| program(1.0) | |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] | |
| { | |
| func main<ios17>(tensor<int32, [1, 512]> attention_mask, tensor<int32, [1, 512]> input_ids) { | |
| tensor<fp32, [50265, 768]> text_branch_embeddings_word_embeddings_weight = const()[name = tensor<string, []>("text_branch_embeddings_word_embeddings_weight"), val = tensor<fp32, [50265, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; | |
| tensor<fp32, [514, 768]> text_branch_embeddings_position_embeddings_weight = const()[name = tensor<string, []>("text_branch_embeddings_position_embeddings_weight"), val = tensor<fp32, [514, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154414208)))]; | |
| tensor<fp32, [768]> text_branch_embeddings_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_embeddings_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155993280)))]; | |
| tensor<fp32, [768]> text_branch_embeddings_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_embeddings_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155996416)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_0_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_0_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(155999552)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_0_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_0_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156002688)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_0_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_0_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158362048)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_0_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_0_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(158365184)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_0_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_0_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160724544)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_0_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_0_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(160727680)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_0_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_0_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163087040)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_0_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_0_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(163090176)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_0_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_0_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165449536)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_0_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_0_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165452672)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_0_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_0_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165455808)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_0_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_0_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(165468160)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_0_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_0_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174905408)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_0_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_0_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(174908544)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_0_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_0_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184345792)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_0_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_0_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184348928)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_1_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_1_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184352064)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_1_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_1_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184355200)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_1_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_1_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186714560)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_1_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_1_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186717696)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_1_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_1_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189077056)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_1_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_1_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(189080192)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_1_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_1_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191439552)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_1_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_1_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(191442688)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_1_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_1_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(193802048)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_1_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_1_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(193805184)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_1_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_1_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(193808320)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_1_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_1_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(193820672)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_1_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_1_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203257920)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_1_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_1_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(203261056)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_1_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_1_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212698304)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_1_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_1_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212701440)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_2_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_2_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212704576)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_2_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_2_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(212707712)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_2_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_2_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215067072)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_2_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_2_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(215070208)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_2_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_2_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217429568)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_2_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_2_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(217432704)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_2_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_2_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219792064)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_2_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_2_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(219795200)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_2_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_2_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222154560)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_2_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_2_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222157696)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_2_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_2_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222160832)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_2_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_2_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222173184)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_2_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_2_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231610432)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_2_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_2_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(231613568)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_2_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_2_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(241050816)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_2_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_2_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(241053952)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_3_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_3_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(241057088)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_3_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_3_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(241060224)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_3_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_3_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243419584)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_3_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_3_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(243422720)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_3_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_3_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(245782080)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_3_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_3_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(245785216)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_3_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_3_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248144576)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_3_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_3_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(248147712)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_3_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_3_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250507072)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_3_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_3_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250510208)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_3_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_3_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250513344)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_3_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_3_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(250525696)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_3_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_3_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259962944)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_3_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_3_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(259966080)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_3_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_3_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269403328)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_3_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_3_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269406464)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_4_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_4_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269409600)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_4_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_4_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(269412736)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_4_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_4_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271772096)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_4_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_4_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271775232)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_4_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_4_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274134592)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_4_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_4_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(274137728)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_4_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_4_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(276497088)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_4_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_4_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(276500224)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_4_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_4_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(278859584)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_4_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_4_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(278862720)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_4_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_4_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(278865856)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_4_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_4_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(278878208)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_4_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_4_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288315456)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_4_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