program(1.0) [buildInfo = dict, tensor>({{"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(tensor input_ids) [FlexibleShapeInformation = tuple, dict, tensor>>, tuple, dict, list, ?>>>>((("DefaultShapes", {{"input_ids", [1, 45]}}), ("RangeDims", {{"input_ids", [[1, 1], [3, 512]]}})))] { tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_full_layer_layer_norm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1152)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2240)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_output_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3328)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(789824)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_ffn_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(792960)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1579456)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_LayerNorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1580544)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1581632)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_dense_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1582720)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1844928)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_value_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1846016)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2108224)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_key_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2109312)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2371520)))]; tensor ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight = const()[name = tensor("ts_bert_encoder_albert_layer_groups_0_albert_layers_0_attention_query_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2372608)))]; tensor ts_bert_encoder_embedding_hidden_mapping_in_bias = const()[name = tensor("ts_bert_encoder_embedding_hidden_mapping_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2634816)))]; tensor ts_bert_encoder_embedding_hidden_mapping_in_weight = const()[name = tensor("ts_bert_encoder_embedding_hidden_mapping_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2635904)))]; tensor ts_bert_embeddings_LayerNorm_bias = const()[name = tensor("ts_bert_embeddings_LayerNorm_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2767040)))]; tensor ts_bert_embeddings_LayerNorm_weight = const()[name = tensor("ts_bert_embeddings_LayerNorm_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2767616)))]; tensor ts_bert_embeddings_token_type_embeddings_weight = const()[name = tensor("ts_bert_embeddings_token_type_embeddings_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2768192)))]; tensor ts_bert_embeddings_position_embeddings_weight = const()[name = tensor("ts_bert_embeddings_position_embeddings_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2769280)))]; tensor ts_bert_embeddings_word_embeddings_weight = const()[name = tensor("ts_bert_embeddings_word_embeddings_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3031488)))]; tensor ts_bert_encoder_bias = const()[name = tensor("ts_bert_encoder_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3122688)))]; tensor ts_bert_encoder_weight = const()[name = tensor("ts_bert_encoder_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3123520)))]; tensor ts_predictor_duration_proj_linear_layer_bias = const()[name = tensor("ts_predictor_duration_proj_linear_layer_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3320192)))]; tensor ts_predictor_duration_proj_linear_layer_weight = const()[name = tensor("ts_predictor_duration_proj_linear_layer_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3320512)))]; tensor ts_text_encoder_embedding_weight = const()[name = tensor("ts_text_encoder_embedding_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3358976)))]; tensor ts_text_encoder_cnn_0_0_bias = const()[name = tensor("ts_text_encoder_cnn_0_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3495744)))]; tensor ts_text_encoder_cnn_0_0_weight = const()[name = tensor("ts_text_encoder_cnn_0_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3496576)))]; tensor ts_text_encoder_cnn_0_1_beta = const()[name = tensor("ts_text_encoder_cnn_0_1_beta"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4233920)))]; tensor ts_text_encoder_cnn_0_1_gamma = const()[name = tensor("ts_text_encoder_cnn_0_1_gamma"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4234752)))]; tensor ts_text_encoder_cnn_1_0_bias = const()[name = tensor("ts_text_encoder_cnn_1_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4235584)))]; tensor ts_text_encoder_cnn_1_0_weight = const()[name = tensor("ts_text_encoder_cnn_1_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4236416)))]; tensor ts_text_encoder_cnn_1_1_beta = const()[name = tensor("ts_text_encoder_cnn_1_1_beta"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4973760)))]; tensor ts_text_encoder_cnn_1_1_gamma = const()[name = tensor("ts_text_encoder_cnn_1_1_gamma"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4974592)))]; tensor ts_text_encoder_cnn_2_0_bias = const()[name = tensor("ts_text_encoder_cnn_2_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4975424)))]; tensor ts_text_encoder_cnn_2_0_weight = const()[name = tensor("ts_text_encoder_cnn_2_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4976256)))]; tensor ts_text_encoder_cnn_2_1_beta = const()[name = tensor("ts_text_encoder_cnn_2_1_beta"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5713600)))]; tensor ts_text_encoder_cnn_2_1_gamma = const()[name = tensor("ts_text_encoder_cnn_2_1_gamma"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5714432)))]; tensor ts_asr_proj_0_bias = const()[name = tensor("ts_asr_proj_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5715264)))]; tensor ts_asr_proj_0_weight = const()[name = tensor("ts_asr_proj_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5717376)))]; tensor ts_asr_proj_2_bias = const()[name = tensor("ts_asr_proj_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6110656)))]; tensor ts_asr_proj_2_weight = const()[name = tensor("ts_asr_proj_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6112768)))]; tensor var_258 = const()[name = tensor("op_258"), val = tensor(0x1.197998p-40)]; tensor var_260 = const()[name = tensor("op_260"), val = tensor(-1)]; tensor var_266_shape = shape(x = input_ids)[name = tensor("op_266_shape")]; tensor gather_0_batch_dims_0 = const()[name = tensor("gather_0_batch_dims_0"), val = tensor(0)]; tensor gather_0_validate_indices_0 = const()[name = tensor("gather_0_validate_indices_0"), val = tensor(false)]; tensor select_1 = const()[name = tensor("select_1"), val = tensor(1)]; tensor gather_0_axis_1 = const()[name = tensor("gather_0_axis_1"), val = tensor(0)]; tensor 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("gather_0")]; tensor const_0 = const()[name = tensor("const_0"), val = tensor(0)]; tensor const_1 = const()[name = tensor("const_1"), val = tensor(1)]; tensor var_267 = range_1d(end = gather_0, start = const_0, step = const_1)[name = tensor("op_267")]; tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([0])]; tensor input_1 = expand_dims(axes = input_1_axes_0, x = var_267)[name = tensor("input_1")]; tensor var_269_batch_dims_0 = const()[name = tensor("op_269_batch_dims_0"), val = tensor(0)]; tensor var_269_validate_indices_0 = const()[name = tensor("op_269_validate_indices_0"), val = tensor(false)]; tensor greater_equal_1_y_0 = const()[name = tensor("greater_equal_1_y_0"), val = tensor(0)]; tensor greater_equal_1 = greater_equal(x = input_ids, y = greater_equal_1_y_0)[name = tensor("greater_equal_1")]; tensor slice_by_index_1 = const()[name = tensor("slice_by_index_1"), val = tensor(178)]; tensor add_11 = add(x = input_ids, y = slice_by_index_1)[name = tensor("add_11")]; tensor select_2 = select(a = input_ids, b = add_11, cond = greater_equal_1)[name = tensor("select_2")]; tensor var_269_axis_1 = const()[name = tensor("op_269_axis_1"), val = tensor(0)]; tensor 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("op_269")]; tensor var_270_batch_dims_0 = const()[name = tensor("op_270_batch_dims_0"), val = tensor(0)]; tensor var_270_validate_indices_0 = const()[name = tensor("op_270_validate_indices_0"), val = tensor(false)]; tensor greater_equal_2_y_0 = const()[name = tensor("greater_equal_2_y_0"), val = tensor(0)]; tensor greater_equal_2 = greater_equal(x = input_1, y = greater_equal_2_y_0)[name = tensor("greater_equal_2")]; tensor slice_by_index_2 = const()[name = tensor("slice_by_index_2"), val = tensor(512)]; tensor add_12 = add(x = input_1, y = slice_by_index_2)[name = tensor("add_12")]; tensor select_3 = select(a = input_1, b = add_12, cond = greater_equal_2)[name = tensor("select_3")]; tensor var_270_axis_1 = const()[name = tensor("op_270_axis_1"), val = tensor(0)]; tensor 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("op_270")]; tensor var_271 = add(x = var_269, y = var_270)[name = tensor("op_271")]; tensor input_3 = sub(x = input_ids, y = input_ids)[name = tensor("sub_0")]; tensor var_273_batch_dims_0 = const()[name = tensor("op_273_batch_dims_0"), val = tensor(0)]; tensor var_273_validate_indices_0 = const()[name = tensor("op_273_validate_indices_0"), val = tensor(false)]; tensor greater_equal_3_y_0 = const()[name = tensor("greater_equal_3_y_0"), val = tensor(0)]; tensor greater_equal_3 = greater_equal(x = input_3, y = greater_equal_3_y_0)[name = tensor("greater_equal_3")]; tensor slice_by_index_3 = const()[name = tensor("slice_by_index_3"), val = tensor(2)]; tensor add_13 = add(x = input_3, y = slice_by_index_3)[name = tensor("add_13")]; tensor select_4 = select(a = input_3, b = add_13, cond = greater_equal_3)[name = tensor("select_4")]; tensor var_273_axis_1 = const()[name = tensor("op_273_axis_1"), val = tensor(0)]; tensor 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("op_273")]; tensor input_5 = add(x = var_271, y = var_273)[name = tensor("input_5")]; tensor input_7_axes_0 = const()[name = tensor("input_7_axes_0"), val = tensor([-1])]; tensor 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("input_7")]; tensor 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("linear_0")]; tensor 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("linear_1")]; tensor concat_0x = const()[name = tensor("concat_0x"), val = tensor([1, -1, 4, 64])]; tensor var_280 = reshape(shape = concat_0x, x = var_278)[name = tensor("op_280")]; tensor 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("linear_2")]; tensor concat_1x = const()[name = tensor("concat_1x"), val = tensor([1, -1, 4, 64])]; tensor var_284 = reshape(shape = concat_1x, x = var_282)[name = tensor("op_284")]; tensor 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("linear_3")]; tensor concat_2x = const()[name = tensor("concat_2x"), val = tensor([1, -1, 4, 64])]; tensor var_288 = reshape(shape = concat_2x, x = var_286)[name = tensor("op_288")]; tensor v_1_perm_0 = const()[name = tensor("v_1_perm_0"), val = tensor([0, 2, 1, 3])]; tensor var_291_transpose_x_0 = const()[name = tensor("op_291_transpose_x_0"), val = tensor(false)]; tensor var_291_transpose_y_0 = const()[name = tensor("op_291_transpose_y_0"), val = tensor(false)]; tensor transpose_28_perm_0 = const()[name = tensor("transpose_28_perm_0"), val = tensor([0, 2, -3, -1])]; tensor transpose_29_perm_0 = const()[name = tensor("transpose_29_perm_0"), val = tensor([0, 2, -1, -3])]; tensor transpose_29 = transpose(perm = transpose_29_perm_0, x = var_284)[name = tensor("transpose_82")]; tensor transpose_28 = transpose(perm = transpose_28_perm_0, x = var_280)[name = tensor("transpose_83")]; tensor 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("op_291")]; tensor _inversed_input_11_y_0 = const()[name = tensor("_inversed_input_11_y_0"), val = tensor(0x1p-3)]; tensor _inversed_input_11 = mul(x = var_291, y = _inversed_input_11_y_0)[name = tensor("_inversed_input_11")]; tensor att_1 = softmax(axis = var_260, x = _inversed_input_11)[name = tensor("att_1")]; tensor var_295_transpose_x_0 = const()[name = tensor("op_295_transpose_x_0"), val = tensor(false)]; tensor var_295_transpose_y_0 = const()[name = tensor("op_295_transpose_y_0"), val = tensor(false)]; tensor v_1 = transpose(perm = v_1_perm_0, x = var_288)[name = tensor("transpose_84")]; tensor 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("op_295")]; tensor var_296_perm_0 = const()[name = tensor("op_296_perm_0"), val = tensor([0, 2, 1, 3])]; tensor concat_3x = const()[name = tensor("concat_3x"), val = tensor([1, -1, 256])]; tensor var_296 = transpose(perm = var_296_perm_0, x = var_295)[name = tensor("transpose_81")]; tensor input_13 = reshape(shape = concat_3x, x = var_296)[name = tensor("input_13")]; tensor 