| program(1.0) |
| [buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] |
| { |
| func main<ios17>(tensor<int32, [1, ?]> input_ids) [FlexibleShapeInformation = tuple<tuple<tensor<string, []>, dict<tensor<string, []>, tensor<int32, [?]>>>, tuple<tensor<string, []>, dict<tensor<string, []>, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"input_ids", [1, 45]}}), ("RangeDims", {{"input_ids", [[1, 1], [3, 512]]}})))] { |
| tensor<fp32, [256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))]; |
| tensor<fp32, [256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152)))]; |
| tensor<fp32, [256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2240)))]; |
| tensor<fp32, [256, 768]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight"), val = tensor<fp32, [256, 768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3328)))]; |
| tensor<fp32, [768]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias"), val = tensor<fp32, [768]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(789824)))]; |
| tensor<fp32, [768, 256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight"), val = tensor<fp32, [768, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(792960)))]; |
| tensor<fp32, [256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1579456)))]; |
| tensor<fp32, [256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1580544)))]; |
| tensor<fp32, [256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1581632)))]; |
| tensor<fp32, [256, 256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1582720)))]; |
| tensor<fp32, [256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1844928)))]; |
| tensor<fp32, [256, 256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1846016)))]; |
| tensor<fp32, [256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2108224)))]; |
| tensor<fp32, [256, 256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2109312)))]; |
| tensor<fp32, [256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2371520)))]; |
| tensor<fp32, [256, 256]> ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight = const()[name = tensor<string, []>("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight"), val = tensor<fp32, [256, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2372608)))]; |
| tensor<fp32, [256]> ts_bert_encoder_embedding_hidden_mapping_in_bias = const()[name = tensor<string, []>("ts_bert_encoder_embedding_hidden_mapping_in_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2634816)))]; |
| tensor<fp32, [256, 128]> ts_bert_encoder_embedding_hidden_mapping_in_weight = const()[name = tensor<string, []>("ts_bert_encoder_embedding_hidden_mapping_in_weight"), val = tensor<fp32, [256, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2635904)))]; |
| tensor<fp32, [128]> ts_bert_embeddings_LayerNorm_bias = const()[name = tensor<string, []>("ts_bert_embeddings_LayerNorm_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2767040)))]; |
| tensor<fp32, [128]> ts_bert_embeddings_LayerNorm_weight = const()[name = tensor<string, []>("ts_bert_embeddings_LayerNorm_weight"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2767616)))]; |
| tensor<fp32, [2, 128]> ts_bert_embeddings_token_type_embeddings_weight = const()[name = tensor<string, []>("ts_bert_embeddings_token_type_embeddings_weight"), val = tensor<fp32, [2, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2768192)))]; |
| tensor<fp32, [512, 128]> ts_bert_embeddings_position_embeddings_weight = const()[name = tensor<string, []>("ts_bert_embeddings_position_embeddings_weight"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2769280)))]; |
| tensor<fp32, [178, 128]> ts_bert_embeddings_word_embeddings_weight = const()[name = tensor<string, []>("ts_bert_embeddings_word_embeddings_weight"), val = tensor<fp32, [178, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3031488)))]; |
| tensor<fp32, [192]> ts_bert_encoder_bias = const()[name = tensor<string, []>("ts_bert_encoder_bias"), val = tensor<fp32, [192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3122688)))]; |
| tensor<fp32, [192, 256]> ts_bert_encoder_weight = const()[name = tensor<string, []>("ts_bert_encoder_weight"), val = tensor<fp32, [192, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3123520)))]; |
| tensor<fp32, [50]> ts_predictor_duration_proj_linear_layer_bias = const()[name = tensor<string, []>("ts_predictor_duration_proj_linear_layer_bias"), val = tensor<fp32, [50]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3320192)))]; |
| tensor<fp32, [50, 192]> ts_predictor_duration_proj_linear_layer_weight = const()[name = tensor<string, []>("ts_predictor_duration_proj_linear_layer_weight"), val = tensor<fp32, [50, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3320512)))]; |
| tensor<fp32, [178, 192]> ts_text_encoder_embedding_weight = const()[name = tensor<string, []>("ts_text_encoder_embedding_weight"), val = tensor<fp32, [178, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3358976)))]; |
| tensor<fp32, [192]> ts_text_encoder_cnn_0_0_bias = const()[name = tensor<string, []>("ts_text_encoder_cnn_0_0_bias"), val = tensor<fp32, [192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3495744)))]; |
| tensor<fp32, [192, 192, 5]> ts_text_encoder_cnn_0_0_weight = const()[name = tensor<string, []>("ts_text_encoder_cnn_0_0_weight"), val = tensor<fp32, [192, 192, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3496576)))]; |
| tensor<fp32, [192]> ts_text_encoder_cnn_0_1_beta = const()[name = tensor<string, []>("ts_text_encoder_cnn_0_1_beta"), val = tensor<fp32, [192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4233920)))]; |
| tensor<fp32, [192]> ts_text_encoder_cnn_0_1_gamma = const()[name = tensor<string, []>("ts_text_encoder_cnn_0_1_gamma"), val = tensor<fp32, [192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4234752)))]; |
| tensor<fp32, [192]> ts_text_encoder_cnn_1_0_bias = const()[name = tensor<string, []>("ts_text_encoder_cnn_1_0_bias"), val = tensor<fp32, [192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4235584)))]; |
| tensor<fp32, [192, 192, 5]> ts_text_encoder_cnn_1_0_weight = const()[name = tensor<string, []>("ts_text_encoder_cnn_1_0_weight"), val = tensor<fp32, [192, 192, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4236416)))]; |
| tensor<fp32, [192]> ts_text_encoder_cnn_1_1_beta = const()[name = tensor<string, []>("ts_text_encoder_cnn_1_1_beta"), val = tensor<fp32, [192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4973760)))]; |
| tensor<fp32, [192]> ts_text_encoder_cnn_1_1_gamma = const()[name = tensor<string, []>("ts_text_encoder_cnn_1_1_gamma"), val = tensor<fp32, [192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4974592)))]; |
| tensor<fp32, [192]> ts_text_encoder_cnn_2_0_bias = const()[name = tensor<string, []>("ts_text_encoder_cnn_2_0_bias"), val = tensor<fp32, [192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4975424)))]; |
| tensor<fp32, [192, 192, 5]> ts_text_encoder_cnn_2_0_weight = const()[name = tensor<string, []>("ts_text_encoder_cnn_2_0_weight"), val = tensor<fp32, [192, 192, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4976256)))]; |
| tensor<fp32, [192]> ts_text_encoder_cnn_2_1_beta = const()[name = tensor<string, []>("ts_text_encoder_cnn_2_1_beta"), val = tensor<fp32, [192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5713600)))]; |
| tensor<fp32, [192]> ts_text_encoder_cnn_2_1_gamma = const()[name = tensor<string, []>("ts_text_encoder_cnn_2_1_gamma"), val = tensor<fp32, [192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5714432)))]; |
| tensor<fp32, [512]> ts_asr_proj_0_bias = const()[name = tensor<string, []>("ts_asr_proj_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5715264)))]; |
| tensor<fp32, [512, 192]> ts_asr_proj_0_weight = const()[name = tensor<string, []>("ts_asr_proj_0_weight"), val = tensor<fp32, [512, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5717376)))]; |
| tensor<fp32, [512]> ts_asr_proj_2_bias = const()[name = tensor<string, []>("ts_asr_proj_2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6110656)))]; |
| tensor<fp32, [512, 512]> ts_asr_proj_2_weight = const()[name = tensor<string, []>("ts_asr_proj_2_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6112768)))]; |
| tensor<fp32, []> var_258 = const()[name = tensor<string, []>("op_258"), val = tensor<fp32, []>(0x1.197998p-40)]; |
| tensor<int32, []> var_260 = const()[name = tensor<string, []>("op_260"), val = tensor<int32, []>(-1)]; |
| tensor<int32, [2]> var_266_shape = shape(x = input_ids)[name = tensor<string, []>("op_266_shape")]; |
| tensor<int32, []> gather_0_batch_dims_0 = const()[name = tensor<string, []>("gather_0_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> gather_0_validate_indices_0 = const()[name = tensor<string, []>("gather_0_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, []> select_1 = const()[name = tensor<string, []>("select_1"), val = tensor<int32, []>(1)]; |
| tensor<int32, []> gather_0_axis_1 = const()[name = tensor<string, []>("gather_0_axis_1"), val = tensor<int32, []>(0)]; |
| tensor<int32, []> gather_0 = gather(axis = gather_0_axis_1, batch_dims = gather_0_batch_dims_0, indices = select_1, validate_indices = gather_0_validate_indices_0, x = var_266_shape)[name = tensor<string, []>("gather_0")]; |
| tensor<int32, []> const_0 = const()[name = tensor<string, []>("const_0"), val = tensor<int32, []>(0)]; |