_4_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(288318592)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_4_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_4_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297755840)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_4_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_4_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297758976)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_5_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_5_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297762112)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_5_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_5_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(297765248)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_5_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_5_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300124608)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_5_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_5_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(300127744)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_5_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_5_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302487104)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_5_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_5_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302490240)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_5_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_5_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304849600)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_5_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_5_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304852736)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_5_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_5_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307212096)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_5_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_5_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307215232)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_5_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_5_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307218368)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_5_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_5_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307230720)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_5_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_5_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316667968)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_5_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_5_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316671104)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_5_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_5_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326108352)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_5_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_5_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326111488)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_6_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_6_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326114624)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_6_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_6_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(326117760)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_6_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_6_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328477120)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_6_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_6_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(328480256)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_6_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_6_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330839616)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_6_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_6_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(330842752)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_6_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_6_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(333202112)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_6_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_6_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(333205248)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_6_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_6_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335564608)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_6_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_6_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335567744)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_6_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_6_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335570880)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_6_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_6_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(335583232)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_6_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_6_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(345020480)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_6_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_6_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(345023616)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_6_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_6_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(354460864)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_6_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_6_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(354464000)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_7_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_7_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(354467136)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_7_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_7_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(354470272)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_7_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_7_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356829632)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_7_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_7_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(356832768)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_7_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_7_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359192128)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_7_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_7_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(359195264)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_7_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_7_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(361554624)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_7_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_7_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(361557760)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_7_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_7_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(363917120)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_7_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_7_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(363920256)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_7_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_7_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(363923392)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_7_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_7_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(363935744)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_7_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_7_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(373372992)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_7_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_7_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(373376128)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_7_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_7_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382813376)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_7_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_7_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382816512)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_8_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_8_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382819648)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_8_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_8_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(382822784)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_8_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_8_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385182144)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_8_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_8_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(385185280)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_8_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_8_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387544640)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_8_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_8_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387547776)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_8_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_8_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389907136)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_8_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_8_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389910272)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_8_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_8_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(392269632)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_8_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_8_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(392272768)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_8_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_8_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(392275904)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_8_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_8_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(392288256)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_8_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_8_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(401725504)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_8_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_8_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(401728640)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_8_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_8_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411165888)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_8_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_8_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411169024)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_9_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_9_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411172160)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_9_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_9_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(411175296)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_9_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_9_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(413534656)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_9_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_9_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(413537792)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_9_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_9_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415897152)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_9_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_9_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(415900288)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_9_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_9_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(418259648)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_9_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_9_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(418262784)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_9_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_9_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(420622144)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_9_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_9_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(420625280)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_9_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_9_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(420628416)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_9_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_9_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(420640768)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_9_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_9_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430078016)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_9_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_9_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(430081152)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_9_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_9_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(439518400)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_9_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_9_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(439521536)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_10_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_10_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(439524672)