++ b/compiled/VAEEncoder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:329f708df0bae1990a1886007b5ae56cfd9a44e7091e8f822db907a9fc411858 +size 68338112 diff --git a/compiled/merges.txt b/compiled/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..bbfec752c9a675946c6dce106def6f35c882dcc2 --- /dev/null +++ b/compiled/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or +n e +ð Ł +r a +h a +d e +o l +v e +s i +u r +a l +s e +' s +u n +d i +b e +l a +w h +o o +d ay +e n +m a +n o +l e +t o +ou r +i r +g h +w it +i t +y o +a s +s p +th is +t s +at i +yo u +wit h +a d +i s +a b +l y +w e +th e +t e +a s +a g +v i +p p +s u +h o +m y +. . +b u +c om +s e +er s +m e +m e +al l +c on +m o +k e +g e +ou t +en t +c o +f e +v er +a r +f ro +a u +p o +c e +gh t +ar e +s s +fro m +c h +t r +ou n +on e +b y +d o +t h +w or +er e +k e +p ro +f or +d s +b o +t a +w e +g o +h e +t er +in g +d e +b e +ati on +m or +a y +e x +il l +p e +k s +s c +l u +f u +q u +v er +ðŁ ĺ +j u +m u +at e +an d +v e +k ing +m ar +o p +h i +.. . +p re +a d +r u +th at +j o +o f +c e +ne w +a m +a p +g re +s s +d u +no w +y e +t ing +y our +it y +n i +c i +p ar +g u +f i +a f +p er +t er +u p +s o +g i +on s +g r +g e +b r +p l +' t +m i +in e +we e +b i +u s +sh o +ha ve +to day +a v +m an +en t +ac k +ur e +ou r +â Ģ +c u +l d +lo o +i m +ic e +s om +f in +re d +re n +oo d +w as +ti on +p i +i r +th er +t y +p h +ar d +e c +! ! +m on +mor e +w ill +t ra +c an +c ol +p u +t e +w n +m b +s o +it i +ju st +n ing +h ere +t u +p a +p r +bu t +wh at +al ly +f ir +m in +c a +an t +s a +t ed +e v +m ent +f a +ge t +am e +ab out +g ra +no t +ha pp +ay s +m an +h is +ti me +li ke +g h +ha s +th an +lo ve +ar t +st e +d ing +h e +c re +w s +w at +d er +it e +s er +ac e +ag e +en d +st r +a w +st or +r e +c ar +el l +al l +p s +f ri +p ho +p or +d o +a k +w i +f re +wh o +sh i +b oo +s on +el l +wh en +il l +ho w +gre at +w in +e l +b l +s si +al i +som e +ðŁ Ĵ +t on +d er +le s +p la +ï ¸ +e d +s ch +h u +on g +d on +k i +s h +an n +c or +. . +oun d +a z +in e +ar y +fu l +st u +ou ld +st i +g o +se e +ab le +ar s +l l +m is +b er +c k +w a +en ts +n o +si g +f e +fir st +e t +sp e +ac k +i f +ou s +' m +st er +a pp +an g +an ce +an s +g ood +b re +e ver +the y +t ic +com e +of f +b ack +as e +ing s +ol d +i ght +f o +h er +happ y +p ic +it s +v ing +u s +m at +h om +d y +e m +s k +y ing +the ir +le d +r y +u l +h ar +c k +t on +on al +h el +r ic +b ir +vi e +w ay +t ri +d a +p le +b ro +st o +oo l +ni ght +tr u +b a +re ad +re s +ye ar +f r +t or +al s +c oun +c la +t ure +v el +at ed +le c +en d +th ing +v o +ic i +be st +c an +wor k +la st +af ter +en ce +p ri +p e +e s +i l +âĢ ¦ +d re +y s +o ver +i es +ðŁ ij +com m +t w +in k +s un +c l +li fe +t t +a ch +l and +s y +t re +t al +p ol +s m +du c +s al +f t +' re +ch e +w ar +t ur +ati ons +ac h +m s +il e +p m +ou gh +at e +st ar +wee k +! !! +c lu +th ere +n er +t om +s el +ï¸ ı +wor ld +v es +c am +go t +in ter +of f +u m +ton ight +o ther +h ou +loo k +j e +i d +si on +be au +at t +el i +or t +re c +f f +st er +su pp +g en +be en +il y +te am +m m +i c +pe op +it t +at s +on ly +mb er +en g +b ri +m p +k now +b ur +b ar +in s +lo w +sh e +ro w +â Ŀ +t ro +peop le +vi a +lo w +ag a +be t +x t +f ac +ch ar +e ar +w al +s en +f am +b le +n ati +is h +n or +g ame +li ve +s co +le y +d on +ic k +b all +ver y +the se +p an +i a +at ing +c r +a re +g ir +ma ke +st re +sho w +. 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