| tensor<int32, []> const_1 = const()[name = tensor<string, []>("const_1"), val = tensor<int32, []>(1)]; |
| tensor<int32, [?]> var_267 = range_1d(end = gather_0, start = const_0, step = const_1)[name = tensor<string, []>("op_267")]; |
| tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([0])]; |
| tensor<int32, [1, ?]> input_1 = expand_dims(axes = input_1_axes_0, x = var_267)[name = tensor<string, []>("input_1")]; |
| tensor<int32, []> var_269_batch_dims_0 = const()[name = tensor<string, []>("op_269_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> var_269_validate_indices_0 = const()[name = tensor<string, []>("op_269_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, ?]> greater_equal_1 = greater_equal(x = input_ids, 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, []>(178)]; |
| tensor<int32, [1, ?]> add_11 = add(x = input_ids, y = slice_by_index_1)[name = tensor<string, []>("add_11")]; |
| tensor<int32, [1, ?]> select_2 = select(a = input_ids, b = add_11, cond = greater_equal_1)[name = tensor<string, []>("select_2")]; |
| tensor<int32, []> var_269_axis_1 = const()[name = tensor<string, []>("op_269_axis_1"), val = tensor<int32, []>(0)]; |
| tensor<fp32, [1, ?, 128]> var_269 = gather(axis = var_269_axis_1, batch_dims = var_269_batch_dims_0, indices = select_2, validate_indices = var_269_validate_indices_0, x = ts_bert_embeddings_word_embeddings_weight)[name = tensor<string, []>("op_269")]; |
| tensor<int32, []> var_270_batch_dims_0 = const()[name = tensor<string, []>("op_270_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> var_270_validate_indices_0 = const()[name = tensor<string, []>("op_270_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, []> greater_equal_2_y_0 = const()[name = tensor<string, []>("greater_equal_2_y_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, [1, ?]> greater_equal_2 = greater_equal(x = input_1, y = greater_equal_2_y_0)[name = tensor<string, []>("greater_equal_2")]; |
| tensor<int32, []> slice_by_index_2 = const()[name = tensor<string, []>("slice_by_index_2"), val = tensor<int32, []>(512)]; |
| tensor<int32, [1, ?]> add_12 = add(x = input_1, y = slice_by_index_2)[name = tensor<string, []>("add_12")]; |
| tensor<int32, [1, ?]> select_3 = select(a = input_1, b = add_12, cond = greater_equal_2)[name = tensor<string, []>("select_3")]; |
| tensor<int32, []> var_270_axis_1 = const()[name = tensor<string, []>("op_270_axis_1"), val = tensor<int32, []>(0)]; |
| tensor<fp32, [1, ?, 128]> var_270 = gather(axis = var_270_axis_1, batch_dims = var_270_batch_dims_0, indices = select_3, validate_indices = var_270_validate_indices_0, x = ts_bert_embeddings_position_embeddings_weight)[name = tensor<string, []>("op_270")]; |
| tensor<fp32, [1, ?, 128]> var_271 = add(x = var_269, y = var_270)[name = tensor<string, []>("op_271")]; |
| tensor<int32, [1, ?]> input_3 = sub(x = input_ids, y = input_ids)[name = tensor<string, []>("sub_0")]; |
| tensor<int32, []> var_273_batch_dims_0 = const()[name = tensor<string, []>("op_273_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> var_273_validate_indices_0 = const()[name = tensor<string, []>("op_273_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, []> greater_equal_3_y_0 = const()[name = tensor<string, []>("greater_equal_3_y_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, [1, ?]> greater_equal_3 = greater_equal(x = input_3, y = greater_equal_3_y_0)[name = tensor<string, []>("greater_equal_3")]; |
| tensor<int32, []> slice_by_index_3 = const()[name = tensor<string, []>("slice_by_index_3"), val = tensor<int32, []>(2)]; |
| tensor<int32, [1, ?]> add_13 = add(x = input_3, y = slice_by_index_3)[name = tensor<string, []>("add_13")]; |
| tensor<int32, [1, ?]> select_4 = select(a = input_3, b = add_13, cond = greater_equal_3)[name = tensor<string, []>("select_4")]; |
| tensor<int32, []> var_273_axis_1 = const()[name = tensor<string, []>("op_273_axis_1"), val = tensor<int32, []>(0)]; |
| tensor<fp32, [1, ?, 128]> var_273 = gather(axis = var_273_axis_1, batch_dims = var_273_batch_dims_0, indices = select_4, validate_indices = var_273_validate_indices_0, x = ts_bert_embeddings_token_type_embeddings_weight)[name = tensor<string, []>("op_273")]; |
| tensor<fp32, [1, ?, 128]> input_5 = add(x = var_271, y = var_273)[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, ?, 128]> input_7 = layer_norm(axes = input_7_axes_0, beta = ts_bert_embeddings_LayerNorm_bias, epsilon = var_258, gamma = ts_bert_embeddings_LayerNorm_weight, x = input_5)[name = tensor<string, []>("input_7")]; |
| tensor<fp32, [1, ?, 256]> input_9 = linear(bias = ts_bert_encoder_embedding_hidden_mapping_in_bias, weight = ts_bert_encoder_embedding_hidden_mapping_in_weight, x = input_7)[name = tensor<string, []>("linear_0")]; |
| tensor<fp32, [1, ?, 256]> var_278 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_9)[name = tensor<string, []>("linear_1")]; |
| tensor<int32, [4]> concat_0x = const()[name = tensor<string, []>("concat_0x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_280 = reshape(shape = concat_0x, x = var_278)[name = tensor<string, []>("op_280")]; |
| tensor<fp32, [1, ?, 256]> var_282 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_9)[name = tensor<string, []>("linear_2")]; |
| tensor<int32, [4]> concat_1x = const()[name = tensor<string, []>("concat_1x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_284 = reshape(shape = concat_1x, x = var_282)[name = tensor<string, []>("op_284")]; |
| tensor<fp32, [1, ?, 256]> var_286 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_9)[name = tensor<string, []>("linear_3")]; |
| tensor<int32, [4]> concat_2x = const()[name = tensor<string, []>("concat_2x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_288 = reshape(shape = concat_2x, x = var_286)[name = tensor<string, []>("op_288")]; |
| tensor<int32, [4]> v_1_perm_0 = const()[name = tensor<string, []>("v_1_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<bool, []> var_291_transpose_x_0 = const()[name = tensor<string, []>("op_291_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_291_transpose_y_0 = const()[name = tensor<string, []>("op_291_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_28_perm_0 = const()[name = tensor<string, []>("transpose_28_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_29_perm_0 = const()[name = tensor<string, []>("transpose_29_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; |
| tensor<fp32, [1, 4, 64, ?]> transpose_29 = transpose(perm = transpose_29_perm_0, x = var_284)[name = tensor<string, []>("transpose_82")]; |
| tensor<fp32, [1, 4, ?, 64]> transpose_28 = transpose(perm = transpose_28_perm_0, x = var_280)[name = tensor<string, []>("transpose_83")]; |
| tensor<fp32, [1, 4, ?, ?]> var_291 = matmul(transpose_x = var_291_transpose_x_0, transpose_y = var_291_transpose_y_0, x = transpose_28, y = transpose_29)[name = tensor<string, []>("op_291")]; |
| tensor<fp32, []> _inversed_input_11_y_0 = const()[name = tensor<string, []>("_inversed_input_11_y_0"), val = tensor<fp32, []>(0x1p-3)]; |
| tensor<fp32, [1, 4, ?, ?]> _inversed_input_11 = mul(x = var_291, y = _inversed_input_11_y_0)[name = tensor<string, []>("_inversed_input_11")]; |
| tensor<fp32, [1, 4, ?, ?]> att_1 = softmax(axis = var_260, x = _inversed_input_11)[name = tensor<string, []>("att_1")]; |
| tensor<bool, []> var_295_transpose_x_0 = const()[name = tensor<string, []>("op_295_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_295_transpose_y_0 = const()[name = tensor<string, []>("op_295_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 4, ?, 64]> v_1 = transpose(perm = v_1_perm_0, x = var_288)[name = tensor<string, []>("transpose_84")]; |
| tensor<fp32, [1, 4, ?, 64]> var_295 = matmul(transpose_x = var_295_transpose_x_0, transpose_y = var_295_transpose_y_0, x = att_1, y = v_1)[name = tensor<string, []>("op_295")]; |
| tensor<int32, [4]> var_296_perm_0 = const()[name = tensor<string, []>("op_296_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> concat_3x = const()[name = tensor<string, []>("concat_3x"), val = tensor<int32, [3]>([1, -1, 256])]; |
| tensor<fp32, [1, ?, 4, 64]> var_296 = transpose(perm = var_296_perm_0, x = var_295)[name = tensor<string, []>("transpose_81")]; |
| tensor<fp32, [1, ?, 256]> input_13 = reshape(shape = concat_3x, x = var_296)[name = tensor<string, []>("input_13")]; |
| tensor<fp32, [1, ?, 256]> var_299 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_13)[name = tensor<string, []>("linear_4")]; |
| tensor<fp32, [1, ?, 256]> input_15 = add(x = var_299, y = input_9)[name = tensor<string, []>("input_15")]; |
| tensor<int32, [1]> input_17_axes_0 = const()[name = tensor<string, []>("input_17_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 256]> input_17 = layer_norm(axes = input_17_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_15)[name = tensor<string, []>("input_17")]; |
| tensor<fp32, [1, ?, 768]> var_303 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_17)[name = tensor<string, []>("linear_5")]; |
| tensor<string, []> input_19_mode_0 = const()[name = tensor<string, []>("input_19_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")]; |
| tensor<fp32, [1, ?, 768]> input_19 = gelu(mode = input_19_mode_0, x = var_303)[name = tensor<string, []>("input_19")]; |
| tensor<fp32, [1, ?, 256]> f_1 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_19)[name = tensor<string, []>("linear_6")]; |