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_10_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_10_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(439527808)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_10_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_10_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441887168)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_10_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_10_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(441890304)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_10_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_10_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444249664)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_10_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_10_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(444252800)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_10_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_10_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446612160)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_10_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_10_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(446615296)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_10_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_10_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448974656)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_10_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_10_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448977792)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_10_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_10_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448980928)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_10_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_10_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448993280)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_10_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_10_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458430528)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_10_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_10_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(458433664)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_10_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_10_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467870912)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_10_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_10_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467874048)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_11_attention_self_query_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_11_attention_self_query_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467877184)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_11_attention_self_query_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_11_attention_self_query_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(467880320)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_11_attention_self_key_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_11_attention_self_key_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470239680)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_11_attention_self_key_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_11_attention_self_key_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(470242816)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_11_attention_self_value_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_11_attention_self_value_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(472602176)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_11_attention_self_value_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_11_attention_self_value_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(472605312)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_11_attention_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_11_attention_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474964672)))]; | |
| tensor<fp32, [768, 768]> text_branch_encoder_layer_11_attention_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_11_attention_output_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(474967808)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_11_attention_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_11_attention_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477327168)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_11_attention_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_11_attention_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477330304)))]; | |
| tensor<fp32, [3072]> text_branch_encoder_layer_11_intermediate_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_11_intermediate_dense_bias"), val = tensor<fp32, [3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477333440)))]; | |
| tensor<fp32, [3072, 768]> text_branch_encoder_layer_11_intermediate_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_11_intermediate_dense_weight"), val = tensor<fp32, [3072, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477345792)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_11_output_dense_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_11_output_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486783040)))]; | |
| tensor<fp32, [768, 3072]> text_branch_encoder_layer_11_output_dense_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_11_output_dense_weight"), val = tensor<fp32, [768, 3072]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(486786176)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_11_output_LayerNorm_bias = const()[name = tensor<string, []>("text_branch_encoder_layer_11_output_LayerNorm_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496223424)))]; | |
| tensor<fp32, [768]> text_branch_encoder_layer_11_output_LayerNorm_weight = const()[name = tensor<string, []>("text_branch_encoder_layer_11_output_LayerNorm_weight"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496226560)))]; | |
| tensor<fp32, [768]> text_branch_pooler_dense_bias = const()[name = tensor<string, []>("text_branch_pooler_dense_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496229696)))]; | |
| tensor<fp32, [768, 768]> text_branch_pooler_dense_weight = const()[name = tensor<string, []>("text_branch_pooler_dense_weight"), val = tensor<fp32, [768, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(496232832)))]; | |
| tensor<fp32, [512]> text_projection_0_bias = const()[name = tensor<string, []>("text_projection_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498592192)))]; | |
| tensor<fp32, [512, 768]> text_projection_0_weight = const()[name = tensor<string, []>("text_projection_0_weight"), val = tensor<fp32, [512, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(498594304)))]; | |
| tensor<fp32, [512]> text_projection_2_bias = const()[name = tensor<string, []>("text_projection_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500167232)))]; | |
| tensor<fp32, [512, 512]> text_projection_2_weight = const()[name = tensor<string, []>("text_projection_2_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(500169344)))]; | |
| tensor<int32, []> var_10 = const()[name = tensor<string, []>("op_10"), val = tensor<int32, []>(-1)]; | |
| tensor<fp32, []> var_12 = const()[name = tensor<string, []>("op_12"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; | |
| tensor<fp32, []> var_15 = const()[name = tensor<string, []>("op_15"), val = tensor<fp32, []>(0x1p+0)]; | |
| tensor<int32, []> var_22 = const()[name = tensor<string, []>("op_22"), val = tensor<int32, []>(1)]; | |
| tensor<int32, [1]> var_36_axes_0 = const()[name = tensor<string, []>("op_36_axes_0"), val = tensor<int32, [1]>([1])]; | |
| tensor<int32, [1, 1, 512]> var_36 = expand_dims(axes = var_36_axes_0, x = attention_mask)[name = tensor<string, []>("op_36")]; | |
| tensor<int32, [1]> var_37_axes_0 = const()[name = tensor<string, []>("op_37_axes_0"), val = tensor<int32, [1]>([2])]; | |
| tensor<int32, [1, 1, 1, 512]> var_37 = expand_dims(axes = var_37_axes_0, x = var_36)[name = tensor<string, []>("op_37")]; | |
| tensor<string, []> var_39_dtype_0 = const()[name = tensor<string, []>("op_39_dtype_0"), val = tensor<string, []>("fp32")]; | |
| tensor<fp32, [1, 1, 1, 512]> var_39 = cast(dtype = var_39_dtype_0, x = var_37)[name = tensor<string, []>("cast_78")]; | |
| tensor<fp32, [1, 1, 1, 512]> var_40 = sub(x = var_15, y = var_39)[name = tensor<string, []>("op_40")]; | |
| tensor<fp32, []> var_41 = const()[name = tensor<string, []>("op_41"), val = tensor<fp32, []>(-0x1.fffffep+127)]; | |
| tensor<fp32, [1, 1, 1, 512]> attention_mask_1 = mul(x = var_40, y = var_41)[name = tensor<string, []>("attention_mask")]; | |
| tensor<bool, [1, 512]> var_47 = not_equal(x = input_ids, y = var_22)[name = tensor<string, []>("op_47")]; | |
| tensor<string, []> mask_dtype_0 = const()[name = tensor<string, []>("mask_dtype_0"), val = tensor<string, []>("int32")]; | |
| tensor<bool, []> var_49_exclusive_0 = const()[name = tensor<string, []>("op_49_exclusive_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> var_49_reverse_0 = const()[name = tensor<string, []>("op_49_reverse_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [1, 512]> mask = cast(dtype = mask_dtype_0, x = var_47)[name = tensor<string, []>("cast_77")]; | |
| tensor<int32, [1, 512]> var_49 = cumsum(axis = var_22, exclusive = var_49_exclusive_0, reverse = var_49_reverse_0, x = mask)[name = tensor<string, []>("op_49")]; | |
| tensor<int32, [1, 512]> incremental_indices = mul(x = var_49, y = mask)[name = tensor<string, []>("incremental_indices")]; | |
| tensor<int32, []> var_55 = const()[name = tensor<string, []>("op_55"), val = tensor<int32, []>(1)]; | |
| tensor<int32, [1, 512]> input_3 = add(x = incremental_indices, y = var_55)[name = tensor<string, []>("input_3")]; | |
| tensor<int32, []> inputs_embeds_batch_dims_0 = const()[name = tensor<string, []>("inputs_embeds_batch_dims_0"), val = tensor<int32, []>(0)]; | |
| tensor<bool, []> inputs_embeds_validate_indices_0 = const()[name = tensor<string, []>("inputs_embeds_validate_indices_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, []> greater_equal_0_y_0 = const()[name = tensor<string, []>("greater_equal_0_y_0"), val = tensor<int32, []>(0)]; | |
| tensor<bool, [1, 512]> greater_equal_0 = greater_equal(x = input_ids, y = greater_equal_0_y_0)[name = tensor<string, []>("greater_equal_0")]; | |
| tensor<int32, []> slice_by_index_0 = const()[name = tensor<string, []>("slice_by_index_0"), val = tensor<int32, []>(50265)]; | |
| tensor<int32, [1, 512]> add_0 = add(x = input_ids, y = slice_by_index_0)[name = tensor<string, []>("add_0")]; | |
| tensor<int32, [1, 512]> select_0 = select(a = input_ids, b = add_0, cond = greater_equal_0)[name = tensor<string, []>("select_0")]; | |
| tensor<int32, []> inputs_embeds_axis_1 = const()[name = tensor<string, []>("inputs_embeds_axis_1"), val = tensor<int32, []>(0)]; | |
| tensor<fp32, [1, 512, 768]> inputs_embeds = gather(axis = inputs_embeds_axis_1, batch_dims = inputs_embeds_batch_dims_0, indices = select_0, validate_indices = inputs_embeds_validate_indices_0, x = text_branch_embeddings_word_embeddings_weight)[name = tensor<string, []>("inputs_embeds")]; | |
| tensor<fp32, [1, 512, 768]> token_type_embeddings_1 = const()[name = tensor<string, []>("token_type_embeddings_1"), val = tensor<fp32, [1, 512, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(501217984)))]; | |
| tensor<fp32, [1, 512, 768]> embeddings_1 = add(x = inputs_embeds, y = token_type_embeddings_1)[name = tensor<string, []>("embeddings_1")]; | |
| tensor<int32, []> position_embeddings_1_batch_dims_0 = const()[name = tensor<string, []>("position_embeddings_1_batch_dims_0"), val = tensor<int32, []>(0)]; | |
| tensor<bool, []> position_embeddings_1_validate_indices_0 = const()[name = tensor<string, []>("position_embeddings_1_validate_indices_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, []> greater_equal_1_y_0 = const()[name = tensor<string, []>("greater_equal_1_y_0"), val = tensor<int32, []>(0)]; | |
| tensor<bool, [1, 512]> greater_equal_1 = greater_equal(x = input_3, y = greater_equal_1_y_0)[name = tensor<string, []>("greater_equal_1")]; | |
| tensor<int32, []> slice_by_index_1 = const()[name = tensor<string, []>("slice_by_index_1"), val = tensor<int32, []>(514)]; | |
| tensor<int32, [1, 512]> add_1 = add(x = input_3, y = slice_by_index_1)[name = tensor<string, []>("add_1")]; | |
| tensor<int32, [1, 512]> select_1 = select(a = input_3, b = add_1, cond = greater_equal_1)[name = tensor<string, []>("select_1")]; | |
| tensor<int32, []> position_embeddings_1_axis_1 = const()[name = tensor<string, []>("position_embeddings_1_axis_1"), val = tensor<int32, []>(0)]; | |
| tensor<fp32, [1, 512, 768]> position_embeddings_1 = gather(axis = position_embeddings_1_axis_1, batch_dims = position_embeddings_1_batch_dims_0, indices = select_1, validate_indices = position_embeddings_1_validate_indices_0, x = text_branch_embeddings_position_embeddings_weight)[name = tensor<string, []>("position_embeddings_1")]; | |
| tensor<fp32, [1, 512, 768]> input_5 = add(x = embeddings_1, y = position_embeddings_1)[name = tensor<string, []>("input_5")]; | |
| tensor<int32, [1]> input_7_axes_0 = const()[name = tensor<string, []>("input_7_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_7 = layer_norm(axes = input_7_axes_0, beta = text_branch_embeddings_LayerNorm_bias, epsilon = var_12, gamma = text_branch_embeddings_LayerNorm_weight, x = input_5)[name = tensor<string, []>("input_7")]; | |
| tensor<fp32, [1, 512, 768]> x_9 = linear(bias = text_branch_encoder_layer_0_attention_self_query_bias, weight = text_branch_encoder_layer_0_attention_self_query_weight, x = input_7)[name = tensor<string, []>("linear_0")]; | |
| tensor<fp32, [1, 512, 768]> x_1 = linear(bias = text_branch_encoder_layer_0_attention_self_key_bias, weight = text_branch_encoder_layer_0_attention_self_key_weight, x = input_7)[name = tensor<string, []>("linear_1")]; | |
| tensor<int32, [4]> var_110 = const()[name = tensor<string, []>("op_110"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_3 = reshape(shape = var_110, x = x_1)[name = tensor<string, []>("x_3")]; | |
| tensor<fp32, [1, 512, 768]> x_5 = linear(bias = text_branch_encoder_layer_0_attention_self_value_bias, weight = text_branch_encoder_layer_0_attention_self_value_weight, x = input_7)[name = tensor<string, []>("linear_2")]; | |