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("linear_4")]; tensor input_15 = add(x = var_299, y = input_9)[name = tensor("input_15")]; tensor input_17_axes_0 = const()[name = tensor("input_17_axes_0"), val = tensor([-1])]; tensor 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("input_17")]; tensor 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("linear_5")]; tensor input_19_mode_0 = const()[name = tensor("input_19_mode_0"), val = tensor("TANH_APPROXIMATION")]; tensor input_19 = gelu(mode = input_19_mode_0, x = var_303)[name = tensor("input_19")]; tensor 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("linear_6")]; tensor input_21 = add(x = f_1, y = input_17)[name = tensor("input_21")]; tensor input_23_axes_0 = const()[name = tensor("input_23_axes_0"), val = tensor([-1])]; tensor 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("input_23")]; tensor 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("linear_7")]; tensor concat_4x = const()[name = tensor("concat_4x"), val = tensor([1, -1, 4, 64])]; tensor var_311 = reshape(shape = concat_4x, x = var_309)[name = tensor("op_311")]; tensor 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("linear_8")]; tensor concat_5x = const()[name = tensor("concat_5x"), val = tensor([1, -1, 4, 64])]; tensor var_315 = reshape(shape = concat_5x, x = var_313)[name = tensor("op_315")]; tensor 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("linear_9")]; tensor concat_6x = const()[name = tensor("concat_6x"), val = tensor([1, -1, 4, 64])]; tensor var_319 = reshape(shape = concat_6x, x = var_317)[name = tensor("op_319")]; tensor v_3_perm_0 = const()[name = tensor("v_3_perm_0"), val = tensor([0, 2, 1, 3])]; tensor var_322_transpose_x_0 = const()[name = tensor("op_322_transpose_x_0"), val = tensor(false)]; tensor var_322_transpose_y_0 = const()[name = tensor("op_322_transpose_y_0"), val = tensor(false)]; tensor transpose_30_perm_0 = const()[name = tensor("transpose_30_perm_0"), val = tensor([0, 2, -3, -1])]; tensor transpose_31_perm_0 = const()[name = tensor("transpose_31_perm_0"), val = tensor([0, 2, -1, -3])]; tensor transpose_31 = transpose(perm = transpose_31_perm_0, x = var_315)[name = tensor("transpose_78")]; tensor transpose_30 = transpose(perm = transpose_30_perm_0, x = var_311)[name = tensor("transpose_79")]; tensor 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("op_322")]; tensor _inversed_input_25_y_0 = const()[name = tensor("_inversed_input_25_y_0"), val = tensor(0x1p-3)]; tensor _inversed_input_25 = mul(x = var_322, y = _inversed_input_25_y_0)[name = tensor("_inversed_input_25")]; tensor att_3 = softmax(axis = var_260, x = _inversed_input_25)[name = tensor("att_3")]; tensor var_326_transpose_x_0 = const()[name = tensor("op_326_transpose_x_0"), val = tensor(false)]; tensor var_326_transpose_y_0 = const()[name = tensor("op_326_transpose_y_0"), val = tensor(false)]; tensor v_3 = transpose(perm = v_3_perm_0, x = var_319)[name = tensor("transpose_80")]; tensor 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("op_326")]; tensor var_327_perm_0 = const()[name = tensor("op_327_perm_0"), val = tensor([0, 2, 1, 3])]; tensor concat_7x = const()[name = tensor("concat_7x"), val = tensor([1, -1, 256])]; tensor var_327 = transpose(perm = var_327_perm_0, x = var_326)[name = tensor("transpose_77")]; tensor input_27 = reshape(shape = concat_7x, x = var_327)[name = tensor("input_27")]; tensor 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("linear_10")]; tensor input_29 = add(x = var_330, y = input_23)[name = tensor("input_29")]; tensor input_31_axes_0 = const()[name = tensor("input_31_axes_0"), val = tensor([-1])]; tensor 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("input_31")]; tensor 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("linear_11")]; tensor input_33_mode_0 = const()[name = tensor("input_33_mode_0"), val = tensor("TANH_APPROXIMATION")]; tensor input_33 = gelu(mode = input_33_mode_0, x = var_334)[name = tensor("input_33")]; tensor 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("linear_12")]; tensor input_35 = add(x = f_3, y = input_31)[name = tensor("input_35")]; tensor input_37_axes_0 = const()[name = tensor("input_37_axes_0"), val = tensor([-1])]; tensor 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("input_37")]; tensor 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("linear_13")]; tensor concat_8x = const()[name = tensor("concat_8x"), val = tensor([1, -1, 4, 64])]; tensor var_342 = reshape(shape = concat_8x, x = var_340)[name = tensor("op_342")]; tensor 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("linear_14")]; tensor concat_9x = const()[name = tensor("concat_9x"), val = tensor([1, -1, 4, 64])]; tensor var_346 = reshape(shape = concat_9x, x = var_344)[name = tensor("op_346")]; tensor 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("linear_15")]; tensor concat_10x = const()[name = tensor("concat_10x"), val = tensor([1, -1, 4, 64])]; tensor var_350 = reshape(shape = concat_10x, x = var_348)[name = tensor("op_350")]; tensor v_5_perm_0 = const()[name = tensor("v_5_perm_0"), val = tensor([0, 2, 1, 3])]; tensor var_353_transpose_x_0 = const()[name = tensor("op_353_transpose_x_0"), val = tensor(false)]; tensor var_353_transpose_y_0 = const()[name = tensor("op_353_transpose_y_0"), val = tensor(false)]; tensor transpose_32_perm_0 = const()[name = tensor("transpose_32_perm_0"), val = tensor([0, 2, -3, -1])]; tensor transpose_33_perm_0 = const()[name = tensor("transpose_33_perm_0"), val = tensor([0, 2, -1, -3])]; tensor transpose_33 = transpose(perm = transpose_33_perm_0, x = var_346)[name = tensor("transpose_74")]; tensor transpose_32 = transpose(perm = transpose_32_perm_0, x = var_342)[name = tensor("transpose_75")]; tensor 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("op_353")]; tensor _inversed_input_39_y_0 = const()[name = tensor("_inversed_input_39_y_0"), val = tensor(0x1p-3)]; tensor _inversed_input_39 = mul(x = var_353, y = _inversed_input_39_y_0)[name = tensor("_inversed_input_39")]; tensor att_5 = softmax(axis = var_260, x = _inversed_input_39)[name = tensor("att_5")]; tensor var_357_transpose_x_0 = const()[name = tensor("op_357_transpose_x_0"), val = tensor(false)]; tensor var_357_transpose_y_0 = const()[name = tensor("op_357_transpose_y_0"), val = tensor(false)]; tensor v_5 = transpose(perm = v_5_perm_0, x = var_350)[name = tensor("transpose_76")]; tensor 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("op_357")]; tensor var_358_perm_0 = const()[name = tensor("op_358_perm_0"), val = tensor([0, 2, 1, 3])]; tensor concat_11x = const()[name = tensor("concat_11x"), val = tensor([1, -1, 256])]; tensor var_358 = transpose(perm = var_358_perm_0, x = var_357)[name = tensor("transpose_73")]; tensor input_41 = reshape(shape = concat_11x, x = var_358)[name = tensor("input_41")]; tensor 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("linear_16")]; tensor input_43 = add(x = var_361, y = input_37)[name = tensor("input_43")]; tensor input_45_axes_0 = const()[name = tensor("input_45_axes_0"), val = tensor([-1])]; tensor 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("input_45")]; tensor 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("linear_17")]; tensor input_47_mode_0 = const()[name = tensor("input_47_mode_0"), val = tensor("TANH_APPROXIMATION")]; tensor input_47 = gelu(mode = input_47_mode_0, x = var_365)[name = tensor("input_47")]; tensor 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("linear_18")]; tensor input_49 = add(x = f_5, y = input_45)[name = tensor("input_49")]; tensor input_51_axes_0 = const()[name = tensor("input_51_axes_0"), val = tensor([-1])]; tensor 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("input_51")]; tensor 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("linear_19")]; tensor concat_12x = const()[name = tensor("concat_12x"), val = tensor([1, -1, 4, 64])]; tensor var_373 = reshape(shape = concat_12x, x = var_371)[name = tensor("op_373")]; tensor 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("linear_20")]; tensor concat_13x = const()[name = tensor("concat_13x"), val = tensor([1, -1, 4, 64])]; tensor var_377 = reshape(shape = concat_13x, x = var_375)[name = tensor("op_377")]; tensor 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("linear_21")]; tensor concat_14x = const()[name = tensor("concat_14x"), val = tensor([1, -1, 4, 64])]; tensor var_381 = reshape(shape = concat_14x, x = var_379)[name = tensor("op_381")]; tensor v_7_perm_0 = const()[name = tensor("v_7_perm_0"), val = tensor([0, 2, 1, 3])]; tensor var_384_transpose_x_0 = const()[name = tensor("op_384_transpose_x_0"), val = tensor(false)]; tensor var_384_transpose_y_0 = const()[name = tensor("op_384_transpose_y_0"), val = tensor(false)]; tensor transpose_34_perm_0 = const()[name = tensor("transpose_34_perm_0"), val = tensor([0, 2, -3, -1])]; tensor transpose_35_perm_0 = const()[name = tensor("transpose_35_perm_0"), val = tensor([0, 2, -1, -3])]; tensor transpose_35 = transpose(perm = transpose_35_perm_0, x = var_377)[name = tensor("transpose_70")]; tensor transpose_34 = transpose(perm = transpose_34_perm_0, x = var_373)[name = tensor("transpose_71")]; tensor 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("op_384")]; tensor _inversed_input_53_y_0 = const()[name = tensor("_inversed_input_53_y_0"), val = tensor(0x1p-3)]; tensor _inversed_input_53 = mul(x = var_384, y = _inversed_input_53_y_0)[name = tensor("_inversed_input_53")]; tensor att_7 = softmax(axis = var_260, x = _inversed_input_53)[name = tensor("att_7")]; tensor var_388_transpose_x_0 = const()[name = tensor("op_388_transpose_x_0"), val = tensor(false)]; tensor var_388_transpose_y_0 = const()[name = tensor("op_388_transpose_y_0"), val = tensor(false)]; tensor v_7 = transpose(perm = v_7_perm_0, x = var_381)[name = tensor("transpose_72")]; tensor 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("op_388")]; tensor var_389_perm_0 = const()[name = tensor("op_389_perm_0"), val = tensor([0, 2, 1, 3])]; tensor concat_15x = const()[name = tensor("concat_15x"), val = tensor([1, -1, 256])]; tensor var_389 = transpose(perm = var_389_perm_0, x = var_388)[name = tensor("transpose_69")]; tensor input_55 = reshape(shape = concat_15x, x = var_389)[name = tensor("input_55")]; tensor 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("linear_22")]; tensor input_57 = add(x = var_392, y = input_51)[name = tensor("input_57")]; tensor input_59_axes_0 = const()[name = tensor("input_59_axes_0"), val = tensor([-1])]; tensor 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("input_59")]; tensor 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("linear_23")]; tensor input_61_mode_0 = const()[name = tensor("input_61_mode_0"), val = tensor("TANH_APPROXIMATION")]; tensor input_61 = gelu(mode = input_61_mode_0, x = var_396)[name = tensor("input_61")]; tensor 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("linear_24")]; tensor input_63 = add(x = f_7, y = input_59)[name = tensor("input_63")]; tensor input_65_axes_0 = const()[name = tensor("input_65_axes_0"), val = tensor([-1])]; tensor 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("input_65")]; tensor 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("linear_25")]; tensor concat_16x = const()[name = tensor("concat_16x"), val = tensor([1, -1, 4, 64])]; tensor var_404 = reshape(shape = concat_16x, x = var_402)[name = tensor("op_404")]; tensor 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("linear_26")]; tensor concat_17x = const()[name = tensor("concat_17x"), val = tensor([1, -1, 4, 64])]; tensor var_408 = reshape(shape = concat_17x, x = var_406)[name = tensor("op_408")]; tensor 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("linear_27")]; tensor concat_18x = const()[name = tensor("concat_18x"), val = tensor([1, -1, 4, 64])]; tensor var_412 = reshape(shape = concat_18x, x = var_410)[name = tensor("op_412")]; tensor v_9_perm_0 = const()[name = tensor("v_9_perm_0"), val = tensor([0, 2, 1, 3])]; tensor var_415_transpose_x_0 = const()[name = tensor("op_415_transpose_x_0"), val = tensor(false)]; tensor var_415_transpose_y_0 = const()[name = tensor("op_415_transpose_y_0"), val = tensor(false)]; tensor transpose_36_perm_0 = const()[name = tensor("transpose_36_perm_0"), val = tensor([0, 2, -3, -1])]; tensor transpose_37_perm_0 = const()[name = tensor("transpose_37_perm_0"), val = tensor([0, 2, -1, -3])]; tensor transpose_37 = transpose(perm = transpose_37_perm_0, x = var_408)[name = tensor("transpose_66")]; tensor transpose_36 = transpose(perm = transpose_36_perm_0, x = var_404)[name = tensor("transpose_67")]; tensor 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("op_415")]; tensor _inversed_input_67_y_0 = const()[name = tensor("_inversed_input_67_y_0"), val = tensor(0x1p-3)]; tensor _inversed_input_67 = mul(x = var_415, y = _inversed_input_67_y_0)[name = tensor("_inversed_input_67")]; tensor att_9 = softmax(axis = var_260, x = _inversed_input_67)[name = tensor("att_9")]; tensor var_419_transpose_x_0 = const()[name = tensor("op_419_transpose_x_0"), val = tensor(false)]; tensor var_419_transpose_y_0 = const()[name = tensor("op_419_transpose_y_0"), val = tensor(false)]; tensor v_9 = transpose(perm = v_9_perm_0, x = var_412)[name = tensor("transpose_68")]; tensor 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("op_419")]; tensor var_420_perm_0 = const()[name = tensor("op_420_perm_0"), val = tensor([0, 2, 1, 3])]; tensor concat_19x = const()[name = tensor("concat_19x"), val = tensor([1, -1, 256])]; tensor var_420 = transpose(perm = var_420_perm_0, x = var_419)[name = tensor("transpose_65")]; tensor input_69 = reshape(shape = concat_19x, x = var_420)[name = tensor("input_69")]; tensor 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("linear_28")]; tensor input_71 = add(x = var_423, y = input_65)[name = tensor("input_71")]; tensor input_73_axes_0 = const()[name = tensor("input_73_axes_0"), val = tensor([-1])]; tensor 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("input_73")]; tensor 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("linear_29")]; tensor input_75_mode_0 = const()[name = tensor("input_75_mode_0"), val = tensor("TANH_APPROXIMATION")]; tensor input_75 = gelu(mode = input_75_mode_0, x = var_427)[name = tensor("input_75")]; tensor 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("linear_30")]; tensor input_77 = add(x = f_9, y = input_73)[name = tensor("input_77")]; tensor input_79_axes_0 = const()[name = tensor("input_79_axes_0"), val = tensor([-1])]; tensor 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("input_79")]; tensor 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("linear_31")]; tensor concat_20x = const()[name = tensor("concat_20x"), val = tensor([1, -1, 4, 64])]; tensor var_435 = reshape(shape = concat_20x, x = var_433)[name = tensor("op_435")]; tensor 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("linear_32")]; tensor concat_21x = const()[name = tensor("concat_21x"), val = tensor([1, -1, 4, 64])]; tensor var_439 = reshape(shape = concat_21x, x = var_437)[name = tensor("op_439")]; tensor 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("linear_33")]; tensor concat_22x = const()[name = tensor("concat_22x"), val = tensor([1, -1, 4, 64])]; tensor var_443 = reshape(shape = concat_22x, x = var_441)[name = tensor("op_443")]; tensor v_11_perm_0 = const()[name = tensor("v_11_perm_0"), val = tensor([0, 2, 1, 3])]; tensor var_446_transpose_x_0 = const()[name = tensor("op_446_transpose_x_0"), val = tensor(false)]; tensor var_446_transpose_y_0 = const()[name = tensor("op_446_transpose_y_0"), val = tensor(false)]; tensor transpose_38_perm_0 = const()[name = tensor("transpose_38_perm_0"), val = tensor([0, 2, -3, -1])]; tensor transpose_39_perm_0 = const()[name = tensor("transpose_39_perm_0"), val = tensor([0, 2, -1, -3])]; tensor transpose_39 = transpose(perm = transpose_39_perm_0, x = var_439)[name = tensor("transpose_62")]; tensor transpose_38 = transpose(perm = transpose_38_perm_0, x = var_435)[name = tensor("transpose_63")]; tensor 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("op_446")]; tensor _inversed_input_81_y_0 = const()[name = tensor("_inversed_input_81_y_0"), val = tensor(0x1p-3)]; tensor _inversed_input_81 = mul(x = var_446, y = _inversed_input_81_y_0)[name = tensor("_inversed_input_81")]; tensor att = softmax(axis = var_260, x = _inversed_input_81)[name = tensor("att")]; tensor var_450_transpose_x_0 = const()[name = tensor("op_450_transpose_x_0"), val = tensor(false)]; tensor var_450_transpose_y_0 = const()[name = tensor("op_450_transpose_y_0"), val = tensor(false)]; tensor v_11 = transpose(perm = v_11_perm_0, x = var_443)[name = tensor("transpose_64")]; tensor var_450 = matmul(transpose_x = var_450_transpose_x_0, transpose_y = var_450_transpose_y_0, x = att, y = v_11)[name = tensor("op_450")]; tensor var_451_perm_0 = const()[name = tensor("op_451_perm_0"), val = tensor([0, 2, 1, 3])]; tensor concat_23x = const()[name = tensor("concat_23x"), val = tensor([1, -1, 256])]; tensor var_451 = transpose(perm = var_451_perm_0, x = var_450)[name = tensor("transpose_61")]; tensor input_83 = reshape(shape = concat_23x, x = var_451)[name = tensor("input_83")]; tensor 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("linear_34")]; tensor input_85 = add(x = var_454, y = input_79)[name = tensor("input_85")]; tensor input_87_axes_0 = const()[name = tensor("input_87_axes_0"), val = tensor([-1])]; tensor 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("input_87")]; tensor 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("linear_35")]; tensor input_89_mode_0 = const()[name = tensor("input_89_mode_0"), val = tensor("TANH_APPROXIMATION")]; tensor input_89 = gelu(mode = input_89_mode_0, x = var_458)[name = tensor("input_89")]; tensor 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("linear_36")]; tensor input_91 = add(x = f, y = input_87)[name = tensor("input_91")]; tensor input_93_axes_0 = const()[name = tensor("input_93_axes_0"), val = tensor([-1])]; tensor 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("input_93")]; tensor var_466 = linear(bias = ts_bert_encoder_bias, weight = ts_bert_encoder_weight, x = input_93)[name = tensor("linear_37")]; tensor x_1_perm_0 = const()[name = tensor("x_1_perm_0"), val = tensor([0, -1, -2])]; tensor var_485 = const()[name = tensor("op_485"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7161408)))]; tensor var_486 = const()[name = tensor("op_486"), val = tensor(-1)]; tensor var_487 = const()[name = tensor("op_487"), val = tensor(-1)]; tensor concat_24_axis_0 = const()[name = tensor("concat_24_axis_0"), val = tensor(0)]; tensor concat_24_interleave_0 = const()[name = tensor("concat_24_interleave_0"), val = tensor(false)]; tensor concat_24 = concat(axis = concat_24_axis_0, interleave = concat_24_interleave_0, values = (var_486, var_487, gather_0))[name = tensor("concat_24")]; tensor shape_1 = const()[name = tensor("shape_1"), val = tensor([1, 32, 1])]; tensor equal_0_y_0 = const()[name = tensor("equal_0_y_0"), val = tensor(-1)]; tensor equal_0 = equal(x = concat_24, y = equal_0_y_0)[name = tensor("equal_0")]; tensor select_0 = select(a = shape_1, b = concat_24, cond = equal_0)[name = tensor("select_0")]; tensor real_div_0 = real_div(x = select_0, y = shape_1)[name = tensor("real_div_0")]; tensor sx = tile(reps = real_div_0, x = var_485)[name = tensor("sx")]; tensor var_492 = const()[name = tensor("op_492"), val = tensor(1)]; tensor x_3_interleave_0 = const()[name = tensor("x_3_interleave_0"), val = tensor(false)]; tensor x_1 = transpose(perm = x_1_perm_0, x = var_466)[name = tensor("transpose_60")]; tensor x_3 = concat(axis = var_492, interleave = x_3_interleave_0, values = (x_1, sx))[name = tensor("x_3")]; tensor transpose_6_perm_0 = const()[name = tensor("transpose_6_perm_0"), val = tensor([-1, 0, -2])]; tensor add_0 = const()[name = tensor("add_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7161600)))]; tensor add_1 = const()[name = tensor("add_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7163200)))]; tensor concat_29 = const()[name = tensor("concat_29"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7164800)))]; tensor concat_30 = const()[name = tensor("concat_30"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7508928)))]; tensor concat_31 = const()[name = tensor("concat_31"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7656448)))]; tensor concat_32 = const()[name = tensor("concat_32"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8000576)))]; tensor var_522_batch_first_lstm_h0_reshaped = const()[name = tensor("op_522_batch_first_lstm_h0_reshaped"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8148096)))]; tensor var_522_batch_first_direction_0 = const()[name = tensor("op_522_batch_first_direction_0"), val = tensor("bidirectional")]; tensor var_522_batch_first_output_sequence_0 = const()[name = tensor("op_522_batch_first_output_sequence_0"), val = tensor(true)]; tensor var_522_batch_first_recurrent_activation_0 = const()[name = tensor("op_522_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; tensor var_522_batch_first_cell_activation_0 = const()[name = tensor("op_522_batch_first_cell_activation_0"), val = tensor("tanh")]; tensor var_522_batch_first_activation_0 = const()[name = tensor("op_522_batch_first_activation_0"), val = tensor("tanh")]; tensor transpose_6 = transpose(perm = transpose_6_perm_0, x = x_3)[name = tensor("transpose_59")]; tensor var_522_batch_first_0, tensor var_522_batch_first_1, tensor 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("op_522_batch_first")]; tensor transpose_13_perm_0 = const()[name = tensor("transpose_13_perm_0"), val = tensor([1, 0, 2])]; tensor var_532 = const()[name = tensor("op_532"), val = tensor(0x1.4f8b58p-17)]; tensor beta_3 = const()[name = tensor("beta_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8148928)))]; tensor x_11_axes_0 = const()[name = tensor("x_11_axes_0"), val = tensor([-1])]; tensor transpose_13 = transpose(perm = transpose_13_perm_0, x = var_522_batch_first_0)[name = tensor("transpose_58")]; tensor x_11 = layer_norm(axes = x_11_axes_0, epsilon = var_532, x = transpose_13)[name = tensor("x_11")]; tensor var_556 = const()[name = tensor("op_556"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8149760)))]; tensor var_557 = mul(x = var_556, y = x_11)[name = tensor("op_557")]; tensor x_13 = add(x = var_557, y = beta_3)[name = tensor("x_13")]; tensor var_563_perm_0 = const()[name = tensor("op_563_perm_0"), val = tensor([0, -1, -2])]; tensor var_565 = const()[name = tensor("op_565"), val = tensor(1)]; tensor x_15_interleave_0 = const()[name = tensor("x_15_interleave_0"), val = tensor(false)]; tensor var_563 = transpose(perm = var_563_perm_0, x = x_13)[name = tensor("transpose_57")]; tensor x_15 = concat(axis = var_565, interleave = x_15_interleave_0, values = (var_563, sx))[name = tensor("x_15")]; tensor transpose_8_perm_0 = const()[name = tensor("transpose_8_perm_0"), val = tensor([-1, 0, -2])]; tensor add_2 = const()[name = tensor("add_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8150592)))]; tensor add_3 = const()[name = tensor("add_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8152192)))]; tensor concat_39 = const()[name = tensor("concat_39"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8153792)))]; tensor concat_40 = const()[name = tensor("concat_40"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8497920)))]; tensor concat_41 = const()[name = tensor("concat_41"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8645440)))]; tensor concat_42 = const()[name = tensor("concat_42"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8989568)))]; tensor var_595_batch_first_direction_0 = const()[name = tensor("op_595_batch_first_direction_0"), val = tensor("bidirectional")]; tensor var_595_batch_first_output_sequence_0 = const()[name = tensor("op_595_batch_first_output_sequence_0"), val = tensor(true)]; tensor var_595_batch_first_recurrent_activation_0 = const()[name = tensor("op_595_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; tensor var_595_batch_first_cell_activation_0 = const()[name = tensor("op_595_batch_first_cell_activation_0"), val = tensor("tanh")]; tensor var_595_batch_first_activation_0 = const()[name = tensor("op_595_batch_first_activation_0"), val = tensor("tanh")]; tensor transpose_8 = transpose(perm = transpose_8_perm_0, x = x_15)[name = tensor("transpose_56")]; tensor var_595_batch_first_0, tensor var_595_batch_first_1, tensor 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("op_595_batch_first")]; tensor transpose_14_perm_0 = const()[name = tensor("transpose_14_perm_0"), val = tensor([1, 0, 2])]; tensor var_605 = const()[name = tensor("op_605"), val = tensor(0x1.4f8b58p-17)]; tensor beta_7 = const()[name = tensor("beta_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9137088)))]; tensor x_23_axes_0 = const()[name = tensor("x_23_axes_0"), val = tensor([-1])]; tensor transpose_14 = transpose(perm = transpose_14_perm_0, x = var_595_batch_first_0)[name = tensor("transpose_55")]; tensor x_23 = layer_norm(axes = x_23_axes_0, epsilon = var_605, x = transpose_14)[name = tensor("x_23")]; tensor var_629 = const()[name = tensor("op_629"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9137920)))]; tensor var_630 = mul(x = var_629, y = x_23)[name = tensor("op_630")]; tensor x_25 = add(x = var_630, y = beta_7)[name = tensor("x_25")]; tensor var_636_perm_0 = const()[name = tensor("op_636_perm_0"), val = tensor([0, -1, -2])]; tensor var_638 = const()[name = tensor("op_638"), val = tensor(1)]; tensor x_27_interleave_0 = const()[name = tensor("x_27_interleave_0"), val = tensor(false)]; tensor var_636 = transpose(perm = var_636_perm_0, x = x_25)[name = tensor("transpose_54")]; tensor x_27 = concat(axis = var_638, interleave = x_27_interleave_0, values = (var_636, sx))[name = tensor("x_27")]; tensor transpose_10_perm_0 = const()[name = tensor("transpose_10_perm_0"), val = tensor([-1, 0, -2])]; tensor add_4 = const()[name = tensor("add_4"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9138752)))]; tensor add_5 = const()[name = tensor("add_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9140352)))]; tensor concat_49 = const()[name = tensor("concat_49"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9141952)))]; tensor concat_50 = const()[name = tensor("concat_50"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9486080)))]; tensor concat_51 = const()[name = tensor("concat_51"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9633600)))]; tensor concat_52 = const()[name = tensor("concat_52"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9977728)))]; tensor var_668_batch_first_direction_0 = const()[name = tensor("op_668_batch_first_direction_0"), val = tensor("bidirectional")]; tensor var_668_batch_first_output_sequence_0 = const()[name = tensor("op_668_batch_first_output_sequence_0"), val = tensor(true)]; tensor var_668_batch_first_recurrent_activation_0 = const()[name = tensor("op_668_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; tensor var_668_batch_first_cell_activation_0 = const()[name = tensor("op_668_batch_first_cell_activation_0"), val = tensor("tanh")]; tensor var_668_batch_first_activation_0 = const()[name = tensor("op_668_batch_first_activation_0"), val = tensor("tanh")]; tensor transpose_10 = transpose(perm = transpose_10_perm_0, x = x_27)[name = tensor("transpose_53")]; tensor var_668_batch_first_0, tensor var_668_batch_first_1, tensor 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("op_668_batch_first")]; tensor transpose_15_perm_0 = const()[name = tensor("transpose_15_perm_0"), val = tensor([1, 0, 2])]; tensor var_678 = const()[name = tensor("op_678"), val = tensor(0x1.4f8b58p-17)]; tensor beta_11 = const()[name = tensor("beta_11"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10125248)))]; tensor x_35_axes_0 = const()[name = tensor("x_35_axes_0"), val = tensor([-1])]; tensor transpose_15 = transpose(perm = transpose_15_perm_0, x = var_668_batch_first_0)[name = tensor("transpose_52")]; tensor x_35 = layer_norm(axes = x_35_axes_0, epsilon = var_678, x = transpose_15)[name = tensor("x_35")]; tensor var_702 = const()[name = tensor("op_702"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10126080)))]; tensor var_703 = mul(x = var_702, y = x_35)[name = tensor("op_703")]; tensor x_37 = add(x = var_703, y = beta_11)[name = tensor("x_37")]; tensor var_709_perm_0 = const()[name = tensor("op_709_perm_0"), val = tensor([0, -1, -2])]; tensor var_711 = const()[name = tensor("op_711"), val = tensor(1)]; tensor x_39_interleave_0 = const()[name = tensor("x_39_interleave_0"), val = tensor(false)]; tensor var_709 = transpose(perm = var_709_perm_0, x = x_37)[name = tensor("transpose_51")]; tensor d = concat(axis = var_711, interleave = x_39_interleave_0, values = (var_709, sx))[name = tensor("x_39")]; tensor transpose_12_perm_0 = const()[name = tensor("transpose_12_perm_0"), val = tensor([-1, 0, -2])]; tensor add_6 = const()[name = tensor("add_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10126912)))]; tensor add_7 = const()[name = tensor("add_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10128512)))]; tensor concat_59 = const()[name = tensor("concat_59"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10130112)))]; tensor concat_60 = const()[name = tensor("concat_60"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10474240)))]; tensor concat_61 = const()[name = tensor("concat_61"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10621760)))]; tensor concat_62 = const()[name = tensor("concat_62"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10965888)))]; tensor input_109_batch_first_direction_0 = const()[name = tensor("input_109_batch_first_direction_0"), val = tensor("bidirectional")]; tensor input_109_batch_first_output_sequence_0 = const()[name = tensor("input_109_batch_first_output_sequence_0"), val = tensor(true)]; tensor input_109_batch_first_recurrent_activation_0 = const()[name = tensor("input_109_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; tensor input_109_batch_first_cell_activation_0 = const()[name = tensor("input_109_batch_first_cell_activation_0"), val = tensor("tanh")]; tensor input_109_batch_first_activation_0 = const()[name = tensor("input_109_batch_first_activation_0"), val = tensor("tanh")]; tensor transpose_12 = transpose(perm = transpose_12_perm_0, x = d)[name = tensor("transpose_50")]; tensor input_109_batch_first_0, tensor input_109_batch_first_1, tensor 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("input_109_batch_first")]; tensor input_109_perm_0 = const()[name = tensor("input_109_perm_0"), val = tensor([1, 0, 2])]; tensor input_109 = transpose(perm = input_109_perm_0, x = input_109_batch_first_0)[name = tensor("transpose_49")]; tensor var_747 = linear(bias = ts_predictor_duration_proj_linear_layer_bias, weight = ts_predictor_duration_proj_linear_layer_weight, x = input_109)[name = tensor("linear_41")]; tensor var_748 = sigmoid(x = var_747)[name = tensor("op_748")]; tensor var_753_axes_0 = const()[name = tensor("op_753_axes_0"), val = tensor([-1])]; tensor var_753_keep_dims_0 = const()[name = tensor("op_753_keep_dims_0"), val = tensor(false)]; tensor duration = reduce_sum(axes = var_753_axes_0, keep_dims = var_753_keep_dims_0, x = var_748)[name = tensor("op_753")]; tensor var_757_batch_dims_0 = const()[name = tensor("op_757_batch_dims_0"), val = tensor(0)]; tensor var_757_validate_indices_0 = const()[name = tensor("op_757_validate_indices_0"), val = tensor(false)]; tensor var_757_axis_1 = const()[name = tensor("op_757_axis_1"), val = tensor(0)]; tensor 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("op_757")]; tensor input_111_perm_0 = const()[name = tensor("input_111_perm_0"), val = tensor([0, 2, 1])]; tensor var_761 = const()[name = tensor("op_761"), val = tensor(0x1.99999ap-3)]; tensor var_764 = const()[name = tensor("op_764"), val = tensor(0x1.4f8b58p-17)]; tensor x_41_pad_type_0 = const()[name = tensor("x_41_pad_type_0"), val = tensor("custom")]; tensor x_41_pad_0 = const()[name = tensor("x_41_pad_0"), val = tensor([2, 2])]; tensor x_41_strides_0 = const()[name = tensor("x_41_strides_0"), val = tensor([1])]; tensor x_41_dilations_0 = const()[name = tensor("x_41_dilations_0"), val = tensor([1])]; tensor x_41_groups_0 = const()[name = tensor("x_41_groups_0"), val = tensor(1)]; tensor input_111 = transpose(perm = input_111_perm_0, x = var_757)[name = tensor("transpose_48")]; tensor 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("x_41")]; tensor input_113_perm_0 = const()[name = tensor("input_113_perm_0"), val = tensor([0, -1, 1])]; tensor x_43_axes_0 = const()[name = tensor("x_43_axes_0"), val = tensor([-1])]; tensor input_113 = transpose(perm = input_113_perm_0, x = x_41)[name = tensor("transpose_47")]; tensor 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("x_43")]; tensor input_115_perm_0 = const()[name = tensor("input_115_perm_0"), val = tensor([0, -1, 1])]; tensor input_115 = transpose(perm = input_115_perm_0, x = x_43)[name = tensor("transpose_46")]; tensor input_117 = leaky_relu(alpha = var_761, x = input_115)[name = tensor("input_117")]; tensor var_787 = const()[name = tensor("op_787"), val = tensor(0x1.99999ap-3)]; tensor var_790 = const()[name = tensor("op_790"), val = tensor(0x1.4f8b58p-17)]; tensor x_45_pad_type_0 = const()[name = tensor("x_45_pad_type_0"), val = tensor("custom")]; tensor x_45_pad_0 = const()[name = tensor("x_45_pad_0"), val = tensor([2, 2])]; tensor x_45_strides_0 = const()[name = tensor("x_45_strides_0"), val = tensor([1])]; tensor x_45_dilations_0 = const()[name = tensor("x_45_dilations_0"), val = tensor([1])]; tensor x_45_groups_0 = const()[name = tensor("x_45_groups_0"), val = tensor(1)]; tensor 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("x_45")]; tensor input_121_perm_0 = const()[name = tensor("input_121_perm_0"), val = tensor([0, -1, 1])]; tensor x_47_axes_0 = const()[name = tensor("x_47_axes_0"), val = tensor([-1])]; tensor input_121 = transpose(perm = input_121_perm_0, x = x_45)[name = tensor("transpose_45")]; tensor 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("x_47")]; tensor input_123_perm_0 = const()[name = tensor("input_123_perm_0"), val = tensor([0, -1, 1])]; tensor input_123 = transpose(perm = input_123_perm_0, x = x_47)[name = tensor("transpose_44")]; tensor input_125 = leaky_relu(alpha = var_787, x = input_123)[name = tensor("input_125")]; tensor var_813 = const()[name = tensor("op_813"), val = tensor(0x1.99999ap-3)]; tensor var_816 = const()[name = tensor("op_816"), val = tensor(0x1.4f8b58p-17)]; tensor x_49_pad_type_0 = const()[name = tensor("x_49_pad_type_0"), val = tensor("custom")]; tensor x_49_pad_0 = const()[name = tensor("x_49_pad_0"), val = tensor([2, 2])]; tensor x_49_strides_0 = const()[name = tensor("x_49_strides_0"), val = tensor([1])]; tensor x_49_dilations_0 = const()[name = tensor("x_49_dilations_0"), val = tensor([1])]; tensor x_49_groups_0 = const()[name = tensor("x_49_groups_0"), val = tensor(1)]; tensor 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("x_49")]; tensor input_129_perm_0 = const()[name = tensor("input_129_perm_0"), val = tensor([0, -1, 1])]; tensor x_axes_0 = const()[name = tensor("x_axes_0"), val = tensor([-1])]; tensor input_129 = transpose(perm = input_129_perm_0, x = x_49)[name = tensor("transpose_43")]; tensor 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("x")]; tensor input_133 = leaky_relu(alpha = var_813, x = x)[name = tensor("input_133")]; tensor input_135_batch_first_transpose_perm_0 = const()[name = tensor("input_135_batch_first_transpose_perm_0"), val = tensor([1, 0, 2])]; tensor add_8 = const()[name = tensor("add_8"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11113408)))]; tensor add_9 = const()[name = tensor("add_9"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11115008)))]; tensor concat_69 = const()[name = tensor("concat_69"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11116608)))]; tensor concat_70 = const()[name = tensor("concat_70"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11411584)))]; tensor concat_71 = const()[name = tensor("concat_71"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11559104)))]; tensor concat_72 = const()[name = tensor("concat_72"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11854080)))]; tensor input_137_batch_first_direction_0 = const()[name = tensor("input_137_batch_first_direction_0"), val = tensor("bidirectional")]; tensor input_137_batch_first_output_sequence_0 = const()[name = tensor("input_137_batch_first_output_sequence_0"), val = tensor(true)]; tensor input_137_batch_first_recurrent_activation_0 = const()[name = tensor("input_137_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; tensor input_137_batch_first_cell_activation_0 = const()[name = tensor("input_137_batch_first_cell_activation_0"), val = tensor("tanh")]; tensor input_137_batch_first_activation_0 = const()[name = tensor("input_137_batch_first_activation_0"), val = tensor("tanh")]; tensor input_135_batch_first_transpose = transpose(perm = input_135_batch_first_transpose_perm_0, x = input_133)[name = tensor("transpose_42")]; tensor input_137_batch_first_0, tensor input_137_batch_first_1, tensor 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("input_137_batch_first")]; tensor input_137_perm_0 = const()[name = tensor("input_137_perm_0"), val = tensor([1, 0, 2])]; tensor input_137 = transpose(perm = input_137_perm_0, x = input_137_batch_first_0)[name = tensor("transpose_41")]; tensor input_139 = linear(bias = ts_asr_proj_0_bias, weight = ts_asr_proj_0_weight, x = input_137)[name = tensor("linear_42")]; tensor input_mode_0 = const()[name = tensor("input_mode_0"), val = tensor("EXACT")]; tensor input = gelu(mode = input_mode_0, x = input_139)[name = tensor("input")]; tensor var_879 = linear(bias = ts_asr_proj_2_bias, weight = ts_asr_proj_2_weight, x = input)[name = tensor("linear_43")]; tensor var_882_perm_0 = const()[name = tensor("op_882_perm_0"), val = tensor([0, -1, -2])]; tensor asr_tok = transpose(perm = var_882_perm_0, x = var_879)[name = tensor("transpose_40")]; } -> (duration, d, asr_tok); }