| tensor<fp32, [1, ?, 256]> input_21 = add(x = f_1, y = input_17)[name = tensor<string, []>("input_21")]; |
| tensor<int32, [1]> input_23_axes_0 = const()[name = tensor<string, []>("input_23_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 256]> input_23 = layer_norm(axes = input_23_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_21)[name = tensor<string, []>("input_23")]; |
| tensor<fp32, [1, ?, 256]> var_309 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_23)[name = tensor<string, []>("linear_7")]; |
| tensor<int32, [4]> concat_4x = const()[name = tensor<string, []>("concat_4x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_311 = reshape(shape = concat_4x, x = var_309)[name = tensor<string, []>("op_311")]; |
| tensor<fp32, [1, ?, 256]> var_313 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_23)[name = tensor<string, []>("linear_8")]; |
| tensor<int32, [4]> concat_5x = const()[name = tensor<string, []>("concat_5x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_315 = reshape(shape = concat_5x, x = var_313)[name = tensor<string, []>("op_315")]; |
| tensor<fp32, [1, ?, 256]> var_317 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_23)[name = tensor<string, []>("linear_9")]; |
| tensor<int32, [4]> concat_6x = const()[name = tensor<string, []>("concat_6x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_319 = reshape(shape = concat_6x, x = var_317)[name = tensor<string, []>("op_319")]; |
| tensor<int32, [4]> v_3_perm_0 = const()[name = tensor<string, []>("v_3_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<bool, []> var_322_transpose_x_0 = const()[name = tensor<string, []>("op_322_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_322_transpose_y_0 = const()[name = tensor<string, []>("op_322_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_30_perm_0 = const()[name = tensor<string, []>("transpose_30_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_31_perm_0 = const()[name = tensor<string, []>("transpose_31_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; |
| tensor<fp32, [1, 4, 64, ?]> transpose_31 = transpose(perm = transpose_31_perm_0, x = var_315)[name = tensor<string, []>("transpose_78")]; |
| tensor<fp32, [1, 4, ?, 64]> transpose_30 = transpose(perm = transpose_30_perm_0, x = var_311)[name = tensor<string, []>("transpose_79")]; |
| tensor<fp32, [1, 4, ?, ?]> var_322 = matmul(transpose_x = var_322_transpose_x_0, transpose_y = var_322_transpose_y_0, x = transpose_30, y = transpose_31)[name = tensor<string, []>("op_322")]; |
| tensor<fp32, []> _inversed_input_25_y_0 = const()[name = tensor<string, []>("_inversed_input_25_y_0"), val = tensor<fp32, []>(0x1p-3)]; |
| tensor<fp32, [1, 4, ?, ?]> _inversed_input_25 = mul(x = var_322, y = _inversed_input_25_y_0)[name = tensor<string, []>("_inversed_input_25")]; |
| tensor<fp32, [1, 4, ?, ?]> att_3 = softmax(axis = var_260, x = _inversed_input_25)[name = tensor<string, []>("att_3")]; |
| tensor<bool, []> var_326_transpose_x_0 = const()[name = tensor<string, []>("op_326_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_326_transpose_y_0 = const()[name = tensor<string, []>("op_326_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 4, ?, 64]> v_3 = transpose(perm = v_3_perm_0, x = var_319)[name = tensor<string, []>("transpose_80")]; |
| tensor<fp32, [1, 4, ?, 64]> var_326 = matmul(transpose_x = var_326_transpose_x_0, transpose_y = var_326_transpose_y_0, x = att_3, y = v_3)[name = tensor<string, []>("op_326")]; |
| tensor<int32, [4]> var_327_perm_0 = const()[name = tensor<string, []>("op_327_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> concat_7x = const()[name = tensor<string, []>("concat_7x"), val = tensor<int32, [3]>([1, -1, 256])]; |
| tensor<fp32, [1, ?, 4, 64]> var_327 = transpose(perm = var_327_perm_0, x = var_326)[name = tensor<string, []>("transpose_77")]; |
| tensor<fp32, [1, ?, 256]> input_27 = reshape(shape = concat_7x, x = var_327)[name = tensor<string, []>("input_27")]; |
| tensor<fp32, [1, ?, 256]> var_330 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_27)[name = tensor<string, []>("linear_10")]; |
| tensor<fp32, [1, ?, 256]> input_29 = add(x = var_330, y = input_23)[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, ?, 256]> input_31 = layer_norm(axes = input_31_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_29)[name = tensor<string, []>("input_31")]; |
| tensor<fp32, [1, ?, 768]> var_334 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_31)[name = tensor<string, []>("linear_11")]; |
| tensor<string, []> input_33_mode_0 = const()[name = tensor<string, []>("input_33_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")]; |
| tensor<fp32, [1, ?, 768]> input_33 = gelu(mode = input_33_mode_0, x = var_334)[name = tensor<string, []>("input_33")]; |
| tensor<fp32, [1, ?, 256]> f_3 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_33)[name = tensor<string, []>("linear_12")]; |
| tensor<fp32, [1, ?, 256]> input_35 = add(x = f_3, y = input_31)[name = tensor<string, []>("input_35")]; |
| tensor<int32, [1]> input_37_axes_0 = const()[name = tensor<string, []>("input_37_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 256]> input_37 = layer_norm(axes = input_37_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_35)[name = tensor<string, []>("input_37")]; |
| tensor<fp32, [1, ?, 256]> var_340 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_37)[name = tensor<string, []>("linear_13")]; |
| tensor<int32, [4]> concat_8x = const()[name = tensor<string, []>("concat_8x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_342 = reshape(shape = concat_8x, x = var_340)[name = tensor<string, []>("op_342")]; |
| tensor<fp32, [1, ?, 256]> var_344 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_37)[name = tensor<string, []>("linear_14")]; |
| tensor<int32, [4]> concat_9x = const()[name = tensor<string, []>("concat_9x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_346 = reshape(shape = concat_9x, x = var_344)[name = tensor<string, []>("op_346")]; |
| tensor<fp32, [1, ?, 256]> var_348 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_37)[name = tensor<string, []>("linear_15")]; |
| tensor<int32, [4]> concat_10x = const()[name = tensor<string, []>("concat_10x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_350 = reshape(shape = concat_10x, x = var_348)[name = tensor<string, []>("op_350")]; |
| tensor<int32, [4]> v_5_perm_0 = const()[name = tensor<string, []>("v_5_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<bool, []> var_353_transpose_x_0 = const()[name = tensor<string, []>("op_353_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_353_transpose_y_0 = const()[name = tensor<string, []>("op_353_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_32_perm_0 = const()[name = tensor<string, []>("transpose_32_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_33_perm_0 = const()[name = tensor<string, []>("transpose_33_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; |
| tensor<fp32, [1, 4, 64, ?]> transpose_33 = transpose(perm = transpose_33_perm_0, x = var_346)[name = tensor<string, []>("transpose_74")]; |
| tensor<fp32, [1, 4, ?, 64]> transpose_32 = transpose(perm = transpose_32_perm_0, x = var_342)[name = tensor<string, []>("transpose_75")]; |
| tensor<fp32, [1, 4, ?, ?]> var_353 = matmul(transpose_x = var_353_transpose_x_0, transpose_y = var_353_transpose_y_0, x = transpose_32, y = transpose_33)[name = tensor<string, []>("op_353")]; |
| tensor<fp32, []> _inversed_input_39_y_0 = const()[name = tensor<string, []>("_inversed_input_39_y_0"), val = tensor<fp32, []>(0x1p-3)]; |
| tensor<fp32, [1, 4, ?, ?]> _inversed_input_39 = mul(x = var_353, y = _inversed_input_39_y_0)[name = tensor<string, []>("_inversed_input_39")]; |
| tensor<fp32, [1, 4, ?, ?]> att_5 = softmax(axis = var_260, x = _inversed_input_39)[name = tensor<string, []>("att_5")]; |
| tensor<bool, []> var_357_transpose_x_0 = const()[name = tensor<string, []>("op_357_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_357_transpose_y_0 = const()[name = tensor<string, []>("op_357_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 4, ?, 64]> v_5 = transpose(perm = v_5_perm_0, x = var_350)[name = tensor<string, []>("transpose_76")]; |
| tensor<fp32, [1, 4, ?, 64]> var_357 = matmul(transpose_x = var_357_transpose_x_0, transpose_y = var_357_transpose_y_0, x = att_5, y = v_5)[name = tensor<string, []>("op_357")]; |
| tensor<int32, [4]> var_358_perm_0 = const()[name = tensor<string, []>("op_358_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> concat_11x = const()[name = tensor<string, []>("concat_11x"), val = tensor<int32, [3]>([1, -1, 256])]; |
| tensor<fp32, [1, ?, 4, 64]> var_358 = transpose(perm = var_358_perm_0, x = var_357)[name = tensor<string, []>("transpose_73")]; |
| tensor<fp32, [1, ?, 256]> input_41 = reshape(shape = concat_11x, x = var_358)[name = tensor<string, []>("input_41")]; |
| tensor<fp32, [1, ?, 256]> var_361 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_41)[name = tensor<string, []>("linear_16")]; |
| tensor<fp32, [1, ?, 256]> input_43 = add(x = var_361, y = input_37)[name = tensor<string, []>("input_43")]; |
| tensor<int32, [1]> input_45_axes_0 = const()[name = tensor<string, []>("input_45_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 256]> input_45 = layer_norm(axes = input_45_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_43)[name = tensor<string, []>("input_45")]; |