| tensor<int32, [4]> var_119 = const()[name = tensor<string, []>("op_119"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_7 = reshape(shape = var_119, x = x_5)[name = tensor<string, []>("x_7")]; | |
| tensor<int32, [4]> var_121 = const()[name = tensor<string, []>("op_121"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_125 = const()[name = tensor<string, []>("op_125"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_11 = reshape(shape = var_125, x = x_9)[name = tensor<string, []>("x_11")]; | |
| tensor<bool, []> attention_scores_1_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_1_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_1_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_1_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_36_perm_0 = const()[name = tensor<string, []>("transpose_36_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_37_perm_0 = const()[name = tensor<string, []>("transpose_37_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_37 = transpose(perm = transpose_37_perm_0, x = x_3)[name = tensor<string, []>("transpose_105")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_36 = transpose(perm = transpose_36_perm_0, x = x_11)[name = tensor<string, []>("transpose_106")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_1 = matmul(transpose_x = attention_scores_1_transpose_x_0, transpose_y = attention_scores_1_transpose_y_0, x = transpose_36, y = transpose_37)[name = tensor<string, []>("attention_scores_1")]; | |
| tensor<fp32, []> _inversed_attention_scores_3_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_3_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_3 = mul(x = attention_scores_1, y = _inversed_attention_scores_3_y_0)[name = tensor<string, []>("_inversed_attention_scores_3")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_11 = add(x = _inversed_attention_scores_3, y = attention_mask_1)[name = tensor<string, []>("input_11")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_13 = softmax(axis = var_10, x = input_11)[name = tensor<string, []>("input_13")]; | |
| tensor<bool, []> context_layer_1_transpose_x_0 = const()[name = tensor<string, []>("context_layer_1_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_1_transpose_y_0 = const()[name = tensor<string, []>("context_layer_1_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_1 = transpose(perm = var_121, x = x_7)[name = tensor<string, []>("transpose_107")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_1 = matmul(transpose_x = context_layer_1_transpose_x_0, transpose_y = context_layer_1_transpose_y_0, x = input_13, y = value_layer_1)[name = tensor<string, []>("context_layer_1")]; | |
| tensor<int32, [4]> var_137 = const()[name = tensor<string, []>("op_137"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_142 = const()[name = tensor<string, []>("op_142"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_138 = transpose(perm = var_137, x = context_layer_1)[name = tensor<string, []>("transpose_104")]; | |
| tensor<fp32, [1, 512, 768]> input_15 = reshape(shape = var_142, x = var_138)[name = tensor<string, []>("input_15")]; | |
| tensor<fp32, [1, 512, 768]> input_17 = linear(bias = text_branch_encoder_layer_0_attention_output_dense_bias, weight = text_branch_encoder_layer_0_attention_output_dense_weight, x = input_15)[name = tensor<string, []>("linear_3")]; | |
| tensor<fp32, [1, 512, 768]> input_19 = add(x = input_17, y = input_7)[name = tensor<string, []>("input_19")]; | |
| tensor<int32, [1]> input_21_axes_0 = const()[name = tensor<string, []>("input_21_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_21 = layer_norm(axes = input_21_axes_0, beta = text_branch_encoder_layer_0_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_0_attention_output_LayerNorm_weight, x = input_19)[name = tensor<string, []>("input_21")]; | |
| tensor<fp32, [1, 512, 3072]> input_23 = linear(bias = text_branch_encoder_layer_0_intermediate_dense_bias, weight = text_branch_encoder_layer_0_intermediate_dense_weight, x = input_21)[name = tensor<string, []>("linear_4")]; | |
| tensor<string, []> input_25_mode_0 = const()[name = tensor<string, []>("input_25_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_25 = gelu(mode = input_25_mode_0, x = input_23)[name = tensor<string, []>("input_25")]; | |
| tensor<fp32, [1, 512, 768]> input_27 = linear(bias = text_branch_encoder_layer_0_output_dense_bias, weight = text_branch_encoder_layer_0_output_dense_weight, x = input_25)[name = tensor<string, []>("linear_5")]; | |
| tensor<fp32, [1, 512, 768]> input_29 = add(x = input_27, y = input_21)[name = tensor<string, []>("input_29")]; | |
| tensor<int32, [1]> input_31_axes_0 = const()[name = tensor<string, []>("input_31_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_31 = layer_norm(axes = input_31_axes_0, beta = text_branch_encoder_layer_0_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_0_output_LayerNorm_weight, x = input_29)[name = tensor<string, []>("input_31")]; | |
| tensor<fp32, [1, 512, 768]> x_21 = linear(bias = text_branch_encoder_layer_1_attention_self_query_bias, weight = text_branch_encoder_layer_1_attention_self_query_weight, x = input_31)[name = tensor<string, []>("linear_6")]; | |
| tensor<fp32, [1, 512, 768]> x_13 = linear(bias = text_branch_encoder_layer_1_attention_self_key_bias, weight = text_branch_encoder_layer_1_attention_self_key_weight, x = input_31)[name = tensor<string, []>("linear_7")]; | |
| tensor<int32, [4]> var_187 = const()[name = tensor<string, []>("op_187"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_15 = reshape(shape = var_187, x = x_13)[name = tensor<string, []>("x_15")]; | |
| tensor<fp32, [1, 512, 768]> x_17 = linear(bias = text_branch_encoder_layer_1_attention_self_value_bias, weight = text_branch_encoder_layer_1_attention_self_value_weight, x = input_31)[name = tensor<string, []>("linear_8")]; | |
| tensor<int32, [4]> var_196 = const()[name = tensor<string, []>("op_196"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_19 = reshape(shape = var_196, x = x_17)[name = tensor<string, []>("x_19")]; | |
| tensor<int32, [4]> var_198 = const()[name = tensor<string, []>("op_198"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_202 = const()[name = tensor<string, []>("op_202"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_23 = reshape(shape = var_202, x = x_21)[name = tensor<string, []>("x_23")]; | |
| tensor<bool, []> attention_scores_5_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_5_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_5_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_5_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_38_perm_0 = const()[name = tensor<string, []>("transpose_38_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_39_perm_0 = const()[name = tensor<string, []>("transpose_39_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_39 = transpose(perm = transpose_39_perm_0, x = x_15)[name = tensor<string, []>("transpose_101")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_38 = transpose(perm = transpose_38_perm_0, x = x_23)[name = tensor<string, []>("transpose_102")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_5 = matmul(transpose_x = attention_scores_5_transpose_x_0, transpose_y = attention_scores_5_transpose_y_0, x = transpose_38, y = transpose_39)[name = tensor<string, []>("attention_scores_5")]; | |
| tensor<fp32, []> _inversed_attention_scores_7_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_7_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_7 = mul(x = attention_scores_5, y = _inversed_attention_scores_7_y_0)[name = tensor<string, []>("_inversed_attention_scores_7")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_33 = add(x = _inversed_attention_scores_7, y = attention_mask_1)[name = tensor<string, []>("input_33")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_35 = softmax(axis = var_10, x = input_33)[name = tensor<string, []>("input_35")]; | |
| tensor<bool, []> context_layer_5_transpose_x_0 = const()[name = tensor<string, []>("context_layer_5_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_5_transpose_y_0 = const()[name = tensor<string, []>("context_layer_5_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_3 = transpose(perm = var_198, x = x_19)[name = tensor<string, []>("transpose_103")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_5 = matmul(transpose_x = context_layer_5_transpose_x_0, transpose_y = context_layer_5_transpose_y_0, x = input_35, y = value_layer_3)[name = tensor<string, []>("context_layer_5")]; | |
| tensor<int32, [4]> var_214 = const()[name = tensor<string, []>("op_214"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_219 = const()[name = tensor<string, []>("op_219"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_215 = transpose(perm = var_214, x = context_layer_5)[name = tensor<string, []>("transpose_100")]; | |
| tensor<fp32, [1, 512, 768]> input_37 = reshape(shape = var_219, x = var_215)[name = tensor<string, []>("input_37")]; | |
| tensor<fp32, [1, 512, 768]> input_39 = linear(bias = text_branch_encoder_layer_1_attention_output_dense_bias, weight = text_branch_encoder_layer_1_attention_output_dense_weight, x = input_37)[name = tensor<string, []>("linear_9")]; | |
| tensor<fp32, [1, 512, 768]> input_41 = add(x = input_39, y = input_31)[name = tensor<string, []>("input_41")]; | |
| tensor<int32, [1]> input_43_axes_0 = const()[name = tensor<string, []>("input_43_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_43 = layer_norm(axes = input_43_axes_0, beta = text_branch_encoder_layer_1_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_1_attention_output_LayerNorm_weight, x = input_41)[name = tensor<string, []>("input_43")]; | |
| tensor<fp32, [1, 512, 3072]> input_45 = linear(bias = text_branch_encoder_layer_1_intermediate_dense_bias, weight = text_branch_encoder_layer_1_intermediate_dense_weight, x = input_43)[name = tensor<string, []>("linear_10")]; | |
| tensor<string, []> input_47_mode_0 = const()[name = tensor<string, []>("input_47_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_47 = gelu(mode = input_47_mode_0, x = input_45)[name = tensor<string, []>("input_47")]; | |
| tensor<fp32, [1, 512, 768]> input_49 = linear(bias = text_branch_encoder_layer_1_output_dense_bias, weight = text_branch_encoder_layer_1_output_dense_weight, x = input_47)[name = tensor<string, []>("linear_11")]; | |
| tensor<fp32, [1, 512, 768]> input_51 = add(x = input_49, y = input_43)[name = tensor<string, []>("input_51")]; | |
| tensor<int32, [1]> input_53_axes_0 = const()[name = tensor<string, []>("input_53_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_53 = layer_norm(axes = input_53_axes_0, beta = text_branch_encoder_layer_1_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_1_output_LayerNorm_weight, x = input_51)[name = tensor<string, []>("input_53")]; | |
| tensor<fp32, [1, 512, 768]> x_33 = linear(bias = text_branch_encoder_layer_2_attention_self_query_bias, weight = text_branch_encoder_layer_2_attention_self_query_weight, x = input_53)[name = tensor<string, []>("linear_12")]; | |
| tensor<fp32, [1, 512, 768]> x_25 = linear(bias = text_branch_encoder_layer_2_attention_self_key_bias, weight = text_branch_encoder_layer_2_attention_self_key_weight, x = input_53)[name = tensor<string, []>("linear_13")]; | |
| tensor<int32, [4]> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_27 = reshape(shape = var_264, x = x_25)[name = tensor<string, []>("x_27")]; | |
| tensor<fp32, [1, 512, 768]> x_29 = linear(bias = text_branch_encoder_layer_2_attention_self_value_bias, weight = text_branch_encoder_layer_2_attention_self_value_weight, x = input_53)[name = tensor<string, []>("linear_14")]; | |
| tensor<int32, [4]> var_273 = const()[name = tensor<string, []>("op_273"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_31 = reshape(shape = var_273, x = x_29)[name = tensor<string, []>("x_31")]; | |
| tensor<int32, [4]> var_275 = const()[name = tensor<string, []>("op_275"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_279 = const()[name = tensor<string, []>("op_279"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_35 = reshape(shape = var_279, x = x_33)[name = tensor<string, []>("x_35")]; | |
| tensor<bool, []> attention_scores_9_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_9_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_9_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_9_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_40_perm_0 = const()[name = tensor<string, []>("transpose_40_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_41_perm_0 = const()[name = tensor<string, []>("transpose_41_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_41 = transpose(perm = transpose_41_perm_0, x = x_27)[name = tensor<string, []>("transpose_97")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_40 = transpose(perm = transpose_40_perm_0, x = x_35)[name = tensor<string, []>("transpose_98")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_9 = matmul(transpose_x = attention_scores_9_transpose_x_0, transpose_y = attention_scores_9_transpose_y_0, x = transpose_40, y = transpose_41)[name = tensor<string, []>("attention_scores_9")]; | |
| tensor<fp32, []> _inversed_attention_scores_11_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_11_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_11 = mul(x = attention_scores_9, y = _inversed_attention_scores_11_y_0)[name = tensor<string, []>("_inversed_attention_scores_11")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_55 = add(x = _inversed_attention_scores_11, y = attention_mask_1)[name = tensor<string, []>("input_55")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_57 = softmax(axis = var_10, x = input_55)[name = tensor<string, []>("input_57")]; | |
| tensor<bool, []> context_layer_9_transpose_x_0 = const()[name = tensor<string, []>("context_layer_9_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_9_transpose_y_0 = const()[name = tensor<string, []>("context_layer_9_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_5 = transpose(perm = var_275, x = x_31)[name = tensor<string, []>("transpose_99")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_9 = matmul(transpose_x = context_layer_9_transpose_x_0, transpose_y = context_layer_9_transpose_y_0, x = input_57, y = value_layer_5)[name = tensor<string, []>("context_layer_9")]; | |
| tensor<int32, [4]> var_291 = const()[name = tensor<string, []>("op_291"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_296 = const()[name = tensor<string, []>("op_296"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_292 = transpose(perm = var_291, x = context_layer_9)[name = tensor<string, []>("transpose_96")]; | |
| tensor<fp32, [1, 512, 768]> input_59 = reshape(shape = var_296, x = var_292)[name = tensor<string, []>("input_59")]; | |
| tensor<fp32, [1, 512, 768]> input_61 = linear(bias = text_branch_encoder_layer_2_attention_output_dense_bias, weight = text_branch_encoder_layer_2_attention_output_dense_weight, x = input_59)[name = tensor<string, []>("linear_15")]; | |
| tensor<fp32, [1, 512, 768]> input_63 = add(x = input_61, y = input_53)[name = tensor<string, []>("input_63")]; | |
| tensor<int32, [1]> input_65_axes_0 = const()[name = tensor<string, []>("input_65_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_65 = layer_norm(axes = input_65_axes_0, beta = text_branch_encoder_layer_2_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_2_attention_output_LayerNorm_weight, x = input_63)[name = tensor<string, []>("input_65")]; | |
| tensor<fp32, [1, 512, 3072]> input_67 = linear(bias = text_branch_encoder_layer_2_intermediate_dense_bias, weight = text_branch_encoder_layer_2_intermediate_dense_weight, x = input_65)[name = tensor<string, []>("linear_16")]; | |
| tensor<string, []> input_69_mode_0 = const()[name = tensor<string, []>("input_69_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_69 = gelu(mode = input_69_mode_0, x = input_67)[name = tensor<string, []>("input_69")]; | |
| tensor<fp32, [1, 512, 768]> input_71 = linear(bias = text_branch_encoder_layer_2_output_dense_bias, weight = text_branch_encoder_layer_2_output_dense_weight, x = input_69)[name = tensor<string, []>("linear_17")]; | |
| tensor<fp32, [1, 512, 768]> input_73 = add(x = input_71, y = input_65)[name = tensor<string, []>("input_73")]; | |
| tensor<int32, [1]> input_75_axes_0 = const()[name = tensor<string, []>("input_75_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_75 = layer_norm(axes = input_75_axes_0, beta = text_branch_encoder_layer_2_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_2_output_LayerNorm_weight, x = input_73)[name = tensor<string, []>("input_75")]; | |
| tensor<fp32, [1, 512, 768]> x_45 = linear(bias = text_branch_encoder_layer_3_attention_self_query_bias, weight = text_branch_encoder_layer_3_attention_self_query_weight, x = input_75)[name = tensor<string, []>("linear_18")]; | |
| tensor<fp32, [1, 512, 768]> x_37 = linear(bias = text_branch_encoder_layer_3_attention_self_key_bias, weight = text_branch_encoder_layer_3_attention_self_key_weight, x = input_75)[name = tensor<string, []>("linear_19")]; | |
| tensor<int32, [4]> var_341 = const()[name = tensor<string, []>("op_341"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_39 = reshape(shape = var_341, x = x_37)[name = tensor<string, []>("x_39")]; | |
| tensor<fp32, [1, 512, 768]> x_41 = linear(bias = text_branch_encoder_layer_3_attention_self_value_bias, weight = text_branch_encoder_layer_3_attention_self_value_weight, x = input_75)[name = tensor<string, []>("linear_20")]; | |
| tensor<int32, [4]> var_350 = const()[name = tensor<string, []>("op_350"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_43 = reshape(shape = var_350, x = x_41)[name = tensor<string, []>("x_43")]; | |
| tensor<int32, [4]> var_352 = const()[name = tensor<string, []>("op_352"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_356 = const()[name = tensor<string, []>("op_356"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_47 = reshape(shape = var_356, x = x_45)[name = tensor<string, []>("x_47")]; | |
| tensor<bool, []> attention_scores_13_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_13_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_13_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_13_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_42_perm_0 = const()[name = tensor<string, []>("transpose_42_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_43_perm_0 = const()[name = tensor<string, []>("transpose_43_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_43 = transpose(perm = transpose_43_perm_0, x = x_39)[name = tensor<string, []>("transpose_93")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_42 = transpose(perm = transpose_42_perm_0, x = x_47)[name = tensor<string, []>("transpose_94")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_13 = matmul(transpose_x = attention_scores_13_transpose_x_0, transpose_y = attention_scores_13_transpose_y_0, x = transpose_42, y = transpose_43)[name = tensor<string, []>("attention_scores_13")]; | |
| tensor<fp32, []> _inversed_attention_scores_15_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_15_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_15 = mul(x = attention_scores_13, y = _inversed_attention_scores_15_y_0)[name = tensor<string, []>("_inversed_attention_scores_15")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_77 = add(x = _inversed_attention_scores_15, y = attention_mask_1)[name = tensor<string, []>("input_77")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_79 = softmax(axis = var_10, x = input_77)[name = tensor<string, []>("input_79")]; | |
| tensor<bool, []> context_layer_13_transpose_x_0 = const()[name = tensor<string, []>("context_layer_13_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_13_transpose_y_0 = const()[name = tensor<string, []>("context_layer_13_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_7 = transpose(perm = var_352, x = x_43)[name = tensor<string, []>("transpose_95")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_13 = matmul(transpose_x = context_layer_13_transpose_x_0, transpose_y = context_layer_13_transpose_y_0, x = input_79, y = value_layer_7)[name = tensor<string, []>("context_layer_13")]; | |
| tensor<int32, [4]> var_368 = const()[name = tensor<string, []>("op_368"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_373 = const()[name = tensor<string, []>("op_373"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_369 = transpose(perm = var_368, x = context_layer_13)[name = tensor<string, []>("transpose_92")]; | |
| tensor<fp32, [1, 512, 768]> input_81 = reshape(shape = var_373, x = var_369)[name = tensor<string, []>("input_81")]; | |
| tensor<fp32, [1, 512, 768]> input_83 = linear(bias = text_branch_encoder_layer_3_attention_output_dense_bias, weight = text_branch_encoder_layer_3_attention_output_dense_weight, x = input_81)[name = tensor<string, []>("linear_21")]; | |
| tensor<fp32, [1, 512, 768]> input_85 = add(x = input_83, y = input_75)[name = tensor<string, []>("input_85")]; | |
| tensor<int32, [1]> input_87_axes_0 = const()[name = tensor<string, []>("input_87_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_87 = layer_norm(axes = input_87_axes_0, beta = text_branch_encoder_layer_3_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_3_attention_output_LayerNorm_weight, x = input_85)[name = tensor<string, []>("input_87")]; | |
| tensor<fp32, [1, 512, 3072]> input_89 = linear(bias = text_branch_encoder_layer_3_intermediate_dense_bias, weight = text_branch_encoder_layer_3_intermediate_dense_weight, x = input_87)[name = tensor<string, []>("linear_22")]; | |
| tensor<string, []> input_91_mode_0 = const()[name = tensor<string, []>("input_91_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_91 = gelu(mode = input_91_mode_0, x = input_89)[name = tensor<string, []>("input_91")]; | |
| tensor<fp32, [1, 512, 768]> input_93 = linear(bias = text_branch_encoder_layer_3_output_dense_bias, weight = text_branch_encoder_layer_3_output_dense_weight, x = input_91)[name = tensor<string, []>("linear_23")]; | |
| tensor<fp32, [1, 512, 768]> input_95 = add(x = input_93, y = input_87)[name = tensor<string, []>("input_95")]; | |
| tensor<int32, [1]> input_97_axes_0 = const()[name = tensor<string, []>("input_97_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_97 = layer_norm(axes = input_97_axes_0, beta = text_branch_encoder_layer_3_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_3_output_LayerNorm_weight, x = input_95)[name = tensor<string, []>("input_97")]; | |
| tensor<fp32, [1, 512, 768]> x_57 = linear(bias = text_branch_encoder_layer_4_attention_self_query_bias, weight = text_branch_encoder_layer_4_attention_self_query_weight, x = input_97)[name = tensor<string, []>("linear_24")]; | |
| tensor<fp32, [1, 512, 768]> x_49 = linear(bias = text_branch_encoder_layer_4_attention_self_key_bias, weight = text_branch_encoder_layer_4_attention_self_key_weight, x = input_97)[name = tensor<string, []>("linear_25")]; | |
| tensor<int32, [4]> var_418 = const()[name = tensor<string, []>("op_418"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_51 = reshape(shape = var_418, x = x_49)[name = tensor<string, []>("x_51")]; | |
| tensor<fp32, [1, 512, 768]> x_53 = linear(bias = text_branch_encoder_layer_4_attention_self_value_bias, weight = text_branch_encoder_layer_4_attention_self_value_weight, x = input_97)[name = tensor<string, []>("linear_26")]; | |
| tensor<int32, [4]> var_427 = const()[name = tensor<string, []>("op_427"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_55 = reshape(shape = var_427, x = x_53)[name = tensor<string, []>("x_55")]; | |
| tensor<int32, [4]> var_429 = const()[name = tensor<string, []>("op_429"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_433 = const()[name = tensor<string, []>("op_433"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_59 = reshape(shape = var_433, x = x_57)[name = tensor<string, []>("x_59")]; | |
| tensor<bool, []> attention_scores_17_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_17_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_17_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_17_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_44_perm_0 = const()[name = tensor<string, []>("transpose_44_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_45_perm_0 = const()[name = tensor<string, []>("transpose_45_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_45 = transpose(perm = transpose_45_perm_0, x = x_51)[name = tensor<string, []>("transpose_89")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_44 = transpose(perm = transpose_44_perm_0, x = x_59)[name = tensor<string, []>("transpose_90")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_17 = matmul(transpose_x = attention_scores_17_transpose_x_0, transpose_y = attention_scores_17_transpose_y_0, x = transpose_44, y = transpose_45)[name = tensor<string, []>("attention_scores_17")]; | |
| tensor<fp32, []> _inversed_attention_scores_19_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_19_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_19 = mul(x = attention_scores_17, y = _inversed_attention_scores_19_y_0)[name = tensor<string, []>("_inversed_attention_scores_19")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_99 = add(x = _inversed_attention_scores_19, y = attention_mask_1)[name = tensor<string, []>("input_99")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_101 = softmax(axis = var_10, x = input_99)[name = tensor<string, []>("input_101")]; | |
| tensor<bool, []> context_layer_17_transpose_x_0 = const()[name = tensor<string, []>("context_layer_17_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_17_transpose_y_0 = const()[name = tensor<string, []>("context_layer_17_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_9 = transpose(perm = var_429, x = x_55)[name = tensor<string, []>("transpose_91")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_17 = matmul(transpose_x = context_layer_17_transpose_x_0, transpose_y = context_layer_17_transpose_y_0, x = input_101, y = value_layer_9)[name = tensor<string, []>("context_layer_17")]; | |
| tensor<int32, [4]> var_445 = const()[name = tensor<string, []>("op_445"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_450 = const()[name = tensor<string, []>("op_450"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_446 = transpose(perm = var_445, x = context_layer_17)[name = tensor<string, []>("transpose_88")]; | |
| tensor<fp32, [1, 512, 768]> input_103 = reshape(shape = var_450, x = var_446)[name = tensor<string, []>("input_103")]; | |
| tensor<fp32, [1, 512, 768]> input_105 = linear(bias = text_branch_encoder_layer_4_attention_output_dense_bias, weight = text_branch_encoder_layer_4_attention_output_dense_weight, x = input_103)[name = tensor<string, []>("linear_27")]; | |
| tensor<fp32, [1, 512, 768]> input_107 = add(x = input_105, y = input_97)[name = tensor<string, []>("input_107")]; | |
| tensor<int32, [1]> input_109_axes_0 = const()[name = tensor<string, []>("input_109_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_109 = layer_norm(axes = input_109_axes_0, beta = text_branch_encoder_layer_4_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_4_attention_output_LayerNorm_weight, x = input_107)[name = tensor<string, []>("input_109")]; | |
| tensor<fp32, [1, 512, 3072]> input_111 = linear(bias = text_branch_encoder_layer_4_intermediate_dense_bias, weight = text_branch_encoder_layer_4_intermediate_dense_weight, x = input_109)[name = tensor<string, []>("linear_28")]; | |
| tensor<string, []> input_113_mode_0 = const()[name = tensor<string, []>("input_113_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_113 = gelu(mode = input_113_mode_0, x = input_111)[name = tensor<string, []>("input_113")]; | |
| tensor<fp32, [1, 512, 768]> input_115 = linear(bias = text_branch_encoder_layer_4_output_dense_bias, weight = text_branch_encoder_layer_4_output_dense_weight, x = input_113)[name = tensor<string, []>("linear_29")]; | |
| tensor<fp32, [1, 512, 768]> input_117 = add(x = input_115, y = input_109)[name = tensor<string, []>("input_117")]; | |
| tensor<int32, [1]> input_119_axes_0 = const()[name = tensor<string, []>("input_119_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_119 = layer_norm(axes = input_119_axes_0, beta = text_branch_encoder_layer_4_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_4_output_LayerNorm_weight, x = input_117)[name = tensor<string, []>("input_119")]; | |
| tensor<fp32, [1, 512, 768]> x_69 = linear(bias = text_branch_encoder_layer_5_attention_self_query_bias, weight = text_branch_encoder_layer_5_attention_self_query_weight, x = input_119)[name = tensor<string, []>("linear_30")]; | |
| tensor<fp32, [1, 512, 768]> x_61 = linear(bias = text_branch_encoder_layer_5_attention_self_key_bias, weight = text_branch_encoder_layer_5_attention_self_key_weight, x = input_119)[name = tensor<string, []>("linear_31")]; | |
| tensor<int32, [4]> var_495 = const()[name = tensor<string, []>("op_495"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_63 = reshape(shape = var_495, x = x_61)[name = tensor<string, []>("x_63")]; | |
| tensor<fp32, [1, 512, 768]> x_65 = linear(bias = text_branch_encoder_layer_5_attention_self_value_bias, weight = text_branch_encoder_layer_5_attention_self_value_weight, x = input_119)[name = tensor<string, []>("linear_32")]; | |