| tensor<fp32, [1, ?, 768]> var_365 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_45)[name = tensor<string, []>("linear_17")]; |
| tensor<string, []> input_47_mode_0 = const()[name = tensor<string, []>("input_47_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")]; |
| tensor<fp32, [1, ?, 768]> input_47 = gelu(mode = input_47_mode_0, x = var_365)[name = tensor<string, []>("input_47")]; |
| tensor<fp32, [1, ?, 256]> f_5 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_47)[name = tensor<string, []>("linear_18")]; |
| tensor<fp32, [1, ?, 256]> input_49 = add(x = f_5, y = input_45)[name = tensor<string, []>("input_49")]; |
| tensor<int32, [1]> input_51_axes_0 = const()[name = tensor<string, []>("input_51_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 256]> input_51 = layer_norm(axes = input_51_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_49)[name = tensor<string, []>("input_51")]; |
| tensor<fp32, [1, ?, 256]> var_371 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_51)[name = tensor<string, []>("linear_19")]; |
| tensor<int32, [4]> concat_12x = const()[name = tensor<string, []>("concat_12x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_373 = reshape(shape = concat_12x, x = var_371)[name = tensor<string, []>("op_373")]; |
| tensor<fp32, [1, ?, 256]> var_375 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_51)[name = tensor<string, []>("linear_20")]; |
| tensor<int32, [4]> concat_13x = const()[name = tensor<string, []>("concat_13x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_377 = reshape(shape = concat_13x, x = var_375)[name = tensor<string, []>("op_377")]; |
| tensor<fp32, [1, ?, 256]> var_379 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_51)[name = tensor<string, []>("linear_21")]; |
| tensor<int32, [4]> concat_14x = const()[name = tensor<string, []>("concat_14x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_381 = reshape(shape = concat_14x, x = var_379)[name = tensor<string, []>("op_381")]; |
| tensor<int32, [4]> v_7_perm_0 = const()[name = tensor<string, []>("v_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<bool, []> var_384_transpose_x_0 = const()[name = tensor<string, []>("op_384_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_384_transpose_y_0 = const()[name = tensor<string, []>("op_384_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [4]> transpose_34_perm_0 = const()[name = tensor<string, []>("transpose_34_perm_0"), val = tensor<int32, [4]>([0, 2, -3, -1])]; |
| tensor<int32, [4]> transpose_35_perm_0 = const()[name = tensor<string, []>("transpose_35_perm_0"), val = tensor<int32, [4]>([0, 2, -1, -3])]; |
| tensor<fp32, [1, 4, 64, ?]> transpose_35 = transpose(perm = transpose_35_perm_0, x = var_377)[name = tensor<string, []>("transpose_70")]; |
| tensor<fp32, [1, 4, ?, 64]> transpose_34 = transpose(perm = transpose_34_perm_0, x = var_373)[name = tensor<string, []>("transpose_71")]; |
| tensor<fp32, [1, 4, ?, ?]> var_384 = matmul(transpose_x = var_384_transpose_x_0, transpose_y = var_384_transpose_y_0, x = transpose_34, y = transpose_35)[name = tensor<string, []>("op_384")]; |
| tensor<fp32, []> _inversed_input_53_y_0 = const()[name = tensor<string, []>("_inversed_input_53_y_0"), val = tensor<fp32, []>(0x1p-3)]; |
| tensor<fp32, [1, 4, ?, ?]> _inversed_input_53 = mul(x = var_384, y = _inversed_input_53_y_0)[name = tensor<string, []>("_inversed_input_53")]; |
| tensor<fp32, [1, 4, ?, ?]> att_7 = softmax(axis = var_260, x = _inversed_input_53)[name = tensor<string, []>("att_7")]; |
| tensor<bool, []> var_388_transpose_x_0 = const()[name = tensor<string, []>("op_388_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_388_transpose_y_0 = const()[name = tensor<string, []>("op_388_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 4, ?, 64]> v_7 = transpose(perm = v_7_perm_0, x = var_381)[name = tensor<string, []>("transpose_72")]; |
| tensor<fp32, [1, 4, ?, 64]> var_388 = matmul(transpose_x = var_388_transpose_x_0, transpose_y = var_388_transpose_y_0, x = att_7, y = v_7)[name = tensor<string, []>("op_388")]; |
| tensor<int32, [4]> var_389_perm_0 = const()[name = tensor<string, []>("op_389_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> concat_15x = const()[name = tensor<string, []>("concat_15x"), val = tensor<int32, [3]>([1, -1, 256])]; |
| tensor<fp32, [1, ?, 4, 64]> var_389 = transpose(perm = var_389_perm_0, x = var_388)[name = tensor<string, []>("transpose_69")]; |
| tensor<fp32, [1, ?, 256]> input_55 = reshape(shape = concat_15x, x = var_389)[name = tensor<string, []>("input_55")]; |
| tensor<fp32, [1, ?, 256]> var_392 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_55)[name = tensor<string, []>("linear_22")]; |
| tensor<fp32, [1, ?, 256]> input_57 = add(x = var_392, y = input_51)[name = tensor<string, []>("input_57")]; |
| tensor<int32, [1]> input_59_axes_0 = const()[name = tensor<string, []>("input_59_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 256]> input_59 = layer_norm(axes = input_59_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_57)[name = tensor<string, []>("input_59")]; |
| tensor<fp32, [1, ?, 768]> var_396 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_59)[name = tensor<string, []>("linear_23")]; |
| tensor<string, []> input_61_mode_0 = const()[name = tensor<string, []>("input_61_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")]; |
| tensor<fp32, [1, ?, 768]> input_61 = gelu(mode = input_61_mode_0, x = var_396)[name = tensor<string, []>("input_61")]; |
| tensor<fp32, [1, ?, 256]> f_7 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_61)[name = tensor<string, []>("linear_24")]; |
| tensor<fp32, [1, ?, 256]> input_63 = add(x = f_7, y = input_59)[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, ?, 256]> input_65 = layer_norm(axes = input_65_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_63)[name = tensor<string, []>("input_65")]; |
| tensor<fp32, [1, ?, 256]> var_402 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_65)[name = tensor<string, []>("linear_25")]; |
| tensor<int32, [4]> concat_16x = const()[name = tensor<string, []>("concat_16x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_404 = reshape(shape = concat_16x, x = var_402)[name = tensor<string, []>("op_404")]; |
| tensor<fp32, [1, ?, 256]> var_406 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_65)[name = tensor<string, []>("linear_26")]; |
| tensor<int32, [4]> concat_17x = const()[name = tensor<string, []>("concat_17x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_408 = reshape(shape = concat_17x, x = var_406)[name = tensor<string, []>("op_408")]; |
| tensor<fp32, [1, ?, 256]> var_410 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_65)[name = tensor<string, []>("linear_27")]; |
| tensor<int32, [4]> concat_18x = const()[name = tensor<string, []>("concat_18x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_412 = reshape(shape = concat_18x, x = var_410)[name = tensor<string, []>("op_412")]; |
| tensor<int32, [4]> v_9_perm_0 = const()[name = tensor<string, []>("v_9_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<bool, []> var_415_transpose_x_0 = const()[name = tensor<string, []>("op_415_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_415_transpose_y_0 = const()[name = tensor<string, []>("op_415_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, 4, 64, ?]> transpose_37 = transpose(perm = transpose_37_perm_0, x = var_408)[name = tensor<string, []>("transpose_66")]; |
| tensor<fp32, [1, 4, ?, 64]> transpose_36 = transpose(perm = transpose_36_perm_0, x = var_404)[name = tensor<string, []>("transpose_67")]; |
| tensor<fp32, [1, 4, ?, ?]> var_415 = matmul(transpose_x = var_415_transpose_x_0, transpose_y = var_415_transpose_y_0, x = transpose_36, y = transpose_37)[name = tensor<string, []>("op_415")]; |
| tensor<fp32, []> _inversed_input_67_y_0 = const()[name = tensor<string, []>("_inversed_input_67_y_0"), val = tensor<fp32, []>(0x1p-3)]; |
| tensor<fp32, [1, 4, ?, ?]> _inversed_input_67 = mul(x = var_415, y = _inversed_input_67_y_0)[name = tensor<string, []>("_inversed_input_67")]; |
| tensor<fp32, [1, 4, ?, ?]> att_9 = softmax(axis = var_260, x = _inversed_input_67)[name = tensor<string, []>("att_9")]; |
| tensor<bool, []> var_419_transpose_x_0 = const()[name = tensor<string, []>("op_419_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_419_transpose_y_0 = const()[name = tensor<string, []>("op_419_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 4, ?, 64]> v_9 = transpose(perm = v_9_perm_0, x = var_412)[name = tensor<string, []>("transpose_68")]; |
| tensor<fp32, [1, 4, ?, 64]> var_419 = matmul(transpose_x = var_419_transpose_x_0, transpose_y = var_419_transpose_y_0, x = att_9, y = v_9)[name = tensor<string, []>("op_419")]; |
| tensor<int32, [4]> var_420_perm_0 = const()[name = tensor<string, []>("op_420_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> concat_19x = const()[name = tensor<string, []>("concat_19x"), val = tensor<int32, [3]>([1, -1, 256])]; |
| tensor<fp32, [1, ?, 4, 64]> var_420 = transpose(perm = var_420_perm_0, x = var_419)[name = tensor<string, []>("transpose_65")]; |
| tensor<fp32, [1, ?, 256]> input_69 = reshape(shape = concat_19x, x = var_420)[name = tensor<string, []>("input_69")]; |
| tensor<fp32, [1, ?, 256]> var_423 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_69)[name = tensor<string, []>("linear_28")]; |