| tensor<int32, [4]> var_504 = const()[name = tensor<string, []>("op_504"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_67 = reshape(shape = var_504, x = x_65)[name = tensor<string, []>("x_67")]; | |
| tensor<int32, [4]> var_506 = const()[name = tensor<string, []>("op_506"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_510 = const()[name = tensor<string, []>("op_510"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_71 = reshape(shape = var_510, x = x_69)[name = tensor<string, []>("x_71")]; | |
| tensor<bool, []> attention_scores_21_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_21_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_21_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_21_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_46_perm_0 = const()[name = tensor<string, []>("transpose_46_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_47_perm_0 = const()[name = tensor<string, []>("transpose_47_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_47 = transpose(perm = transpose_47_perm_0, x = x_63)[name = tensor<string, []>("transpose_85")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_46 = transpose(perm = transpose_46_perm_0, x = x_71)[name = tensor<string, []>("transpose_86")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_21 = matmul(transpose_x = attention_scores_21_transpose_x_0, transpose_y = attention_scores_21_transpose_y_0, x = transpose_46, y = transpose_47)[name = tensor<string, []>("attention_scores_21")]; | |
| tensor<fp32, []> _inversed_attention_scores_23_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_23_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_23 = mul(x = attention_scores_21, y = _inversed_attention_scores_23_y_0)[name = tensor<string, []>("_inversed_attention_scores_23")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_121 = add(x = _inversed_attention_scores_23, y = attention_mask_1)[name = tensor<string, []>("input_121")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_123 = softmax(axis = var_10, x = input_121)[name = tensor<string, []>("input_123")]; | |
| tensor<bool, []> context_layer_21_transpose_x_0 = const()[name = tensor<string, []>("context_layer_21_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_21_transpose_y_0 = const()[name = tensor<string, []>("context_layer_21_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_11 = transpose(perm = var_506, x = x_67)[name = tensor<string, []>("transpose_87")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_21 = matmul(transpose_x = context_layer_21_transpose_x_0, transpose_y = context_layer_21_transpose_y_0, x = input_123, y = value_layer_11)[name = tensor<string, []>("context_layer_21")]; | |
| tensor<int32, [4]> var_522 = const()[name = tensor<string, []>("op_522"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_527 = const()[name = tensor<string, []>("op_527"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_523 = transpose(perm = var_522, x = context_layer_21)[name = tensor<string, []>("transpose_84")]; | |
| tensor<fp32, [1, 512, 768]> input_125 = reshape(shape = var_527, x = var_523)[name = tensor<string, []>("input_125")]; | |
| tensor<fp32, [1, 512, 768]> input_127 = linear(bias = text_branch_encoder_layer_5_attention_output_dense_bias, weight = text_branch_encoder_layer_5_attention_output_dense_weight, x = input_125)[name = tensor<string, []>("linear_33")]; | |
| tensor<fp32, [1, 512, 768]> input_129 = add(x = input_127, y = input_119)[name = tensor<string, []>("input_129")]; | |
| tensor<int32, [1]> input_131_axes_0 = const()[name = tensor<string, []>("input_131_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_131 = layer_norm(axes = input_131_axes_0, beta = text_branch_encoder_layer_5_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_5_attention_output_LayerNorm_weight, x = input_129)[name = tensor<string, []>("input_131")]; | |
| tensor<fp32, [1, 512, 3072]> input_133 = linear(bias = text_branch_encoder_layer_5_intermediate_dense_bias, weight = text_branch_encoder_layer_5_intermediate_dense_weight, x = input_131)[name = tensor<string, []>("linear_34")]; | |
| tensor<string, []> input_135_mode_0 = const()[name = tensor<string, []>("input_135_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_135 = gelu(mode = input_135_mode_0, x = input_133)[name = tensor<string, []>("input_135")]; | |
| tensor<fp32, [1, 512, 768]> input_137 = linear(bias = text_branch_encoder_layer_5_output_dense_bias, weight = text_branch_encoder_layer_5_output_dense_weight, x = input_135)[name = tensor<string, []>("linear_35")]; | |
| tensor<fp32, [1, 512, 768]> input_139 = add(x = input_137, y = input_131)[name = tensor<string, []>("input_139")]; | |
| tensor<int32, [1]> input_141_axes_0 = const()[name = tensor<string, []>("input_141_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_141 = layer_norm(axes = input_141_axes_0, beta = text_branch_encoder_layer_5_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_5_output_LayerNorm_weight, x = input_139)[name = tensor<string, []>("input_141")]; | |
| tensor<fp32, [1, 512, 768]> x_81 = linear(bias = text_branch_encoder_layer_6_attention_self_query_bias, weight = text_branch_encoder_layer_6_attention_self_query_weight, x = input_141)[name = tensor<string, []>("linear_36")]; | |
| tensor<fp32, [1, 512, 768]> x_73 = linear(bias = text_branch_encoder_layer_6_attention_self_key_bias, weight = text_branch_encoder_layer_6_attention_self_key_weight, x = input_141)[name = tensor<string, []>("linear_37")]; | |
| tensor<int32, [4]> var_572 = const()[name = tensor<string, []>("op_572"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_75 = reshape(shape = var_572, x = x_73)[name = tensor<string, []>("x_75")]; | |
| tensor<fp32, [1, 512, 768]> x_77 = linear(bias = text_branch_encoder_layer_6_attention_self_value_bias, weight = text_branch_encoder_layer_6_attention_self_value_weight, x = input_141)[name = tensor<string, []>("linear_38")]; | |
| tensor<int32, [4]> var_581 = const()[name = tensor<string, []>("op_581"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_79 = reshape(shape = var_581, x = x_77)[name = tensor<string, []>("x_79")]; | |
| tensor<int32, [4]> var_583 = const()[name = tensor<string, []>("op_583"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_587 = const()[name = tensor<string, []>("op_587"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_83 = reshape(shape = var_587, x = x_81)[name = tensor<string, []>("x_83")]; | |
| tensor<bool, []> attention_scores_25_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_25_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_25_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_25_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_48_perm_0 = const()[name = tensor<string, []>("transpose_48_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_49_perm_0 = const()[name = tensor<string, []>("transpose_49_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_49 = transpose(perm = transpose_49_perm_0, x = x_75)[name = tensor<string, []>("transpose_81")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_48 = transpose(perm = transpose_48_perm_0, x = x_83)[name = tensor<string, []>("transpose_82")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_25 = matmul(transpose_x = attention_scores_25_transpose_x_0, transpose_y = attention_scores_25_transpose_y_0, x = transpose_48, y = transpose_49)[name = tensor<string, []>("attention_scores_25")]; | |
| tensor<fp32, []> _inversed_attention_scores_27_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_27_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_27 = mul(x = attention_scores_25, y = _inversed_attention_scores_27_y_0)[name = tensor<string, []>("_inversed_attention_scores_27")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_143 = add(x = _inversed_attention_scores_27, y = attention_mask_1)[name = tensor<string, []>("input_143")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_145 = softmax(axis = var_10, x = input_143)[name = tensor<string, []>("input_145")]; | |
| tensor<bool, []> context_layer_25_transpose_x_0 = const()[name = tensor<string, []>("context_layer_25_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_25_transpose_y_0 = const()[name = tensor<string, []>("context_layer_25_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_13 = transpose(perm = var_583, x = x_79)[name = tensor<string, []>("transpose_83")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_25 = matmul(transpose_x = context_layer_25_transpose_x_0, transpose_y = context_layer_25_transpose_y_0, x = input_145, y = value_layer_13)[name = tensor<string, []>("context_layer_25")]; | |
| tensor<int32, [4]> var_599 = const()[name = tensor<string, []>("op_599"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_604 = const()[name = tensor<string, []>("op_604"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_600 = transpose(perm = var_599, x = context_layer_25)[name = tensor<string, []>("transpose_80")]; | |
| tensor<fp32, [1, 512, 768]> input_147 = reshape(shape = var_604, x = var_600)[name = tensor<string, []>("input_147")]; | |
| tensor<fp32, [1, 512, 768]> input_149 = linear(bias = text_branch_encoder_layer_6_attention_output_dense_bias, weight = text_branch_encoder_layer_6_attention_output_dense_weight, x = input_147)[name = tensor<string, []>("linear_39")]; | |
| tensor<fp32, [1, 512, 768]> input_151 = add(x = input_149, y = input_141)[name = tensor<string, []>("input_151")]; | |
| tensor<int32, [1]> input_153_axes_0 = const()[name = tensor<string, []>("input_153_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_153 = layer_norm(axes = input_153_axes_0, beta = text_branch_encoder_layer_6_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_6_attention_output_LayerNorm_weight, x = input_151)[name = tensor<string, []>("input_153")]; | |
| tensor<fp32, [1, 512, 3072]> input_155 = linear(bias = text_branch_encoder_layer_6_intermediate_dense_bias, weight = text_branch_encoder_layer_6_intermediate_dense_weight, x = input_153)[name = tensor<string, []>("linear_40")]; | |
| tensor<string, []> input_157_mode_0 = const()[name = tensor<string, []>("input_157_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_157 = gelu(mode = input_157_mode_0, x = input_155)[name = tensor<string, []>("input_157")]; | |
| tensor<fp32, [1, 512, 768]> input_159 = linear(bias = text_branch_encoder_layer_6_output_dense_bias, weight = text_branch_encoder_layer_6_output_dense_weight, x = input_157)[name = tensor<string, []>("linear_41")]; | |
| tensor<fp32, [1, 512, 768]> input_161 = add(x = input_159, y = input_153)[name = tensor<string, []>("input_161")]; | |
| tensor<int32, [1]> input_163_axes_0 = const()[name = tensor<string, []>("input_163_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_163 = layer_norm(axes = input_163_axes_0, beta = text_branch_encoder_layer_6_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_6_output_LayerNorm_weight, x = input_161)[name = tensor<string, []>("input_163")]; | |
| tensor<fp32, [1, 512, 768]> x_93 = linear(bias = text_branch_encoder_layer_7_attention_self_query_bias, weight = text_branch_encoder_layer_7_attention_self_query_weight, x = input_163)[name = tensor<string, []>("linear_42")]; | |
| tensor<fp32, [1, 512, 768]> x_85 = linear(bias = text_branch_encoder_layer_7_attention_self_key_bias, weight = text_branch_encoder_layer_7_attention_self_key_weight, x = input_163)[name = tensor<string, []>("linear_43")]; | |
| tensor<int32, [4]> var_649 = const()[name = tensor<string, []>("op_649"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_87 = reshape(shape = var_649, x = x_85)[name = tensor<string, []>("x_87")]; | |
| tensor<fp32, [1, 512, 768]> x_89 = linear(bias = text_branch_encoder_layer_7_attention_self_value_bias, weight = text_branch_encoder_layer_7_attention_self_value_weight, x = input_163)[name = tensor<string, []>("linear_44")]; | |
| tensor<int32, [4]> var_658 = const()[name = tensor<string, []>("op_658"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_91 = reshape(shape = var_658, x = x_89)[name = tensor<string, []>("x_91")]; | |
| tensor<int32, [4]> var_660 = const()[name = tensor<string, []>("op_660"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_664 = const()[name = tensor<string, []>("op_664"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_95 = reshape(shape = var_664, x = x_93)[name = tensor<string, []>("x_95")]; | |
| tensor<bool, []> attention_scores_29_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_29_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_29_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_29_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_50_perm_0 = const()[name = tensor<string, []>("transpose_50_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_51_perm_0 = const()[name = tensor<string, []>("transpose_51_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_51 = transpose(perm = transpose_51_perm_0, x = x_87)[name = tensor<string, []>("transpose_77")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_50 = transpose(perm = transpose_50_perm_0, x = x_95)[name = tensor<string, []>("transpose_78")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_29 = matmul(transpose_x = attention_scores_29_transpose_x_0, transpose_y = attention_scores_29_transpose_y_0, x = transpose_50, y = transpose_51)[name = tensor<string, []>("attention_scores_29")]; | |
| tensor<fp32, []> _inversed_attention_scores_31_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_31_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_31 = mul(x = attention_scores_29, y = _inversed_attention_scores_31_y_0)[name = tensor<string, []>("_inversed_attention_scores_31")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_165 = add(x = _inversed_attention_scores_31, y = attention_mask_1)[name = tensor<string, []>("input_165")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_167 = softmax(axis = var_10, x = input_165)[name = tensor<string, []>("input_167")]; | |
| tensor<bool, []> context_layer_29_transpose_x_0 = const()[name = tensor<string, []>("context_layer_29_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_29_transpose_y_0 = const()[name = tensor<string, []>("context_layer_29_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_15 = transpose(perm = var_660, x = x_91)[name = tensor<string, []>("transpose_79")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_29 = matmul(transpose_x = context_layer_29_transpose_x_0, transpose_y = context_layer_29_transpose_y_0, x = input_167, y = value_layer_15)[name = tensor<string, []>("context_layer_29")]; | |
| tensor<int32, [4]> var_676 = const()[name = tensor<string, []>("op_676"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_681 = const()[name = tensor<string, []>("op_681"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_677 = transpose(perm = var_676, x = context_layer_29)[name = tensor<string, []>("transpose_76")]; | |
| tensor<fp32, [1, 512, 768]> input_169 = reshape(shape = var_681, x = var_677)[name = tensor<string, []>("input_169")]; | |
| tensor<fp32, [1, 512, 768]> input_171 = linear(bias = text_branch_encoder_layer_7_attention_output_dense_bias, weight = text_branch_encoder_layer_7_attention_output_dense_weight, x = input_169)[name = tensor<string, []>("linear_45")]; | |
| tensor<fp32, [1, 512, 768]> input_173 = add(x = input_171, y = input_163)[name = tensor<string, []>("input_173")]; | |
| tensor<int32, [1]> input_175_axes_0 = const()[name = tensor<string, []>("input_175_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_175 = layer_norm(axes = input_175_axes_0, beta = text_branch_encoder_layer_7_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_7_attention_output_LayerNorm_weight, x = input_173)[name = tensor<string, []>("input_175")]; | |
| tensor<fp32, [1, 512, 3072]> input_177 = linear(bias = text_branch_encoder_layer_7_intermediate_dense_bias, weight = text_branch_encoder_layer_7_intermediate_dense_weight, x = input_175)[name = tensor<string, []>("linear_46")]; | |
| tensor<string, []> input_179_mode_0 = const()[name = tensor<string, []>("input_179_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_179 = gelu(mode = input_179_mode_0, x = input_177)[name = tensor<string, []>("input_179")]; | |
| tensor<fp32, [1, 512, 768]> input_181 = linear(bias = text_branch_encoder_layer_7_output_dense_bias, weight = text_branch_encoder_layer_7_output_dense_weight, x = input_179)[name = tensor<string, []>("linear_47")]; | |
| tensor<fp32, [1, 512, 768]> input_183 = add(x = input_181, y = input_175)[name = tensor<string, []>("input_183")]; | |
| tensor<int32, [1]> input_185_axes_0 = const()[name = tensor<string, []>("input_185_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_185 = layer_norm(axes = input_185_axes_0, beta = text_branch_encoder_layer_7_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_7_output_LayerNorm_weight, x = input_183)[name = tensor<string, []>("input_185")]; | |
| tensor<fp32, [1, 512, 768]> x_105 = linear(bias = text_branch_encoder_layer_8_attention_self_query_bias, weight = text_branch_encoder_layer_8_attention_self_query_weight, x = input_185)[name = tensor<string, []>("linear_48")]; | |
| tensor<fp32, [1, 512, 768]> x_97 = linear(bias = text_branch_encoder_layer_8_attention_self_key_bias, weight = text_branch_encoder_layer_8_attention_self_key_weight, x = input_185)[name = tensor<string, []>("linear_49")]; | |
| tensor<int32, [4]> var_726 = const()[name = tensor<string, []>("op_726"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_99 = reshape(shape = var_726, x = x_97)[name = tensor<string, []>("x_99")]; | |
| tensor<fp32, [1, 512, 768]> x_101 = linear(bias = text_branch_encoder_layer_8_attention_self_value_bias, weight = text_branch_encoder_layer_8_attention_self_value_weight, x = input_185)[name = tensor<string, []>("linear_50")]; | |
| tensor<int32, [4]> var_735 = const()[name = tensor<string, []>("op_735"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_103 = reshape(shape = var_735, x = x_101)[name = tensor<string, []>("x_103")]; | |
| tensor<int32, [4]> var_737 = const()[name = tensor<string, []>("op_737"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_741 = const()[name = tensor<string, []>("op_741"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_107 = reshape(shape = var_741, x = x_105)[name = tensor<string, []>("x_107")]; | |
| tensor<bool, []> attention_scores_33_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_33_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_33_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_33_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_52_perm_0 = const()[name = tensor<string, []>("transpose_52_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_53_perm_0 = const()[name = tensor<string, []>("transpose_53_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_53 = transpose(perm = transpose_53_perm_0, x = x_99)[name = tensor<string, []>("transpose_73")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_52 = transpose(perm = transpose_52_perm_0, x = x_107)[name = tensor<string, []>("transpose_74")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_33 = matmul(transpose_x = attention_scores_33_transpose_x_0, transpose_y = attention_scores_33_transpose_y_0, x = transpose_52, y = transpose_53)[name = tensor<string, []>("attention_scores_33")]; | |
| tensor<fp32, []> _inversed_attention_scores_35_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_35_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_35 = mul(x = attention_scores_33, y = _inversed_attention_scores_35_y_0)[name = tensor<string, []>("_inversed_attention_scores_35")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_187 = add(x = _inversed_attention_scores_35, y = attention_mask_1)[name = tensor<string, []>("input_187")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_189 = softmax(axis = var_10, x = input_187)[name = tensor<string, []>("input_189")]; | |
| tensor<bool, []> context_layer_33_transpose_x_0 = const()[name = tensor<string, []>("context_layer_33_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_33_transpose_y_0 = const()[name = tensor<string, []>("context_layer_33_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_17 = transpose(perm = var_737, x = x_103)[name = tensor<string, []>("transpose_75")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_33 = matmul(transpose_x = context_layer_33_transpose_x_0, transpose_y = context_layer_33_transpose_y_0, x = input_189, y = value_layer_17)[name = tensor<string, []>("context_layer_33")]; | |
| tensor<int32, [4]> var_753 = const()[name = tensor<string, []>("op_753"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_758 = const()[name = tensor<string, []>("op_758"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_754 = transpose(perm = var_753, x = context_layer_33)[name = tensor<string, []>("transpose_72")]; | |
| tensor<fp32, [1, 512, 768]> input_191 = reshape(shape = var_758, x = var_754)[name = tensor<string, []>("input_191")]; | |
| tensor<fp32, [1, 512, 768]> input_193 = linear(bias = text_branch_encoder_layer_8_attention_output_dense_bias, weight = text_branch_encoder_layer_8_attention_output_dense_weight, x = input_191)[name = tensor<string, []>("linear_51")]; | |
| tensor<fp32, [1, 512, 768]> input_195 = add(x = input_193, y = input_185)[name = tensor<string, []>("input_195")]; | |
| tensor<int32, [1]> input_197_axes_0 = const()[name = tensor<string, []>("input_197_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_197 = layer_norm(axes = input_197_axes_0, beta = text_branch_encoder_layer_8_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_8_attention_output_LayerNorm_weight, x = input_195)[name = tensor<string, []>("input_197")]; | |
| tensor<fp32, [1, 512, 3072]> input_199 = linear(bias = text_branch_encoder_layer_8_intermediate_dense_bias, weight = text_branch_encoder_layer_8_intermediate_dense_weight, x = input_197)[name = tensor<string, []>("linear_52")]; | |
| tensor<string, []> input_201_mode_0 = const()[name = tensor<string, []>("input_201_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_201 = gelu(mode = input_201_mode_0, x = input_199)[name = tensor<string, []>("input_201")]; | |
| tensor<fp32, [1, 512, 768]> input_203 = linear(bias = text_branch_encoder_layer_8_output_dense_bias, weight = text_branch_encoder_layer_8_output_dense_weight, x = input_201)[name = tensor<string, []>("linear_53")]; | |
| tensor<fp32, [1, 512, 768]> input_205 = add(x = input_203, y = input_197)[name = tensor<string, []>("input_205")]; | |
| tensor<int32, [1]> input_207_axes_0 = const()[name = tensor<string, []>("input_207_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_207 = layer_norm(axes = input_207_axes_0, beta = text_branch_encoder_layer_8_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_8_output_LayerNorm_weight, x = input_205)[name = tensor<string, []>("input_207")]; | |
| tensor<fp32, [1, 512, 768]> x_117 = linear(bias = text_branch_encoder_layer_9_attention_self_query_bias, weight = text_branch_encoder_layer_9_attention_self_query_weight, x = input_207)[name = tensor<string, []>("linear_54")]; | |
| tensor<fp32, [1, 512, 768]> x_109 = linear(bias = text_branch_encoder_layer_9_attention_self_key_bias, weight = text_branch_encoder_layer_9_attention_self_key_weight, x = input_207)[name = tensor<string, []>("linear_55")]; | |
| tensor<int32, [4]> var_803 = const()[name = tensor<string, []>("op_803"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_111 = reshape(shape = var_803, x = x_109)[name = tensor<string, []>("x_111")]; | |
| tensor<fp32, [1, 512, 768]> x_113 = linear(bias = text_branch_encoder_layer_9_attention_self_value_bias, weight = text_branch_encoder_layer_9_attention_self_value_weight, x = input_207)[name = tensor<string, []>("linear_56")]; | |
| tensor<int32, [4]> var_812 = const()[name = tensor<string, []>("op_812"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_115 = reshape(shape = var_812, x = x_113)[name = tensor<string, []>("x_115")]; | |
| tensor<int32, [4]> var_814 = const()[name = tensor<string, []>("op_814"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_818 = const()[name = tensor<string, []>("op_818"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_119 = reshape(shape = var_818, x = x_117)[name = tensor<string, []>("x_119")]; | |
| tensor<bool, []> attention_scores_37_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_37_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_37_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_37_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_54_perm_0 = const()[name = tensor<string, []>("transpose_54_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_55_perm_0 = const()[name = tensor<string, []>("transpose_55_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_55 = transpose(perm = transpose_55_perm_0, x = x_111)[name = tensor<string, []>("transpose_69")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_54 = transpose(perm = transpose_54_perm_0, x = x_119)[name = tensor<string, []>("transpose_70")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_37 = matmul(transpose_x = attention_scores_37_transpose_x_0, transpose_y = attention_scores_37_transpose_y_0, x = transpose_54, y = transpose_55)[name = tensor<string, []>("attention_scores_37")]; | |
| tensor<fp32, []> _inversed_attention_scores_39_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_39_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_39 = mul(x = attention_scores_37, y = _inversed_attention_scores_39_y_0)[name = tensor<string, []>("_inversed_attention_scores_39")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_209 = add(x = _inversed_attention_scores_39, y = attention_mask_1)[name = tensor<string, []>("input_209")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_211 = softmax(axis = var_10, x = input_209)[name = tensor<string, []>("input_211")]; | |
| tensor<bool, []> context_layer_37_transpose_x_0 = const()[name = tensor<string, []>("context_layer_37_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_37_transpose_y_0 = const()[name = tensor<string, []>("context_layer_37_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_19 = transpose(perm = var_814, x = x_115)[name = tensor<string, []>("transpose_71")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_37 = matmul(transpose_x = context_layer_37_transpose_x_0, transpose_y = context_layer_37_transpose_y_0, x = input_211, y = value_layer_19)[name = tensor<string, []>("context_layer_37")]; | |
| tensor<int32, [4]> var_830 = const()[name = tensor<string, []>("op_830"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_835 = const()[name = tensor<string, []>("op_835"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_831 = transpose(perm = var_830, x = context_layer_37)[name = tensor<string, []>("transpose_68")]; | |
| tensor<fp32, [1, 512, 768]> input_213 = reshape(shape = var_835, x = var_831)[name = tensor<string, []>("input_213")]; | |
| tensor<fp32, [1, 512, 768]> input_215 = linear(bias = text_branch_encoder_layer_9_attention_output_dense_bias, weight = text_branch_encoder_layer_9_attention_output_dense_weight, x = input_213)[name = tensor<string, []>("linear_57")]; | |
| tensor<fp32, [1, 512, 768]> input_217 = add(x = input_215, y = input_207)[name = tensor<string, []>("input_217")]; | |
| tensor<int32, [1]> input_219_axes_0 = const()[name = tensor<string, []>("input_219_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_219 = layer_norm(axes = input_219_axes_0, beta = text_branch_encoder_layer_9_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_9_attention_output_LayerNorm_weight, x = input_217)[name = tensor<string, []>("input_219")]; | |
| tensor<fp32, [1, 512, 3072]> input_221 = linear(bias = text_branch_encoder_layer_9_intermediate_dense_bias, weight = text_branch_encoder_layer_9_intermediate_dense_weight, x = input_219)[name = tensor<string, []>("linear_58")]; | |
| tensor<string, []> input_223_mode_0 = const()[name = tensor<string, []>("input_223_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_223 = gelu(mode = input_223_mode_0, x = input_221)[name = tensor<string, []>("input_223")]; | |
| tensor<fp32, [1, 512, 768]> input_225 = linear(bias = text_branch_encoder_layer_9_output_dense_bias, weight = text_branch_encoder_layer_9_output_dense_weight, x = input_223)[name = tensor<string, []>("linear_59")]; | |
| tensor<fp32, [1, 512, 768]> input_227 = add(x = input_225, y = input_219)[name = tensor<string, []>("input_227")]; | |
| tensor<int32, [1]> input_229_axes_0 = const()[name = tensor<string, []>("input_229_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_229 = layer_norm(axes = input_229_axes_0, beta = text_branch_encoder_layer_9_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_9_output_LayerNorm_weight, x = input_227)[name = tensor<string, []>("input_229")]; | |
| tensor<fp32, [1, 512, 768]> x_129 = linear(bias = text_branch_encoder_layer_10_attention_self_query_bias, weight = text_branch_encoder_layer_10_attention_self_query_weight, x = input_229)[name = tensor<string, []>("linear_60")]; | |