| tensor<fp32, [1, ?, 256]> input_71 = add(x = var_423, y = input_65)[name = tensor<string, []>("input_71")]; |
| tensor<int32, [1]> input_73_axes_0 = const()[name = tensor<string, []>("input_73_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 256]> input_73 = layer_norm(axes = input_73_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_71)[name = tensor<string, []>("input_73")]; |
| tensor<fp32, [1, ?, 768]> var_427 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_73)[name = tensor<string, []>("linear_29")]; |
| tensor<string, []> input_75_mode_0 = const()[name = tensor<string, []>("input_75_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")]; |
| tensor<fp32, [1, ?, 768]> input_75 = gelu(mode = input_75_mode_0, x = var_427)[name = tensor<string, []>("input_75")]; |
| tensor<fp32, [1, ?, 256]> f_9 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_75)[name = tensor<string, []>("linear_30")]; |
| tensor<fp32, [1, ?, 256]> input_77 = add(x = f_9, y = input_73)[name = tensor<string, []>("input_77")]; |
| tensor<int32, [1]> input_79_axes_0 = const()[name = tensor<string, []>("input_79_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 256]> input_79 = layer_norm(axes = input_79_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_77)[name = tensor<string, []>("input_79")]; |
| tensor<fp32, [1, ?, 256]> var_433 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight, x = input_79)[name = tensor<string, []>("linear_31")]; |
| tensor<int32, [4]> concat_20x = const()[name = tensor<string, []>("concat_20x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_435 = reshape(shape = concat_20x, x = var_433)[name = tensor<string, []>("op_435")]; |
| tensor<fp32, [1, ?, 256]> var_437 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight, x = input_79)[name = tensor<string, []>("linear_32")]; |
| tensor<int32, [4]> concat_21x = const()[name = tensor<string, []>("concat_21x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_439 = reshape(shape = concat_21x, x = var_437)[name = tensor<string, []>("op_439")]; |
| tensor<fp32, [1, ?, 256]> var_441 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight, x = input_79)[name = tensor<string, []>("linear_33")]; |
| tensor<int32, [4]> concat_22x = const()[name = tensor<string, []>("concat_22x"), val = tensor<int32, [4]>([1, -1, 4, 64])]; |
| tensor<fp32, [1, ?, 4, 64]> var_443 = reshape(shape = concat_22x, x = var_441)[name = tensor<string, []>("op_443")]; |
| tensor<int32, [4]> v_11_perm_0 = const()[name = tensor<string, []>("v_11_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<bool, []> var_446_transpose_x_0 = const()[name = tensor<string, []>("op_446_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_446_transpose_y_0 = const()[name = tensor<string, []>("op_446_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, 4, 64, ?]> transpose_39 = transpose(perm = transpose_39_perm_0, x = var_439)[name = tensor<string, []>("transpose_62")]; |
| tensor<fp32, [1, 4, ?, 64]> transpose_38 = transpose(perm = transpose_38_perm_0, x = var_435)[name = tensor<string, []>("transpose_63")]; |
| tensor<fp32, [1, 4, ?, ?]> var_446 = matmul(transpose_x = var_446_transpose_x_0, transpose_y = var_446_transpose_y_0, x = transpose_38, y = transpose_39)[name = tensor<string, []>("op_446")]; |
| tensor<fp32, []> _inversed_input_81_y_0 = const()[name = tensor<string, []>("_inversed_input_81_y_0"), val = tensor<fp32, []>(0x1p-3)]; |
| tensor<fp32, [1, 4, ?, ?]> _inversed_input_81 = mul(x = var_446, y = _inversed_input_81_y_0)[name = tensor<string, []>("_inversed_input_81")]; |
| tensor<fp32, [1, 4, ?, ?]> att = softmax(axis = var_260, x = _inversed_input_81)[name = tensor<string, []>("att")]; |
| tensor<bool, []> var_450_transpose_x_0 = const()[name = tensor<string, []>("op_450_transpose_x_0"), val = tensor<bool, []>(false)]; |
| tensor<bool, []> var_450_transpose_y_0 = const()[name = tensor<string, []>("op_450_transpose_y_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 4, ?, 64]> v_11 = transpose(perm = v_11_perm_0, x = var_443)[name = tensor<string, []>("transpose_64")]; |
| tensor<fp32, [1, 4, ?, 64]> var_450 = matmul(transpose_x = var_450_transpose_x_0, transpose_y = var_450_transpose_y_0, x = att, y = v_11)[name = tensor<string, []>("op_450")]; |
| tensor<int32, [4]> var_451_perm_0 = const()[name = tensor<string, []>("op_451_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> concat_23x = const()[name = tensor<string, []>("concat_23x"), val = tensor<int32, [3]>([1, -1, 256])]; |
| tensor<fp32, [1, ?, 4, 64]> var_451 = transpose(perm = var_451_perm_0, x = var_450)[name = tensor<string, []>("transpose_61")]; |
| tensor<fp32, [1, ?, 256]> input_83 = reshape(shape = concat_23x, x = var_451)[name = tensor<string, []>("input_83")]; |
| tensor<fp32, [1, ?, 256]> var_454 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight, x = input_83)[name = tensor<string, []>("linear_34")]; |
| tensor<fp32, [1, ?, 256]> input_85 = add(x = var_454, y = input_79)[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, ?, 256]> input_87 = layer_norm(axes = input_87_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight, x = input_85)[name = tensor<string, []>("input_87")]; |
| tensor<fp32, [1, ?, 768]> var_458 = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight, x = input_87)[name = tensor<string, []>("linear_35")]; |
| tensor<string, []> input_89_mode_0 = const()[name = tensor<string, []>("input_89_mode_0"), val = tensor<string, []>("TANH_APPROXIMATION")]; |
| tensor<fp32, [1, ?, 768]> input_89 = gelu(mode = input_89_mode_0, x = var_458)[name = tensor<string, []>("input_89")]; |
| tensor<fp32, [1, ?, 256]> f = linear(bias = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias, weight = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight, x = input_89)[name = tensor<string, []>("linear_36")]; |
| tensor<fp32, [1, ?, 256]> input_91 = add(x = f, y = input_87)[name = tensor<string, []>("input_91")]; |
| tensor<int32, [1]> input_93_axes_0 = const()[name = tensor<string, []>("input_93_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 256]> input_93 = layer_norm(axes = input_93_axes_0, beta = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias, epsilon = var_258, gamma = ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight, x = input_91)[name = tensor<string, []>("input_93")]; |
| tensor<fp32, [1, ?, 192]> var_466 = linear(bias = ts_bert_encoder_bias, weight = ts_bert_encoder_weight, x = input_93)[name = tensor<string, []>("linear_37")]; |
| tensor<int32, [3]> x_1_perm_0 = const()[name = tensor<string, []>("x_1_perm_0"), val = tensor<int32, [3]>([0, -1, -2])]; |
| tensor<fp32, [1, 32, 1]> var_485 = const()[name = tensor<string, []>("op_485"), val = tensor<fp32, [1, 32, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7161408)))]; |
| tensor<int32, []> var_486 = const()[name = tensor<string, []>("op_486"), val = tensor<int32, []>(-1)]; |
| tensor<int32, []> var_487 = const()[name = tensor<string, []>("op_487"), val = tensor<int32, []>(-1)]; |
| tensor<int32, []> concat_24_axis_0 = const()[name = tensor<string, []>("concat_24_axis_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> concat_24_interleave_0 = const()[name = tensor<string, []>("concat_24_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, [3]> concat_24 = concat(axis = concat_24_axis_0, interleave = concat_24_interleave_0, values = (var_486, var_487, gather_0))[name = tensor<string, []>("concat_24")]; |
| tensor<int32, [3]> shape_1 = const()[name = tensor<string, []>("shape_1"), val = tensor<int32, [3]>([1, 32, 1])]; |
| tensor<int32, []> equal_0_y_0 = const()[name = tensor<string, []>("equal_0_y_0"), val = tensor<int32, []>(-1)]; |
| tensor<bool, [3]> equal_0 = equal(x = concat_24, y = equal_0_y_0)[name = tensor<string, []>("equal_0")]; |
| tensor<int32, [3]> select_0 = select(a = shape_1, b = concat_24, cond = equal_0)[name = tensor<string, []>("select_0")]; |
| tensor<int32, [3]> real_div_0 = real_div(x = select_0, y = shape_1)[name = tensor<string, []>("real_div_0")]; |
| tensor<fp32, [?, ?, ?]> sx = tile(reps = real_div_0, x = var_485)[name = tensor<string, []>("sx")]; |
| tensor<int32, []> var_492 = const()[name = tensor<string, []>("op_492"), val = tensor<int32, []>(1)]; |
| tensor<bool, []> x_3_interleave_0 = const()[name = tensor<string, []>("x_3_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 192, ?]> x_1 = transpose(perm = x_1_perm_0, x = var_466)[name = tensor<string, []>("transpose_60")]; |
| tensor<fp32, [1, ?, ?]> x_3 = concat(axis = var_492, interleave = x_3_interleave_0, values = (x_1, sx))[name = tensor<string, []>("x_3")]; |
| tensor<int32, [3]> transpose_6_perm_0 = const()[name = tensor<string, []>("transpose_6_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])]; |
| tensor<fp32, [384]> add_0 = const()[name = tensor<string, []>("add_0"), val = tensor<fp32, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7161600)))]; |
| tensor<fp32, [384]> add_1 = const()[name = tensor<string, []>("add_1"), val = tensor<fp32, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7163200)))]; |
| tensor<fp32, [384, 224]> concat_29 = const()[name = tensor<string, []>("concat_29"), val = tensor<fp32, [384, 224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7164800)))]; |
| tensor<fp32, [384, 96]> concat_30 = const()[name = tensor<string, []>("concat_30"), val = tensor<fp32, [384, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7508928)))]; |