| tensor<fp32, [1, 512, 768]> x_121 = linear(bias = text_branch_encoder_layer_10_attention_self_key_bias, weight = text_branch_encoder_layer_10_attention_self_key_weight, x = input_229)[name = tensor<string, []>("linear_61")]; | |
| tensor<int32, [4]> var_880 = const()[name = tensor<string, []>("op_880"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_123 = reshape(shape = var_880, x = x_121)[name = tensor<string, []>("x_123")]; | |
| tensor<fp32, [1, 512, 768]> x_125 = linear(bias = text_branch_encoder_layer_10_attention_self_value_bias, weight = text_branch_encoder_layer_10_attention_self_value_weight, x = input_229)[name = tensor<string, []>("linear_62")]; | |
| tensor<int32, [4]> var_889 = const()[name = tensor<string, []>("op_889"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_127 = reshape(shape = var_889, x = x_125)[name = tensor<string, []>("x_127")]; | |
| tensor<int32, [4]> var_891 = const()[name = tensor<string, []>("op_891"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_895 = const()[name = tensor<string, []>("op_895"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_131 = reshape(shape = var_895, x = x_129)[name = tensor<string, []>("x_131")]; | |
| tensor<bool, []> attention_scores_41_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_41_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_41_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_41_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_56_perm_0 = const()[name = tensor<string, []>("transpose_56_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_57_perm_0 = const()[name = tensor<string, []>("transpose_57_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_57 = transpose(perm = transpose_57_perm_0, x = x_123)[name = tensor<string, []>("transpose_65")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_56 = transpose(perm = transpose_56_perm_0, x = x_131)[name = tensor<string, []>("transpose_66")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_41 = matmul(transpose_x = attention_scores_41_transpose_x_0, transpose_y = attention_scores_41_transpose_y_0, x = transpose_56, y = transpose_57)[name = tensor<string, []>("attention_scores_41")]; | |
| tensor<fp32, []> _inversed_attention_scores_43_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_43_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores_43 = mul(x = attention_scores_41, y = _inversed_attention_scores_43_y_0)[name = tensor<string, []>("_inversed_attention_scores_43")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_231 = add(x = _inversed_attention_scores_43, y = attention_mask_1)[name = tensor<string, []>("input_231")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_233 = softmax(axis = var_10, x = input_231)[name = tensor<string, []>("input_233")]; | |
| tensor<bool, []> context_layer_41_transpose_x_0 = const()[name = tensor<string, []>("context_layer_41_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_41_transpose_y_0 = const()[name = tensor<string, []>("context_layer_41_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer_21 = transpose(perm = var_891, x = x_127)[name = tensor<string, []>("transpose_67")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_41 = matmul(transpose_x = context_layer_41_transpose_x_0, transpose_y = context_layer_41_transpose_y_0, x = input_233, y = value_layer_21)[name = tensor<string, []>("context_layer_41")]; | |
| tensor<int32, [4]> var_907 = const()[name = tensor<string, []>("op_907"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_912 = const()[name = tensor<string, []>("op_912"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_908 = transpose(perm = var_907, x = context_layer_41)[name = tensor<string, []>("transpose_64")]; | |
| tensor<fp32, [1, 512, 768]> input_235 = reshape(shape = var_912, x = var_908)[name = tensor<string, []>("input_235")]; | |
| tensor<fp32, [1, 512, 768]> input_237 = linear(bias = text_branch_encoder_layer_10_attention_output_dense_bias, weight = text_branch_encoder_layer_10_attention_output_dense_weight, x = input_235)[name = tensor<string, []>("linear_63")]; | |
| tensor<fp32, [1, 512, 768]> input_239 = add(x = input_237, y = input_229)[name = tensor<string, []>("input_239")]; | |
| tensor<int32, [1]> input_241_axes_0 = const()[name = tensor<string, []>("input_241_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_241 = layer_norm(axes = input_241_axes_0, beta = text_branch_encoder_layer_10_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_10_attention_output_LayerNorm_weight, x = input_239)[name = tensor<string, []>("input_241")]; | |
| tensor<fp32, [1, 512, 3072]> input_243 = linear(bias = text_branch_encoder_layer_10_intermediate_dense_bias, weight = text_branch_encoder_layer_10_intermediate_dense_weight, x = input_241)[name = tensor<string, []>("linear_64")]; | |
| tensor<string, []> input_245_mode_0 = const()[name = tensor<string, []>("input_245_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_245 = gelu(mode = input_245_mode_0, x = input_243)[name = tensor<string, []>("input_245")]; | |
| tensor<fp32, [1, 512, 768]> input_247 = linear(bias = text_branch_encoder_layer_10_output_dense_bias, weight = text_branch_encoder_layer_10_output_dense_weight, x = input_245)[name = tensor<string, []>("linear_65")]; | |
| tensor<fp32, [1, 512, 768]> input_249 = add(x = input_247, y = input_241)[name = tensor<string, []>("input_249")]; | |
| tensor<int32, [1]> input_251_axes_0 = const()[name = tensor<string, []>("input_251_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_251 = layer_norm(axes = input_251_axes_0, beta = text_branch_encoder_layer_10_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_10_output_LayerNorm_weight, x = input_249)[name = tensor<string, []>("input_251")]; | |
| tensor<fp32, [1, 512, 768]> x_141 = linear(bias = text_branch_encoder_layer_11_attention_self_query_bias, weight = text_branch_encoder_layer_11_attention_self_query_weight, x = input_251)[name = tensor<string, []>("linear_66")]; | |
| tensor<fp32, [1, 512, 768]> x_133 = linear(bias = text_branch_encoder_layer_11_attention_self_key_bias, weight = text_branch_encoder_layer_11_attention_self_key_weight, x = input_251)[name = tensor<string, []>("linear_67")]; | |
| tensor<int32, [4]> var_957 = const()[name = tensor<string, []>("op_957"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_135 = reshape(shape = var_957, x = x_133)[name = tensor<string, []>("x_135")]; | |
| tensor<fp32, [1, 512, 768]> x_137 = linear(bias = text_branch_encoder_layer_11_attention_self_value_bias, weight = text_branch_encoder_layer_11_attention_self_value_weight, x = input_251)[name = tensor<string, []>("linear_68")]; | |
| tensor<int32, [4]> var_966 = const()[name = tensor<string, []>("op_966"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x_139 = reshape(shape = var_966, x = x_137)[name = tensor<string, []>("x_139")]; | |
| tensor<int32, [4]> var_968 = const()[name = tensor<string, []>("op_968"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [4]> var_972 = const()[name = tensor<string, []>("op_972"), val = tensor<int32, [4]>([1, 512, 12, 64])]; | |
| tensor<fp32, [1, 512, 12, 64]> x = reshape(shape = var_972, x = x_141)[name = tensor<string, []>("x")]; | |
| tensor<bool, []> attention_scores_45_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_45_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> attention_scores_45_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_45_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<int32, [4]> transpose_58_perm_0 = const()[name = tensor<string, []>("transpose_58_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; | |
| tensor<int32, [4]> transpose_59_perm_0 = const()[name = tensor<string, []>("transpose_59_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; | |
| tensor<fp32, [1, 12, 64, 512]> transpose_59 = transpose(perm = transpose_59_perm_0, x = x_135)[name = tensor<string, []>("transpose_61")]; | |
| tensor<fp32, [1, 12, 512, 64]> transpose_58 = transpose(perm = transpose_58_perm_0, x = x)[name = tensor<string, []>("transpose_62")]; | |
| tensor<fp32, [1, 12, 512, 512]> attention_scores_45 = matmul(transpose_x = attention_scores_45_transpose_x_0, transpose_y = attention_scores_45_transpose_y_0, x = transpose_58, y = transpose_59)[name = tensor<string, []>("attention_scores_45")]; | |
| tensor<fp32, []> _inversed_attention_scores_y_0 = const()[name = tensor<string, []>("_inversed_attention_scores_y_0"), val = tensor<fp32, []>(0x1p-3)]; | |
| tensor<fp32, [1, 12, 512, 512]> _inversed_attention_scores = mul(x = attention_scores_45, y = _inversed_attention_scores_y_0)[name = tensor<string, []>("_inversed_attention_scores")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_253 = add(x = _inversed_attention_scores, y = attention_mask_1)[name = tensor<string, []>("input_253")]; | |
| tensor<fp32, [1, 12, 512, 512]> input_255 = softmax(axis = var_10, x = input_253)[name = tensor<string, []>("input_255")]; | |
| tensor<bool, []> context_layer_45_transpose_x_0 = const()[name = tensor<string, []>("context_layer_45_transpose_x_0"), val = tensor<bool, []>(false)]; | |
| tensor<bool, []> context_layer_45_transpose_y_0 = const()[name = tensor<string, []>("context_layer_45_transpose_y_0"), val = tensor<bool, []>(false)]; | |
| tensor<fp32, [1, 12, 512, 64]> value_layer = transpose(perm = var_968, x = x_139)[name = tensor<string, []>("transpose_63")]; | |
| tensor<fp32, [1, 12, 512, 64]> context_layer_45 = matmul(transpose_x = context_layer_45_transpose_x_0, transpose_y = context_layer_45_transpose_y_0, x = input_255, y = value_layer)[name = tensor<string, []>("context_layer_45")]; | |
| tensor<int32, [4]> var_984 = const()[name = tensor<string, []>("op_984"), val = tensor<int32, [4]>([0, 2, 1, 3])]; | |
| tensor<int32, [3]> var_989 = const()[name = tensor<string, []>("op_989"), val = tensor<int32, [3]>([1, 512, 768])]; | |
| tensor<fp32, [1, 512, 12, 64]> var_985 = transpose(perm = var_984, x = context_layer_45)[name = tensor<string, []>("transpose_60")]; | |
| tensor<fp32, [1, 512, 768]> input_257 = reshape(shape = var_989, x = var_985)[name = tensor<string, []>("input_257")]; | |
| tensor<fp32, [1, 512, 768]> input_259 = linear(bias = text_branch_encoder_layer_11_attention_output_dense_bias, weight = text_branch_encoder_layer_11_attention_output_dense_weight, x = input_257)[name = tensor<string, []>("linear_69")]; | |
| tensor<fp32, [1, 512, 768]> input_261 = add(x = input_259, y = input_251)[name = tensor<string, []>("input_261")]; | |
| tensor<int32, [1]> input_263_axes_0 = const()[name = tensor<string, []>("input_263_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> input_263 = layer_norm(axes = input_263_axes_0, beta = text_branch_encoder_layer_11_attention_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_11_attention_output_LayerNorm_weight, x = input_261)[name = tensor<string, []>("input_263")]; | |
| tensor<fp32, [1, 512, 3072]> input_265 = linear(bias = text_branch_encoder_layer_11_intermediate_dense_bias, weight = text_branch_encoder_layer_11_intermediate_dense_weight, x = input_263)[name = tensor<string, []>("linear_70")]; | |
| tensor<string, []> input_267_mode_0 = const()[name = tensor<string, []>("input_267_mode_0"), val = tensor<string, []>("EXACT")]; | |
| tensor<fp32, [1, 512, 3072]> input_267 = gelu(mode = input_267_mode_0, x = input_265)[name = tensor<string, []>("input_267")]; | |
| tensor<fp32, [1, 512, 768]> input_269 = linear(bias = text_branch_encoder_layer_11_output_dense_bias, weight = text_branch_encoder_layer_11_output_dense_weight, x = input_267)[name = tensor<string, []>("linear_71")]; | |
| tensor<fp32, [1, 512, 768]> input_271 = add(x = input_269, y = input_263)[name = tensor<string, []>("input_271")]; | |
| tensor<int32, [1]> hidden_states_axes_0 = const()[name = tensor<string, []>("hidden_states_axes_0"), val = tensor<int32, [1]>([-1])]; | |
| tensor<fp32, [1, 512, 768]> hidden_states = layer_norm(axes = hidden_states_axes_0, beta = text_branch_encoder_layer_11_output_LayerNorm_bias, epsilon = var_12, gamma = text_branch_encoder_layer_11_output_LayerNorm_weight, x = input_271)[name = tensor<string, []>("hidden_states")]; | |
| tensor<int32, [3]> input_273_begin_0 = const()[name = tensor<string, []>("input_273_begin_0"), val = tensor<int32, [3]>([0, 0, 0])]; | |
| tensor<int32, [3]> input_273_end_0 = const()[name = tensor<string, []>("input_273_end_0"), val = tensor<int32, [3]>([1, 1, 768])]; | |
| tensor<bool, [3]> input_273_end_mask_0 = const()[name = tensor<string, []>("input_273_end_mask_0"), val = tensor<bool, [3]>([true, false, true])]; | |
| tensor<bool, [3]> input_273_squeeze_mask_0 = const()[name = tensor<string, []>("input_273_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])]; | |
| tensor<fp32, [1, 768]> input_273 = slice_by_index(begin = input_273_begin_0, end = input_273_end_0, end_mask = input_273_end_mask_0, squeeze_mask = input_273_squeeze_mask_0, x = hidden_states)[name = tensor<string, []>("input_273")]; | |
| tensor<fp32, [1, 768]> input_275 = linear(bias = text_branch_pooler_dense_bias, weight = text_branch_pooler_dense_weight, x = input_273)[name = tensor<string, []>("linear_72")]; | |
| tensor<fp32, [1, 768]> input_277 = tanh(x = input_275)[name = tensor<string, []>("input_277")]; | |
| tensor<fp32, [1, 512]> input_279 = linear(bias = text_projection_0_bias, weight = text_projection_0_weight, x = input_277)[name = tensor<string, []>("linear_73")]; | |
| tensor<fp32, [1, 512]> input_281 = relu(x = input_279)[name = tensor<string, []>("input_281")]; | |
| tensor<fp32, [1, 512]> input = linear(bias = text_projection_2_bias, weight = text_projection_2_weight, x = input_281)[name = tensor<string, []>("linear_74")]; | |
| tensor<int32, [1]> var_1036 = const()[name = tensor<string, []>("op_1036"), val = tensor<int32, [1]>([-1])]; | |
| tensor<bool, []> var_1037 = const()[name = tensor<string, []>("op_1037"), val = tensor<bool, []>(true)]; | |
| tensor<fp32, [1, 1]> var_1039 = reduce_l2_norm(axes = var_1036, keep_dims = var_1037, x = input)[name = tensor<string, []>("op_1039")]; | |
| tensor<fp32, []> var_1040 = const()[name = tensor<string, []>("op_1040"), val = tensor<fp32, []>(0x1.197998p-40)]; | |
| tensor<fp32, [1, 1]> var_1041 = maximum(x = var_1039, y = var_1040)[name = tensor<string, []>("op_1041")]; | |
| tensor<int32, [2]> denom_reps_0 = const()[name = tensor<string, []>("denom_reps_0"), val = tensor<int32, [2]>([1, 512])]; | |
| tensor<fp32, [1, 512]> denom = tile(reps = denom_reps_0, x = var_1041)[name = tensor<string, []>("denom")]; | |
| tensor<fp32, [1, 512]> text_embedding = real_div(x = input, y = denom)[name = tensor<string, []>("op_1043")]; | |
| } -> (text_embedding); | |
| } |