| tensor<fp32, [384, 224]> concat_31 = const()[name = tensor<string, []>("concat_31"), val = tensor<fp32, [384, 224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7656448)))]; |
| tensor<fp32, [384, 96]> concat_32 = const()[name = tensor<string, []>("concat_32"), val = tensor<fp32, [384, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8000576)))]; |
| tensor<fp32, [1, 192]> var_522_batch_first_lstm_h0_reshaped = const()[name = tensor<string, []>("op_522_batch_first_lstm_h0_reshaped"), val = tensor<fp32, [1, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8148096)))]; |
| tensor<string, []> var_522_batch_first_direction_0 = const()[name = tensor<string, []>("op_522_batch_first_direction_0"), val = tensor<string, []>("bidirectional")]; |
| tensor<bool, []> var_522_batch_first_output_sequence_0 = const()[name = tensor<string, []>("op_522_batch_first_output_sequence_0"), val = tensor<bool, []>(true)]; |
| tensor<string, []> var_522_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("op_522_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")]; |
| tensor<string, []> var_522_batch_first_cell_activation_0 = const()[name = tensor<string, []>("op_522_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<string, []> var_522_batch_first_activation_0 = const()[name = tensor<string, []>("op_522_batch_first_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<fp32, [?, 1, ?]> transpose_6 = transpose(perm = transpose_6_perm_0, x = x_3)[name = tensor<string, []>("transpose_59")]; |
| tensor<fp32, [?, 1, 192]> var_522_batch_first_0, tensor<fp32, [1, 192]> var_522_batch_first_1, tensor<fp32, [1, 192]> var_522_batch_first_2 = lstm(activation = var_522_batch_first_activation_0, bias = add_0, bias_back = add_1, cell_activation = var_522_batch_first_cell_activation_0, direction = var_522_batch_first_direction_0, initial_c = var_522_batch_first_lstm_h0_reshaped, initial_h = var_522_batch_first_lstm_h0_reshaped, output_sequence = var_522_batch_first_output_sequence_0, recurrent_activation = var_522_batch_first_recurrent_activation_0, weight_hh = concat_30, weight_hh_back = concat_32, weight_ih = concat_29, weight_ih_back = concat_31, x = transpose_6)[name = tensor<string, []>("op_522_batch_first")]; |
| tensor<int32, [3]> transpose_13_perm_0 = const()[name = tensor<string, []>("transpose_13_perm_0"), val = tensor<int32, [3]>([1, 0, 2])]; |
| tensor<fp32, []> var_532 = const()[name = tensor<string, []>("op_532"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 1, 192]> beta_3 = const()[name = tensor<string, []>("beta_3"), val = tensor<fp32, [1, 1, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8148928)))]; |
| tensor<int32, [1]> x_11_axes_0 = const()[name = tensor<string, []>("x_11_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 192]> transpose_13 = transpose(perm = transpose_13_perm_0, x = var_522_batch_first_0)[name = tensor<string, []>("transpose_58")]; |
| tensor<fp32, [1, ?, 192]> x_11 = layer_norm(axes = x_11_axes_0, epsilon = var_532, x = transpose_13)[name = tensor<string, []>("x_11")]; |
| tensor<fp32, [1, 1, 192]> var_556 = const()[name = tensor<string, []>("op_556"), val = tensor<fp32, [1, 1, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8149760)))]; |
| tensor<fp32, [1, ?, 192]> var_557 = mul(x = var_556, y = x_11)[name = tensor<string, []>("op_557")]; |
| tensor<fp32, [1, ?, 192]> x_13 = add(x = var_557, y = beta_3)[name = tensor<string, []>("x_13")]; |
| tensor<int32, [3]> var_563_perm_0 = const()[name = tensor<string, []>("op_563_perm_0"), val = tensor<int32, [3]>([0, -1, -2])]; |
| tensor<int32, []> var_565 = const()[name = tensor<string, []>("op_565"), val = tensor<int32, []>(1)]; |
| tensor<bool, []> x_15_interleave_0 = const()[name = tensor<string, []>("x_15_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 192, ?]> var_563 = transpose(perm = var_563_perm_0, x = x_13)[name = tensor<string, []>("transpose_57")]; |
| tensor<fp32, [1, ?, ?]> x_15 = concat(axis = var_565, interleave = x_15_interleave_0, values = (var_563, sx))[name = tensor<string, []>("x_15")]; |
| tensor<int32, [3]> transpose_8_perm_0 = const()[name = tensor<string, []>("transpose_8_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])]; |
| tensor<fp32, [384]> add_2 = const()[name = tensor<string, []>("add_2"), val = tensor<fp32, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8150592)))]; |
| tensor<fp32, [384]> add_3 = const()[name = tensor<string, []>("add_3"), val = tensor<fp32, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8152192)))]; |
| tensor<fp32, [384, 224]> concat_39 = const()[name = tensor<string, []>("concat_39"), val = tensor<fp32, [384, 224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8153792)))]; |
| tensor<fp32, [384, 96]> concat_40 = const()[name = tensor<string, []>("concat_40"), val = tensor<fp32, [384, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8497920)))]; |
| tensor<fp32, [384, 224]> concat_41 = const()[name = tensor<string, []>("concat_41"), val = tensor<fp32, [384, 224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8645440)))]; |
| tensor<fp32, [384, 96]> concat_42 = const()[name = tensor<string, []>("concat_42"), val = tensor<fp32, [384, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8989568)))]; |
| tensor<string, []> var_595_batch_first_direction_0 = const()[name = tensor<string, []>("op_595_batch_first_direction_0"), val = tensor<string, []>("bidirectional")]; |
| tensor<bool, []> var_595_batch_first_output_sequence_0 = const()[name = tensor<string, []>("op_595_batch_first_output_sequence_0"), val = tensor<bool, []>(true)]; |
| tensor<string, []> var_595_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("op_595_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")]; |
| tensor<string, []> var_595_batch_first_cell_activation_0 = const()[name = tensor<string, []>("op_595_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<string, []> var_595_batch_first_activation_0 = const()[name = tensor<string, []>("op_595_batch_first_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<fp32, [?, 1, ?]> transpose_8 = transpose(perm = transpose_8_perm_0, x = x_15)[name = tensor<string, []>("transpose_56")]; |
| tensor<fp32, [?, 1, 192]> var_595_batch_first_0, tensor<fp32, [1, 192]> var_595_batch_first_1, tensor<fp32, [1, 192]> var_595_batch_first_2 = lstm(activation = var_595_batch_first_activation_0, bias = add_2, bias_back = add_3, cell_activation = var_595_batch_first_cell_activation_0, direction = var_595_batch_first_direction_0, initial_c = var_522_batch_first_lstm_h0_reshaped, initial_h = var_522_batch_first_lstm_h0_reshaped, output_sequence = var_595_batch_first_output_sequence_0, recurrent_activation = var_595_batch_first_recurrent_activation_0, weight_hh = concat_40, weight_hh_back = concat_42, weight_ih = concat_39, weight_ih_back = concat_41, x = transpose_8)[name = tensor<string, []>("op_595_batch_first")]; |
| tensor<int32, [3]> transpose_14_perm_0 = const()[name = tensor<string, []>("transpose_14_perm_0"), val = tensor<int32, [3]>([1, 0, 2])]; |
| tensor<fp32, []> var_605 = const()[name = tensor<string, []>("op_605"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 1, 192]> beta_7 = const()[name = tensor<string, []>("beta_7"), val = tensor<fp32, [1, 1, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9137088)))]; |
| tensor<int32, [1]> x_23_axes_0 = const()[name = tensor<string, []>("x_23_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 192]> transpose_14 = transpose(perm = transpose_14_perm_0, x = var_595_batch_first_0)[name = tensor<string, []>("transpose_55")]; |
| tensor<fp32, [1, ?, 192]> x_23 = layer_norm(axes = x_23_axes_0, epsilon = var_605, x = transpose_14)[name = tensor<string, []>("x_23")]; |
| tensor<fp32, [1, 1, 192]> var_629 = const()[name = tensor<string, []>("op_629"), val = tensor<fp32, [1, 1, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9137920)))]; |
| tensor<fp32, [1, ?, 192]> var_630 = mul(x = var_629, y = x_23)[name = tensor<string, []>("op_630")]; |
| tensor<fp32, [1, ?, 192]> x_25 = add(x = var_630, y = beta_7)[name = tensor<string, []>("x_25")]; |
| tensor<int32, [3]> var_636_perm_0 = const()[name = tensor<string, []>("op_636_perm_0"), val = tensor<int32, [3]>([0, -1, -2])]; |
| tensor<int32, []> var_638 = const()[name = tensor<string, []>("op_638"), val = tensor<int32, []>(1)]; |
| tensor<bool, []> x_27_interleave_0 = const()[name = tensor<string, []>("x_27_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 192, ?]> var_636 = transpose(perm = var_636_perm_0, x = x_25)[name = tensor<string, []>("transpose_54")]; |
| tensor<fp32, [1, ?, ?]> x_27 = concat(axis = var_638, interleave = x_27_interleave_0, values = (var_636, sx))[name = tensor<string, []>("x_27")]; |
| tensor<int32, [3]> transpose_10_perm_0 = const()[name = tensor<string, []>("transpose_10_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])]; |
| tensor<fp32, [384]> add_4 = const()[name = tensor<string, []>("add_4"), val = tensor<fp32, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9138752)))]; |
| tensor<fp32, [384]> add_5 = const()[name = tensor<string, []>("add_5"), val = tensor<fp32, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9140352)))]; |
| tensor<fp32, [384, 224]> concat_49 = const()[name = tensor<string, []>("concat_49"), val = tensor<fp32, [384, 224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9141952)))]; |
| tensor<fp32, [384, 96]> concat_50 = const()[name = tensor<string, []>("concat_50"), val = tensor<fp32, [384, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9486080)))]; |
| tensor<fp32, [384, 224]> concat_51 = const()[name = tensor<string, []>("concat_51"), val = tensor<fp32, [384, 224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9633600)))]; |
| tensor<fp32, [384, 96]> concat_52 = const()[name = tensor<string, []>("concat_52"), val = tensor<fp32, [384, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9977728)))]; |
| tensor<string, []> var_668_batch_first_direction_0 = const()[name = tensor<string, []>("op_668_batch_first_direction_0"), val = tensor<string, []>("bidirectional")]; |
| tensor<bool, []> var_668_batch_first_output_sequence_0 = const()[name = tensor<string, []>("op_668_batch_first_output_sequence_0"), val = tensor<bool, []>(true)]; |
| tensor<string, []> var_668_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("op_668_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")]; |
| tensor<string, []> var_668_batch_first_cell_activation_0 = const()[name = tensor<string, []>("op_668_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<string, []> var_668_batch_first_activation_0 = const()[name = tensor<string, []>("op_668_batch_first_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<fp32, [?, 1, ?]> transpose_10 = transpose(perm = transpose_10_perm_0, x = x_27)[name = tensor<string, []>("transpose_53")]; |
| tensor<fp32, [?, 1, 192]> var_668_batch_first_0, tensor<fp32, [1, 192]> var_668_batch_first_1, tensor<fp32, [1, 192]> var_668_batch_first_2 = lstm(activation = var_668_batch_first_activation_0, bias = add_4, bias_back = add_5, cell_activation = var_668_batch_first_cell_activation_0, direction = var_668_batch_first_direction_0, initial_c = var_522_batch_first_lstm_h0_reshaped, initial_h = var_522_batch_first_lstm_h0_reshaped, output_sequence = var_668_batch_first_output_sequence_0, recurrent_activation = var_668_batch_first_recurrent_activation_0, weight_hh = concat_50, weight_hh_back = concat_52, weight_ih = concat_49, weight_ih_back = concat_51, x = transpose_10)[name = tensor<string, []>("op_668_batch_first")]; |
| tensor<int32, [3]> transpose_15_perm_0 = const()[name = tensor<string, []>("transpose_15_perm_0"), val = tensor<int32, [3]>([1, 0, 2])]; |
| tensor<fp32, []> var_678 = const()[name = tensor<string, []>("op_678"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 1, 192]> beta_11 = const()[name = tensor<string, []>("beta_11"), val = tensor<fp32, [1, 1, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10125248)))]; |
| tensor<int32, [1]> x_35_axes_0 = const()[name = tensor<string, []>("x_35_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 192]> transpose_15 = transpose(perm = transpose_15_perm_0, x = var_668_batch_first_0)[name = tensor<string, []>("transpose_52")]; |
| tensor<fp32, [1, ?, 192]> x_35 = layer_norm(axes = x_35_axes_0, epsilon = var_678, x = transpose_15)[name = tensor<string, []>("x_35")]; |
| tensor<fp32, [1, 1, 192]> var_702 = const()[name = tensor<string, []>("op_702"), val = tensor<fp32, [1, 1, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10126080)))]; |
| tensor<fp32, [1, ?, 192]> var_703 = mul(x = var_702, y = x_35)[name = tensor<string, []>("op_703")]; |
| tensor<fp32, [1, ?, 192]> x_37 = add(x = var_703, y = beta_11)[name = tensor<string, []>("x_37")]; |
| tensor<int32, [3]> var_709_perm_0 = const()[name = tensor<string, []>("op_709_perm_0"), val = tensor<int32, [3]>([0, -1, -2])]; |
| tensor<int32, []> var_711 = const()[name = tensor<string, []>("op_711"), val = tensor<int32, []>(1)]; |
| tensor<bool, []> x_39_interleave_0 = const()[name = tensor<string, []>("x_39_interleave_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, 192, ?]> var_709 = transpose(perm = var_709_perm_0, x = x_37)[name = tensor<string, []>("transpose_51")]; |
| tensor<fp32, [1, ?, ?]> d = concat(axis = var_711, interleave = x_39_interleave_0, values = (var_709, sx))[name = tensor<string, []>("x_39")]; |
| tensor<int32, [3]> transpose_12_perm_0 = const()[name = tensor<string, []>("transpose_12_perm_0"), val = tensor<int32, [3]>([-1, 0, -2])]; |
| tensor<fp32, [384]> add_6 = const()[name = tensor<string, []>("add_6"), val = tensor<fp32, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10126912)))]; |
| tensor<fp32, [384]> add_7 = const()[name = tensor<string, []>("add_7"), val = tensor<fp32, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10128512)))]; |
| tensor<fp32, [384, 224]> concat_59 = const()[name = tensor<string, []>("concat_59"), val = tensor<fp32, [384, 224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10130112)))]; |
| tensor<fp32, [384, 96]> concat_60 = const()[name = tensor<string, []>("concat_60"), val = tensor<fp32, [384, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10474240)))]; |
| tensor<fp32, [384, 224]> concat_61 = const()[name = tensor<string, []>("concat_61"), val = tensor<fp32, [384, 224]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10621760)))]; |
| tensor<fp32, [384, 96]> concat_62 = const()[name = tensor<string, []>("concat_62"), val = tensor<fp32, [384, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10965888)))]; |
| tensor<string, []> input_109_batch_first_direction_0 = const()[name = tensor<string, []>("input_109_batch_first_direction_0"), val = tensor<string, []>("bidirectional")]; |
| tensor<bool, []> input_109_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_109_batch_first_output_sequence_0"), val = tensor<bool, []>(true)]; |
| tensor<string, []> input_109_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_109_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")]; |
| tensor<string, []> input_109_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_109_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<string, []> input_109_batch_first_activation_0 = const()[name = tensor<string, []>("input_109_batch_first_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<fp32, [?, 1, ?]> transpose_12 = transpose(perm = transpose_12_perm_0, x = d)[name = tensor<string, []>("transpose_50")]; |
| tensor<fp32, [?, 1, 192]> input_109_batch_first_0, tensor<fp32, [1, 192]> input_109_batch_first_1, tensor<fp32, [1, 192]> input_109_batch_first_2 = lstm(activation = input_109_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_109_batch_first_cell_activation_0, direction = input_109_batch_first_direction_0, initial_c = var_522_batch_first_lstm_h0_reshaped, initial_h = var_522_batch_first_lstm_h0_reshaped, output_sequence = input_109_batch_first_output_sequence_0, recurrent_activation = input_109_batch_first_recurrent_activation_0, weight_hh = concat_60, weight_hh_back = concat_62, weight_ih = concat_59, weight_ih_back = concat_61, x = transpose_12)[name = tensor<string, []>("input_109_batch_first")]; |
| tensor<int32, [3]> input_109_perm_0 = const()[name = tensor<string, []>("input_109_perm_0"), val = tensor<int32, [3]>([1, 0, 2])]; |
| tensor<fp32, [1, ?, 192]> input_109 = transpose(perm = input_109_perm_0, x = input_109_batch_first_0)[name = tensor<string, []>("transpose_49")]; |
| tensor<fp32, [1, ?, 50]> var_747 = linear(bias = ts_predictor_duration_proj_linear_layer_bias, weight = ts_predictor_duration_proj_linear_layer_weight, x = input_109)[name = tensor<string, []>("linear_41")]; |
| tensor<fp32, [1, ?, 50]> var_748 = sigmoid(x = var_747)[name = tensor<string, []>("op_748")]; |
| tensor<int32, [1]> var_753_axes_0 = const()[name = tensor<string, []>("op_753_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<bool, []> var_753_keep_dims_0 = const()[name = tensor<string, []>("op_753_keep_dims_0"), val = tensor<bool, []>(false)]; |
| tensor<fp32, [1, ?]> duration = reduce_sum(axes = var_753_axes_0, keep_dims = var_753_keep_dims_0, x = var_748)[name = tensor<string, []>("op_753")]; |
| tensor<int32, []> var_757_batch_dims_0 = const()[name = tensor<string, []>("op_757_batch_dims_0"), val = tensor<int32, []>(0)]; |
| tensor<bool, []> var_757_validate_indices_0 = const()[name = tensor<string, []>("op_757_validate_indices_0"), val = tensor<bool, []>(false)]; |
| tensor<int32, []> var_757_axis_1 = const()[name = tensor<string, []>("op_757_axis_1"), val = tensor<int32, []>(0)]; |
| tensor<fp32, [1, ?, 192]> var_757 = gather(axis = var_757_axis_1, batch_dims = var_757_batch_dims_0, indices = select_2, validate_indices = var_757_validate_indices_0, x = ts_text_encoder_embedding_weight)[name = tensor<string, []>("op_757")]; |
| tensor<int32, [3]> input_111_perm_0 = const()[name = tensor<string, []>("input_111_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp32, []> var_761 = const()[name = tensor<string, []>("op_761"), val = tensor<fp32, []>(0x1.99999ap-3)]; |
| tensor<fp32, []> var_764 = const()[name = tensor<string, []>("op_764"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<string, []> x_41_pad_type_0 = const()[name = tensor<string, []>("x_41_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_41_pad_0 = const()[name = tensor<string, []>("x_41_pad_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [1]> x_41_strides_0 = const()[name = tensor<string, []>("x_41_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_41_dilations_0 = const()[name = tensor<string, []>("x_41_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> x_41_groups_0 = const()[name = tensor<string, []>("x_41_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 192, ?]> input_111 = transpose(perm = input_111_perm_0, x = var_757)[name = tensor<string, []>("transpose_48")]; |
| tensor<fp32, [1, 192, ?]> x_41 = conv(bias = ts_text_encoder_cnn_0_0_bias, dilations = x_41_dilations_0, groups = x_41_groups_0, pad = x_41_pad_0, pad_type = x_41_pad_type_0, strides = x_41_strides_0, weight = ts_text_encoder_cnn_0_0_weight, x = input_111)[name = tensor<string, []>("x_41")]; |
| tensor<int32, [3]> input_113_perm_0 = const()[name = tensor<string, []>("input_113_perm_0"), val = tensor<int32, [3]>([0, -1, 1])]; |
| tensor<int32, [1]> x_43_axes_0 = const()[name = tensor<string, []>("x_43_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 192]> input_113 = transpose(perm = input_113_perm_0, x = x_41)[name = tensor<string, []>("transpose_47")]; |
| tensor<fp32, [1, ?, 192]> x_43 = layer_norm(axes = x_43_axes_0, beta = ts_text_encoder_cnn_0_1_beta, epsilon = var_764, gamma = ts_text_encoder_cnn_0_1_gamma, x = input_113)[name = tensor<string, []>("x_43")]; |
| tensor<int32, [3]> input_115_perm_0 = const()[name = tensor<string, []>("input_115_perm_0"), val = tensor<int32, [3]>([0, -1, 1])]; |
| tensor<fp32, [1, 192, ?]> input_115 = transpose(perm = input_115_perm_0, x = x_43)[name = tensor<string, []>("transpose_46")]; |
| tensor<fp32, [1, 192, ?]> input_117 = leaky_relu(alpha = var_761, x = input_115)[name = tensor<string, []>("input_117")]; |
| tensor<fp32, []> var_787 = const()[name = tensor<string, []>("op_787"), val = tensor<fp32, []>(0x1.99999ap-3)]; |
| tensor<fp32, []> var_790 = const()[name = tensor<string, []>("op_790"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<string, []> x_45_pad_type_0 = const()[name = tensor<string, []>("x_45_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_45_pad_0 = const()[name = tensor<string, []>("x_45_pad_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [1]> x_45_strides_0 = const()[name = tensor<string, []>("x_45_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_45_dilations_0 = const()[name = tensor<string, []>("x_45_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> x_45_groups_0 = const()[name = tensor<string, []>("x_45_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 192, ?]> x_45 = conv(bias = ts_text_encoder_cnn_1_0_bias, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = ts_text_encoder_cnn_1_0_weight, x = input_117)[name = tensor<string, []>("x_45")]; |
| tensor<int32, [3]> input_121_perm_0 = const()[name = tensor<string, []>("input_121_perm_0"), val = tensor<int32, [3]>([0, -1, 1])]; |
| tensor<int32, [1]> x_47_axes_0 = const()[name = tensor<string, []>("x_47_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 192]> input_121 = transpose(perm = input_121_perm_0, x = x_45)[name = tensor<string, []>("transpose_45")]; |
| tensor<fp32, [1, ?, 192]> x_47 = layer_norm(axes = x_47_axes_0, beta = ts_text_encoder_cnn_1_1_beta, epsilon = var_790, gamma = ts_text_encoder_cnn_1_1_gamma, x = input_121)[name = tensor<string, []>("x_47")]; |
| tensor<int32, [3]> input_123_perm_0 = const()[name = tensor<string, []>("input_123_perm_0"), val = tensor<int32, [3]>([0, -1, 1])]; |
| tensor<fp32, [1, 192, ?]> input_123 = transpose(perm = input_123_perm_0, x = x_47)[name = tensor<string, []>("transpose_44")]; |
| tensor<fp32, [1, 192, ?]> input_125 = leaky_relu(alpha = var_787, x = input_123)[name = tensor<string, []>("input_125")]; |
| tensor<fp32, []> var_813 = const()[name = tensor<string, []>("op_813"), val = tensor<fp32, []>(0x1.99999ap-3)]; |
| tensor<fp32, []> var_816 = const()[name = tensor<string, []>("op_816"), val = tensor<fp32, []>(0x1.4f8b58p-17)]; |
| tensor<string, []> x_49_pad_type_0 = const()[name = tensor<string, []>("x_49_pad_type_0"), val = tensor<string, []>("custom")]; |
| tensor<int32, [2]> x_49_pad_0 = const()[name = tensor<string, []>("x_49_pad_0"), val = tensor<int32, [2]>([2, 2])]; |
| tensor<int32, [1]> x_49_strides_0 = const()[name = tensor<string, []>("x_49_strides_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, [1]> x_49_dilations_0 = const()[name = tensor<string, []>("x_49_dilations_0"), val = tensor<int32, [1]>([1])]; |
| tensor<int32, []> x_49_groups_0 = const()[name = tensor<string, []>("x_49_groups_0"), val = tensor<int32, []>(1)]; |
| tensor<fp32, [1, 192, ?]> x_49 = conv(bias = ts_text_encoder_cnn_2_0_bias, dilations = x_49_dilations_0, groups = x_49_groups_0, pad = x_49_pad_0, pad_type = x_49_pad_type_0, strides = x_49_strides_0, weight = ts_text_encoder_cnn_2_0_weight, x = input_125)[name = tensor<string, []>("x_49")]; |
| tensor<int32, [3]> input_129_perm_0 = const()[name = tensor<string, []>("input_129_perm_0"), val = tensor<int32, [3]>([0, -1, 1])]; |
| tensor<int32, [1]> x_axes_0 = const()[name = tensor<string, []>("x_axes_0"), val = tensor<int32, [1]>([-1])]; |
| tensor<fp32, [1, ?, 192]> input_129 = transpose(perm = input_129_perm_0, x = x_49)[name = tensor<string, []>("transpose_43")]; |
| tensor<fp32, [1, ?, 192]> x = layer_norm(axes = x_axes_0, beta = ts_text_encoder_cnn_2_1_beta, epsilon = var_816, gamma = ts_text_encoder_cnn_2_1_gamma, x = input_129)[name = tensor<string, []>("x")]; |
| tensor<fp32, [1, ?, 192]> input_133 = leaky_relu(alpha = var_813, x = x)[name = tensor<string, []>("input_133")]; |
| tensor<int32, [3]> input_135_batch_first_transpose_perm_0 = const()[name = tensor<string, []>("input_135_batch_first_transpose_perm_0"), val = tensor<int32, [3]>([1, 0, 2])]; |
| tensor<fp32, [384]> add_8 = const()[name = tensor<string, []>("add_8"), val = tensor<fp32, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11113408)))]; |
| tensor<fp32, [384]> add_9 = const()[name = tensor<string, []>("add_9"), val = tensor<fp32, [384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11115008)))]; |
| tensor<fp32, [384, 192]> concat_69 = const()[name = tensor<string, []>("concat_69"), val = tensor<fp32, [384, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11116608)))]; |
| tensor<fp32, [384, 96]> concat_70 = const()[name = tensor<string, []>("concat_70"), val = tensor<fp32, [384, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11411584)))]; |
| tensor<fp32, [384, 192]> concat_71 = const()[name = tensor<string, []>("concat_71"), val = tensor<fp32, [384, 192]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11559104)))]; |
| tensor<fp32, [384, 96]> concat_72 = const()[name = tensor<string, []>("concat_72"), val = tensor<fp32, [384, 96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11854080)))]; |
| tensor<string, []> input_137_batch_first_direction_0 = const()[name = tensor<string, []>("input_137_batch_first_direction_0"), val = tensor<string, []>("bidirectional")]; |
| tensor<bool, []> input_137_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_137_batch_first_output_sequence_0"), val = tensor<bool, []>(true)]; |
| tensor<string, []> input_137_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_137_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")]; |
| tensor<string, []> input_137_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_137_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<string, []> input_137_batch_first_activation_0 = const()[name = tensor<string, []>("input_137_batch_first_activation_0"), val = tensor<string, []>("tanh")]; |
| tensor<fp32, [?, 1, 192]> input_135_batch_first_transpose = transpose(perm = input_135_batch_first_transpose_perm_0, x = input_133)[name = tensor<string, []>("transpose_42")]; |
| tensor<fp32, [?, 1, 192]> input_137_batch_first_0, tensor<fp32, [1, 192]> input_137_batch_first_1, tensor<fp32, [1, 192]> input_137_batch_first_2 = lstm(activation = input_137_batch_first_activation_0, bias = add_8, bias_back = add_9, cell_activation = input_137_batch_first_cell_activation_0, direction = input_137_batch_first_direction_0, initial_c = var_522_batch_first_lstm_h0_reshaped, initial_h = var_522_batch_first_lstm_h0_reshaped, output_sequence = input_137_batch_first_output_sequence_0, recurrent_activation = input_137_batch_first_recurrent_activation_0, weight_hh = concat_70, weight_hh_back = concat_72, weight_ih = concat_69, weight_ih_back = concat_71, x = input_135_batch_first_transpose)[name = tensor<string, []>("input_137_batch_first")]; |
| tensor<int32, [3]> input_137_perm_0 = const()[name = tensor<string, []>("input_137_perm_0"), val = tensor<int32, [3]>([1, 0, 2])]; |
| tensor<fp32, [1, ?, 192]> input_137 = transpose(perm = input_137_perm_0, x = input_137_batch_first_0)[name = tensor<string, []>("transpose_41")]; |
| tensor<fp32, [1, ?, 512]> input_139 = linear(bias = ts_asr_proj_0_bias, weight = ts_asr_proj_0_weight, x = input_137)[name = tensor<string, []>("linear_42")]; |
| tensor<string, []> input_mode_0 = const()[name = tensor<string, []>("input_mode_0"), val = tensor<string, []>("EXACT")]; |
| tensor<fp32, [1, ?, 512]> input = gelu(mode = input_mode_0, x = input_139)[name = tensor<string, []>("input")]; |
| tensor<fp32, [1, ?, 512]> var_879 = linear(bias = ts_asr_proj_2_bias, weight = ts_asr_proj_2_weight, x = input)[name = tensor<string, []>("linear_43")]; |
| tensor<int32, [3]> var_882_perm_0 = const()[name = tensor<string, []>("op_882_perm_0"), val = tensor<int32, [3]>([0, -1, -2])]; |
| tensor<fp32, [1, 512, ?]> asr_tok = transpose(perm = var_882_perm_0, x = var_879)[name = tensor<string, []>("transpose_40")]; |
| } -> (duration, d, asr_tok); |
| } |
